Repository: DanielTakeshi/rl_algorithms Branch: master Commit: 7a43fa485137 Files: 89 Total size: 2.9 MB Directory structure: gitextract_kv5056s4/ ├── .gitignore ├── LICENSE ├── README.md ├── bc/ │ ├── README.md │ ├── bash_scripts/ │ │ ├── demo.bash │ │ ├── gen_exp_data.sh │ │ └── runbc_allmujoco.sh │ ├── bc.py │ ├── experts/ │ │ ├── Ant-v1.pkl │ │ ├── HalfCheetah-v1.pkl │ │ ├── Hopper-v1.pkl │ │ ├── Humanoid-v1.pkl │ │ ├── Reacher-v1.pkl │ │ └── Walker2d-v1.pkl │ ├── load_policy.py │ ├── plot_bc.py │ ├── random_logs/ │ │ └── gen_exp_data.text │ ├── run_expert.py │ └── tf_util.py ├── ddpg/ │ ├── README.md │ ├── ddpg.py │ ├── main.py │ └── replay_buffer.py ├── dqn/ │ ├── README.md │ ├── atari_wrappers.py │ ├── dqn.py │ ├── dqn_utils.py │ ├── logs_pkls/ │ │ ├── BeamRider_s001.pkl │ │ ├── BeamRider_s002.pkl │ │ ├── Breakout_s001.pkl │ │ ├── Breakout_s002.pkl │ │ ├── Enduro_s001.pkl │ │ ├── Enduro_s002.pkl │ │ ├── Pong_s001.pkl │ │ └── Pong_s002.pkl │ ├── logs_text/ │ │ ├── BeamRider_s001.text │ │ ├── BeamRider_s002.text │ │ ├── Breakout_s001.text │ │ ├── Breakout_s002.text │ │ ├── Enduro_s001.text │ │ ├── Enduro_s002.text │ │ ├── Pong_s001.text │ │ └── Pong_s002.text │ ├── plot_dqn.py │ ├── run_dqn_atari.py │ └── run_dqn_ram.py ├── es/ │ ├── README.md │ ├── bash_scripts/ │ │ └── InvertedPendulum-v1.sh │ ├── es.py │ ├── logz.py │ ├── main.py │ ├── optimizers.py │ ├── plot.py │ ├── test.py │ ├── toy_es.py │ └── utils.py ├── g_learning/ │ ├── G-Learning.py │ ├── README.md │ └── __init__.py ├── lib/ │ ├── __init__.py │ ├── envs/ │ │ ├── README.md │ │ ├── __init__.py │ │ ├── blackjack.py │ │ ├── cliff_walking.py │ │ ├── gridworld.py │ │ ├── two_room_domain.py │ │ └── windy_gridworld.py │ └── plotting.py ├── q_learning/ │ ├── Q-Learning.py │ ├── README.md │ └── __init__.py ├── trpo/ │ ├── README.md │ ├── fxn_approx.py │ ├── main.py │ ├── trpo.py │ └── utils_trpo.py ├── utils/ │ ├── __init__.py │ ├── logz.py │ ├── policies.py │ ├── utils_pg.py │ └── value_functions.py └── vpg/ ├── README.md ├── bash_scripts/ │ ├── CartPole-v0.sh │ ├── Pendulum-v0.sh │ ├── halfcheetah.sh │ ├── hopper.sh │ └── walker.sh ├── main.py └── plot_learning_curves.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ __pycache__ *.pyc *.swp *.swo .DS_Store vpg/outputs/*/*/a.diff bc/data/* bc/expert_data/* ================================================ FILE: LICENSE ================================================ MIT License Copyright (c) 2017 Daniel Seita Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: README.md ================================================ Note: this repository has not been updated in years and I don't have plans to do so. I recommend using rlpyt for future RL research. # Reinforcement Learning Algorithms I will use this repository to implement various reinforcement learning algorithms (also imitation learning), because I'm sick of reading about them but not really understanding them. Hence, hopefully this repository will help me understand them better. I will also implement various supporting code as needed, such as for simple custom scenarios like GridWorld. Or I can use OpenAI gym. Click on the links to get to the appropriate algorithms. Each sub-directory will have its own READMEs with results there, along with usage instructions. Here are the algorithms currently implemented or in progress: - [Q-Learning, tabular version](https://github.com/DanielTakeshi/rl_algorithms/tree/master/q_learning) (should be correct) - [G-Learning](https://github.com/DanielTakeshi/rl_algorithms/tree/master/g_learning) (in progress ...) - [Behavioral Cloning](https://github.com/DanielTakeshi/rl_algorithms/tree/master/bc) (should be correct) - [Natural Evolution Strategies](https://github.com/DanielTakeshi/rl_algorithms/tree/master/es) (should be correct) - [Deep-Q Networks](https://github.com/DanielTakeshi/rl_algorithms/tree/master/dqn) (should be correct) - [Vanilla Policy Gradients](https://github.com/DanielTakeshi/rl_algorithms/tree/master/vpg) (should be correct) - [Deep Deterministic Policy Gradients](https://github.com/DanielTakeshi/rl_algorithms/tree/master/ddpg) (in progress ...) - [Trust Region Policy Optimization](https://github.com/DanielTakeshi/rl_algorithms/tree/master/trpo) (in progress ...) Note: "Vanilla Policy Gradients" refers to the REINFORCE algorithm, also known as Monte Carlo Policy Gradient. Sometimes it's called an actor-critic method and other times it's not. Even if it's considered an actor-critic method, the usual way we think of actor-critic involves a TD update rather than waiting until the end of an episode to get returns. # Requirements Right now the code is designed for Python 2.7, but it *should* be compatible with Python 3.5+, with the possible exception of if the bash scripts can't tell the difference between which Python versions I'm using. In short: - Python 2.7.x - Tensorflow 1.2.0 # GPU and TensorFlow (Update 06/16/17, these are out of date ... just install with pip and preferably virtualenv. It's so much easier.) I installed TensorFlow 1.0.1 from source. For the configuration script, I used CUDA 8.0, cuDNN 5.1.5, and compute capability 6.1. Compiling from source means I can get faster CPU instructions. This requires `bazel` plus extra compiler options. I used: ``` bazel build -c opt --copt=-mavx --copt=-mavx2 --copt=-mfma --copt=-mfpmath=both --copt=-msse4.2 --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package ``` This resulted in ton of warning messages but I ended up with: ``` Target //tensorflow/tools/pip_package:build_pip_package up-to-date: bazel-bin/tensorflow/tools/pip_package/build_pip_package INFO: Elapsed time: 884.276s, Critical Path: 672.19s ``` and things seem to be working. Then run the command: ``` bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg ``` To get a wheel, which we then do a pip install. But be careful due to pip on anaconda vs pip with default python. I use anaconda. And make sure you're not in either the `tensorflow` or the `bazel` directories! Track the GPU usage with `nvidia-smi`. Unfortunately, that's only for one time-step, but we can instead run: ``` while true; do nvidia-smi --query-gpu=utilization.gpu --format=csv >> gpu_utilization.log; sleep 10; done; ``` Or something like that. It will record the output every 10 seconds and dump it into the log file. Ideally, GPU usage should be as high as possible (100% or close to it). # References I have read a number of reinforcement learning paper references to help me out. A list of papers and summaries (for a few of them) are [in my paper notes repository](https://github.com/DanielTakeshi/Paper_Notes). ================================================ FILE: bc/README.md ================================================ # Behavioral Cloning ## Main Idea This runs Behavioral Cloning (BC) on MuJoCo environments, with settings inspired by the NIPS 2016 [GAIL paper][1]. Specifically: - The expert is TRPO and provided from Jonathan Ho (see below). - Dataset consists of inputs (states=s) to labels (actions=a). They're continuous, so minimize mean L2 loss across minibatches. It is split into 70% training, 30% validation. - Expert data size is measured in terms of the number of expert rollouts we collected. Note that, like the GAIL paper, I *subsample*, so the actual amount of (s,a) pairs available for BC is much smaller. - The neural network (from TensorFlow) is fully connected and has two hidden layers of 100 units each with hyperbolic tangent non-linearities. It's trained with Adam, with a step size of 1e-3, and has a batch size of 128. - For now, I plot validation set performance (i.e. loss) without really using it. If I needed to be formal and had to pick a given iteration for which to choose my BC expert (because different iterations mean different weights) I'd choose the one with best validation set performance. I also plot training set performance just for kicks. ## Running the Code To run BC, there are several steps: - (If needed) generate expert data. Run ``` ./bash_scripts/gen_exp_data.sh ``` which will run and save expert trajectories in numpy arrays. They're not saved in the repository (ask if you want my version). By default, the number of trajectories is saved into the file name by default and matches the values in the GAIL paper (see Table 1). No subsampling is done at this stage. - See the bash scripts for examples of running BC. For these, I used one script to run everything. - To plot the code, it's simple: `python plot_bc.py`. No command line arguments! If you're interested: - The expert performance that I'm seeing is roughly similar to what's reported in the GAIL paper, with the exception of the Walker environment, but I may be running a newer version from Jonathan Ho. See the output in `logs/gen_exp_data.text` for details. - The `bash_scripts` directory also contains a file called `demo.bash`, which you can use to visualize expert trajectories, just for fun. # Results Many subplots have three curves, each with one standard deviation error regions. The reason is that for a given BC run, each "evaluation point" (e.g. every 50 training minibatches) I will run some "test-time" rollouts to see performance, but I also wanted to test with different initializations, hence the different random seeds. Ant-v1, HalfCheetah-v1, Hopper-v1, and Walker2d-v1 use 4, 11, 18, and 25 expert rollouts since that follows the GAIL paper. Humanoid-v1 uses 80, 160, and 240 expert trajectories. Observations: - BC does well in Ant-v1 and HalfCheetah-v1. - Hopper-v1 seems to be difficult, surprisingly. It has a relatively small state space compared to Ant (11 vs 111). - Walker2d-v1 seems to be in between, BC doesn't get going until 25 rollouts. - How am I doing so poorly on Humanoid-v1? ## Ant-v1 ![ant](figures/Ant-v1.png?raw=true) ## HalfCheetah-v1 ![halfcheetah](figures/HalfCheetah-v1.png?raw=true) ## Hopper-v1 ![hopper](figures/Hopper-v1.png?raw=true) ## Walker2d-v1 ![walker2d](figures/Walker2d-v1.png?raw=true) ## Reacher-v1 ![reacher](figures/Reacher-v1.png?raw=true) ## Humanoid-v1 ![humanoid](figures/Humanoid-v1.png?raw=true) # Original Notes from Berkeley This started from UC Berkeley's Deep Reinforcement Learning class. Here's their information: > Dependencies: TensorFlow, MuJoCo version 1.31, OpenAI Gym > > The only file that you need to look at is `run_expert.py`, which is code to > load up an expert policy, run a specified number of roll-outs, and save out > data. > > In `experts/`, the provided expert policies are: > * Ant-v1.pkl > * HalfCheetah-v1.pkl > * Hopper-v1.pkl > * Humanoid-v1.pkl > * Reacher-v1.pkl > * Walker2d-v1.pkl > > The name of the pickle file corresponds to the name of the gym environment. [1]:https://arxiv.org/abs/1606.03476 ================================================ FILE: bc/bash_scripts/demo.bash ================================================ #!/bin/bash set -eux for e in Hopper-v1 Ant-v1 HalfCheetah-v1 Humanoid-v1 Reacher-v1 Walker2d-v1 do python run_expert.py experts/$e.pkl $e --render --num_rollouts=1 done ================================================ FILE: bc/bash_scripts/gen_exp_data.sh ================================================ python run_expert.py experts/Reacher-v1.pkl Reacher-v1 --save --num_rollouts 4 python run_expert.py experts/Reacher-v1.pkl Reacher-v1 --save --num_rollouts 11 python run_expert.py experts/Reacher-v1.pkl Reacher-v1 --save --num_rollouts 18 python run_expert.py experts/HalfCheetah-v1.pkl HalfCheetah-v1 --save --num_rollouts 4 python run_expert.py experts/HalfCheetah-v1.pkl HalfCheetah-v1 --save --num_rollouts 11 python run_expert.py experts/HalfCheetah-v1.pkl HalfCheetah-v1 --save --num_rollouts 18 python run_expert.py experts/HalfCheetah-v1.pkl HalfCheetah-v1 --save --num_rollouts 25 python run_expert.py experts/Hopper-v1.pkl Hopper-v1 --save --num_rollouts 4 python run_expert.py experts/Hopper-v1.pkl Hopper-v1 --save --num_rollouts 11 python run_expert.py experts/Hopper-v1.pkl Hopper-v1 --save --num_rollouts 18 python run_expert.py experts/Hopper-v1.pkl Hopper-v1 --save --num_rollouts 25 python run_expert.py experts/Walker2d-v1.pkl Walker2d-v1 --save --num_rollouts 4 python run_expert.py experts/Walker2d-v1.pkl Walker2d-v1 --save --num_rollouts 11 python run_expert.py experts/Walker2d-v1.pkl Walker2d-v1 --save --num_rollouts 18 python run_expert.py experts/Walker2d-v1.pkl Walker2d-v1 --save --num_rollouts 25 python run_expert.py experts/Ant-v1.pkl Ant-v1 --save --num_rollouts 4 python run_expert.py experts/Ant-v1.pkl Ant-v1 --save --num_rollouts 11 python run_expert.py experts/Ant-v1.pkl Ant-v1 --save --num_rollouts 18 python run_expert.py experts/Ant-v1.pkl Ant-v1 --save --num_rollouts 25 python run_expert.py experts/Humanoid-v1.pkl Humanoid-v1 --save --num_rollouts 80 python run_expert.py experts/Humanoid-v1.pkl Humanoid-v1 --save --num_rollouts 160 python run_expert.py experts/Humanoid-v1.pkl Humanoid-v1 --save --num_rollouts 240 ================================================ FILE: bc/bash_scripts/runbc_allmujoco.sh ================================================ #!/bin/bash set -eux for e in 4 11 18 25; do for s in 0 1 2; do python bc.py Ant-v1 $e --test_rollouts 50 --eval_freq 100 --train_iters 20001 --seed $s --subsamp_freq 20 python bc.py HalfCheetah-v1 $e --test_rollouts 50 --eval_freq 100 --train_iters 20001 --seed $s --subsamp_freq 20 python bc.py Hopper-v1 $e --test_rollouts 50 --eval_freq 100 --train_iters 20001 --seed $s --subsamp_freq 20 python bc.py Walker2d-v1 $e --test_rollouts 50 --eval_freq 100 --train_iters 20001 --seed $s --subsamp_freq 20 done done for e in 4 11 18; do for s in 0 1 2; do python bc.py Reacher-v1 $e --test_rollouts 50 --eval_freq 100 --train_iters 20001 --seed $s --subsamp_freq 1 done done for e in 80 160 240; do for s in 0 1 2; do python bc.py Humanoid-v1 $e --test_rollouts 50 --eval_freq 100 --train_iters 20001 --seed $s --subsamp_freq 20 done done ================================================ FILE: bc/bc.py ================================================ """ (c) June 2017 by Daniel Seita Behavioral cloning (continuous actions only). For results, see the README(s) nearby. TODO right now we assume we'll get our minibatches of data with `get_batch` but this is inefficient if we decide to scale up and avoid subsampling the data, where it would be better to have a list which supplies fixed, pre-computed minibatches. I should fix this later. TODO handle l2 regualrization? Though I have found that this doesn't have as good an effect as I thought it would ... TODO maybe save the weights with best validation set performance, along with weights every 500 iterations or so? Then we can visualize it. """ import argparse import gym import matplotlib.pyplot as plt import numpy as np import os import pickle import sys import tensorflow as tf import tensorflow.contrib.layers as layers import tf_util plt.style.use('seaborn-darkgrid') np.set_printoptions(edgeitems=100, linewidth=100, suppress=True) def get_tf_session(): """ Returning a session. Set options here if desired. """ tf.reset_default_graph() tf_config = tf.ConfigProto(inter_op_parallelism_threads=1, intra_op_parallelism_threads=1) gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5) session = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) def get_available_gpus(): from tensorflow.python.client import device_lib local_device_protos = device_lib.list_local_devices() return [x.physical_device_desc for x in local_device_protos if x.device_type == 'GPU'] print("AVAILABLE GPUS: ", get_available_gpus()) return session def load_dataset(args): """ Loads the dataset for BC and return training and validation splits, separating the observations and actions, along with observation and action shapes. This is also where we should handle the case of varying-length expert trajectories, even though these should be rare. (I kept those there so that the leading dimension is the number of trajectories, in case we want to use that information somehow, but right now we just mix among trajectories.) In addition, it might be useful to subsample the data. """ # Load the numpy file and parse it. str_roll = str(args.num_rollouts).zfill(3) expert_data = np.load('expert_data/'+args.envname+'_'+str_roll+'.npy') expert_obs = expert_data[()]['observations'] expert_act = expert_data[()]['actions'] expert_ret = expert_data[()]['returns'] expert_stp = expert_data[()]['steps'] N = expert_obs.shape[0] assert N == expert_act.shape[0] == len(expert_ret) == len(expert_stp) obs_shape = expert_obs.shape[2] act_shape = expert_act.shape[2] print("\nobs_shape = {}\nact_shape = {}".format(obs_shape, act_shape)) print("subsampling freq = {}".format(args.subsamp_freq)) print("expert_steps = {}".format(expert_stp)) print("expert_returns = {}".format(expert_ret)) print("mean(expert_returns) = {}".format(np.mean(expert_ret))) # remember! print("(raw) expert_obs.shape = {}".format(expert_obs.shape)) print("(raw) expert_act.shape = {}".format(expert_act.shape)) # Choose a different starting point to subsample for each trajectory. start_indices = np.random.randint(0, args.subsamp_freq, N) # Subsample expert data, remove actions which were only for padding. expert_obs_l = [] expert_act_l = [] for i in range(N): expert_obs_l.append( expert_obs[i, start_indices[i]:expert_stp[i]:args.subsamp_freq, :] ) expert_act_l.append( expert_act[i, start_indices[i]:expert_stp[i]:args.subsamp_freq, :] ) # Concatenate everything together. expert_obs = np.concatenate(expert_obs_l, axis=0) expert_act = np.concatenate(expert_act_l, axis=0) print("(subsampled/reshaped) expert_obs.shape = {}".format(expert_obs.shape)) print("(subsampled/reshaped) expert_act.shape = {}".format(expert_act.shape)) assert expert_obs.shape[0] == expert_act.shape[0] # Finally, form training and validation splits. num_examples = expert_obs.shape[0] num_train = int(args.train_frac * num_examples) shuffled_inds = np.random.permutation(num_examples) train_inds, valid_inds = shuffled_inds[:num_train], shuffled_inds[num_train:] expert_obs_tr = expert_obs[train_inds] expert_act_tr = expert_act[train_inds] expert_obs_val = expert_obs[valid_inds] expert_act_val = expert_act[valid_inds] print("\n(train) expert_obs.shape = {}".format(expert_obs_tr.shape)) print("(train) expert_act.shape = {}".format(expert_act_tr.shape)) print("(valid) expert_obs.shape = {}".format(expert_obs_val.shape)) print("(valid) expert_act.shape = {}\n".format(expert_act_val.shape)) return (expert_obs_tr, expert_act_tr, expert_obs_val, expert_act_val, \ obs_shape, act_shape) def policy_model(data_in, action_dim): """ Create a neural network representing the BC policy. It will be trained using standard supervised learning techniques. Parameters ---------- data_in: [Tensor] The input (a placeholder) to the network, with leading dimension representing the batch size. action_dim: [int] Number of actions, each of which (at least for MuJoCo) is continuous-valued. Returns ------- out [Tensor] The output tensor which represents the predicted (or desired, if testing) action to take for the agent. """ with tf.variable_scope("BCNetwork", reuse=False): out = data_in out = layers.fully_connected(out, num_outputs=100, weights_initializer=layers.xavier_initializer(uniform=True), activation_fn=tf.nn.tanh) out = layers.fully_connected(out, num_outputs=100, weights_initializer=layers.xavier_initializer(uniform=True), activation_fn=tf.nn.tanh) out = layers.fully_connected(out, num_outputs=action_dim, weights_initializer=layers.xavier_initializer(uniform=True), activation_fn=None) return out def get_batch(expert_obs, expert_act, batch_size): """ Obtain a minibatch of samples. Note that this is relatively inefficient, and if dealing with very large datasets without subsampling, use a list of samples instead. """ indices = np.arange(expert_obs.shape[0]) np.random.shuffle(indices) xs = expert_obs[indices[:batch_size]] ys = expert_act[indices[:batch_size]] return xs, ys def run_bc(session, args, log_dir): """ Runs behavioral cloning on some stored data. It roughly mirrors the experimental setup of [Ho & Ermon, NIPS 2016]. They trained using ADAM (batch size 128) on 70% of the data and trained until validation error on the held-out set of 30% no longer decreases. They also substantially subsampled their data. Parameters ---------- session: [TF Session] The TensorFlow session we're using. args: [Arguments Namespace] Namedspace representing convenient arguments from the user. log_dir: [string] Where we save files to. FYI, it doesn't include the ending slash. """ env = gym.make(args.envname) (expert_obs_tr, expert_act_tr, expert_obs_val, expert_act_val, obs_shape, \ act_shape) = load_dataset(args) # Build the data and network. For now, no casting (see DQN code). x = tf.placeholder(tf.float32, shape=[None,obs_shape]) y = tf.placeholder(tf.float32, shape=[None,act_shape]) policy_fn = policy_model(data_in=x, action_dim=act_shape) # Save weights as a single vector to make saving/loading easy. weights_bc = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='BCNetwork') weight_vector = tf.concat([tf.reshape(w, [-1]) for w in weights_bc], axis=0) # Construct the loss function and training information. l2_loss = tf.reduce_mean( tf.reduce_sum((policy_fn-y)*(policy_fn-y), axis=[1]) ) train_step = tf.train.AdamOptimizer(args.lrate).minimize(l2_loss) all_tr_loss = [] all_val_loss = [] all_iters = [] # Makes plotting easier since these are the x-coords. all_returns = [] # Will turn into an array of arrays later. session.run(tf.global_variables_initializer()) for i in range(args.train_iters): b_xs, b_ys = get_batch(expert_obs_tr, expert_act_tr, args.batch_size) _,tr_loss = session.run([train_step, l2_loss], feed_dict={x:b_xs, y:b_ys}) if (i % args.eval_freq == 0): # Only save/evaluate stuff every `args.eval_freq` iterations. val_loss = session.run(l2_loss, feed_dict={x:expert_obs_val, y:expert_act_val}) returns = run_bc_test(args, session, policy_fn, x, env) print("iter={} tr_loss={:.5f} val_loss={:.5f}".format( str(i).zfill(4), tr_loss, val_loss)) print("mean(returns): {}\nstd(returns): {}\n".format( np.mean(returns), np.std(returns))) all_iters.append(i) all_tr_loss.append(tr_loss) all_val_loss.append(val_loss) all_returns.append(returns) # Save snapshot of the current weights. We can pick out the best one # by seeing the minimizing index in `all_val_loss`. itr = str(i).zfill(len(str(abs(args.train_iters)))) weights_numpy = session.run(weight_vector) np.save(log_dir+'/snapshots/weights_'+itr, weights_numpy) # Store the results as numpy arrays so we can easily plot later. np.save(log_dir +"/iters", np.array(all_iters)) np.save(log_dir +"/tr_loss", np.array(all_tr_loss)) np.save(log_dir +"/val_loss", np.array(all_val_loss)) np.save(log_dir +"/returns", np.array(all_returns)) def run_bc_test(args, session, policy_fn, x, env): """ Run the agent in the world! Returns ------- returns [list] A list of returns, one for each of the `args.test_rollouts` rollouts. """ actions = [] observations = [] returns = [] max_steps = env.spec.timestep_limit for rr in range(args.test_rollouts): obs = env.reset() done = False totalr = 0 steps = 0 while not done: # Take steps by expanding observation (to get shapes to match). exp_obs = np.expand_dims(obs, axis=0) action = np.squeeze(session.run(policy_fn, feed_dict={x:exp_obs})) obs, r, done, _ = env.step(action) totalr += r steps += 1 if args.render: env.render() if steps >= max_steps: break returns.append(totalr) return returns if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument('envname', type=str) parser.add_argument('num_rollouts', type=str) parser.add_argument('--batch_size', type=int, default=128) parser.add_argument('--eval_freq', type=int, default=100) parser.add_argument('--lrate', type=float, default=0.001) parser.add_argument('--regu', type=float, default=0.0) # don't use now parser.add_argument('--seed', type=int, default=0) parser.add_argument('--subsamp_freq', type=int, default=20) parser.add_argument('--test_rollouts', type=int, default=50) # GAIL paper used 50 parser.add_argument('--train_frac', type=float, default=0.7) parser.add_argument('--train_iters', type=int, default=5001) # GAIL paper used 20001 parser.add_argument('--render', action='store_true') # don't use now args = parser.parse_args() print("\nUsing the following arguments: {}".format(args)) # Handle some logic with the log file and save the args there. log_dir = "logs/"+args.envname+"/numroll_"+args.num_rollouts+"_seed_"+str(args.seed) print("log_dir: {}\n".format(log_dir)) assert not os.path.exists(log_dir), "Error: log_dir already exists!" os.makedirs(log_dir) os.makedirs(log_dir+'/snapshots/') with open(log_dir+'/args.pkl','w') as f: pickle.dump(args, f) # Create a session, handle random seeds (well, partly...) and run. session = get_tf_session() np.random.seed(args.seed) tf.set_random_seed(args.seed) run_bc(session, args, log_dir) ================================================ FILE: bc/load_policy.py ================================================ import pickle, tensorflow as tf, tf_util, numpy as np def load_policy(filename): with open(filename, 'rb') as f: data = pickle.loads(f.read()) # assert len(data.keys()) == 2 nonlin_type = data['nonlin_type'] policy_type = [k for k in data.keys() if k != 'nonlin_type'][0] assert policy_type == 'GaussianPolicy', 'Policy type {} not supported'.format(policy_type) policy_params = data[policy_type] assert set(policy_params.keys()) == {'logstdevs_1_Da', 'hidden', 'obsnorm', 'out'} # Keep track of input and output dims (i.e. observation and action dims) for the user def build_policy(obs_bo): def read_layer(l): assert list(l.keys()) == ['AffineLayer'] assert sorted(l['AffineLayer'].keys()) == ['W', 'b'] return l['AffineLayer']['W'].astype(np.float32), l['AffineLayer']['b'].astype(np.float32) def apply_nonlin(x): if nonlin_type == 'lrelu': return tf_util.lrelu(x, leak=.01) # openai/imitation nn.py:233 elif nonlin_type == 'tanh': return tf.tanh(x) else: raise NotImplementedError(nonlin_type) # Build the policy. First, observation normalization. assert list(policy_params['obsnorm'].keys()) == ['Standardizer'] obsnorm_mean = policy_params['obsnorm']['Standardizer']['mean_1_D'] obsnorm_meansq = policy_params['obsnorm']['Standardizer']['meansq_1_D'] obsnorm_stdev = np.sqrt(np.maximum(0, obsnorm_meansq - np.square(obsnorm_mean))) print('obs', obsnorm_mean.shape, obsnorm_stdev.shape) normedobs_bo = (obs_bo - obsnorm_mean) / (obsnorm_stdev + 1e-6) # 1e-6 constant from Standardizer class in nn.py:409 in openai/imitation curr_activations_bd = normedobs_bo # Hidden layers next assert list(policy_params['hidden'].keys()) == ['FeedforwardNet'] layer_params = policy_params['hidden']['FeedforwardNet'] for layer_name in sorted(layer_params.keys()): l = layer_params[layer_name] W, b = read_layer(l) curr_activations_bd = apply_nonlin(tf.matmul(curr_activations_bd, W) + b) # Output layer W, b = read_layer(policy_params['out']) output_bo = tf.matmul(curr_activations_bd, W) + b return output_bo obs_bo = tf.placeholder(tf.float32, [None, None]) a_ba = build_policy(obs_bo) policy_fn = tf_util.function([obs_bo], a_ba) return policy_fn ================================================ FILE: bc/plot_bc.py ================================================ """ (c) April 2017 by Daniel Seita Code for plotting behavioral cloning. No need to use command line arguments, just run `python plot_bc.py`. Easy! Right now it generates two figures per environment, one with validation set losses and the other with returns. The latter is probably more interesting. """ import argparse import gym import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import numpy as np import os import pickle import sys np.set_printoptions(edgeitems=100, linewidth=100, suppress=True) # Some matplotlib settings. plt.style.use('seaborn-darkgrid') error_region_alpha = 0.25 LOGDIR = 'logs/' FIGDIR = 'figures/' title_size = 22 tick_size = 17 legend_size = 17 ysize = 18 xsize = 18 lw = 3 ms = 8 colors = ['red', 'blue', 'yellow', 'black'] def plot_bc_modern(edir): """ Plot the results for this particular environment. """ subdirs = os.listdir(LOGDIR+edir) print("plotting subdirs {}".format(subdirs)) # Make it easy to count how many of each numrollouts we have. R_TO_COUNT = {'4':0, '11':0, '18':0, '25':0} R_TO_IJ = {'4':(0,2), '11':(1,0), '18':(1,1), '25':(1,2)} fig,axarr = plt.subplots(2, 3, figsize=(24,15)) axarr[0,2].set_title(edir+", Returns, 4 Rollouts", fontsize=title_size) axarr[1,0].set_title(edir+", Returns, 11 Rollouts", fontsize=title_size) axarr[1,1].set_title(edir+", Returns, 18 Rollouts", fontsize=title_size) axarr[1,2].set_title(edir+", Returns, 25 Rollouts", fontsize=title_size) # Don't forget to plot the expert performance! exp04 = np.mean(np.load("expert_data/"+edir+"_004.npy")[()]['returns']) exp11 = np.mean(np.load("expert_data/"+edir+"_011.npy")[()]['returns']) exp18 = np.mean(np.load("expert_data/"+edir+"_018.npy")[()]['returns']) axarr[0,2].axhline(y=exp04, color='brown', lw=lw, linestyle='--', label='expert') axarr[1,0].axhline(y=exp11, color='brown', lw=lw, linestyle='--', label='expert') axarr[1,1].axhline(y=exp18, color='brown', lw=lw, linestyle='--', label='expert') if 'Reacher' not in edir: exp25 = np.mean(np.load("expert_data/"+edir+"_025.npy")[()]['returns']) axarr[1,2].axhline(y=exp25, color='brown', lw=lw, linestyle='--', label='expert') for dd in subdirs: ddsplit = dd.split("_") # `dd` is of the form `numroll_X_seed_Y` numroll, seed = ddsplit[1], ddsplit[3] xcoord = np.load(LOGDIR+edir+"/"+dd+"/iters.npy") tr_loss = np.load(LOGDIR+edir+"/"+dd+"/tr_loss.npy") val_loss = np.load(LOGDIR+edir+"/"+dd+"/val_loss.npy") returns = np.load(LOGDIR+edir+"/"+dd+"/returns.npy") mean_ret = np.mean(returns, axis=1) std_ret = np.std(returns, axis=1) # Playing with dictionaries ijcoord = R_TO_IJ[numroll] cc = colors[ R_TO_COUNT[numroll] ] R_TO_COUNT[numroll] += 1 axarr[ijcoord].plot(xcoord, mean_ret, lw=lw, color=cc, label=dd) axarr[ijcoord].fill_between(xcoord, mean_ret-std_ret, mean_ret+std_ret, alpha=error_region_alpha, facecolor=cc) # Cram the training and validation losses on these subplots. axarr[0,0].plot(xcoord, tr_loss, lw=lw, label=dd) axarr[0,1].plot(xcoord, val_loss, lw=lw, label=dd) boring_stuff(axarr, edir) plt.tight_layout() plt.savefig(FIGDIR+edir+".png") def plot_bc_humanoid(edir): """ Plots humanoid. The argument here is kind of redundant... also, I guess we'll have to ignore one of the plots here since Humanoid will have 5 subplots. Yeah, it's a bit awkward. """ assert edir == "Humanoid-v1" subdirs = os.listdir(LOGDIR+edir) print("plotting subdirs {}".format(subdirs)) # Make it easy to count how many of each numrollouts we have. R_TO_COUNT = {'80':0, '160':0, '240':0} R_TO_IJ = {'80':(1,0), '160':(1,1), '240':(1,2)} fig,axarr = plt.subplots(2, 3, figsize=(24,15)) axarr[0,2].set_title("Empty Plot", fontsize=title_size) axarr[1,0].set_title(edir+", Returns, 80 Rollouts", fontsize=title_size) axarr[1,1].set_title(edir+", Returns, 160 Rollouts", fontsize=title_size) axarr[1,2].set_title(edir+", Returns, 240 Rollouts", fontsize=title_size) # Plot expert performance (um, this takes a while...). exp080 = np.mean(np.load("expert_data/"+edir+"_080.npy")[()]['returns']) exp160 = np.mean(np.load("expert_data/"+edir+"_160.npy")[()]['returns']) exp240 = np.mean(np.load("expert_data/"+edir+"_240.npy")[()]['returns']) axarr[1,0].axhline(y=exp080, color='brown', lw=lw, linestyle='--', label='expert') axarr[1,1].axhline(y=exp160, color='brown', lw=lw, linestyle='--', label='expert') axarr[1,2].axhline(y=exp240, color='brown', lw=lw, linestyle='--', label='expert') for dd in subdirs: ddsplit = dd.split("_") # `dd` is of the form `numroll_X_seed_Y` numroll, seed = ddsplit[1], ddsplit[3] xcoord = np.load(LOGDIR+edir+"/"+dd+"/iters.npy") tr_loss = np.load(LOGDIR+edir+"/"+dd+"/tr_loss.npy") val_loss = np.load(LOGDIR+edir+"/"+dd+"/val_loss.npy") returns = np.load(LOGDIR+edir+"/"+dd+"/returns.npy") mean_ret = np.mean(returns, axis=1) std_ret = np.std(returns, axis=1) # Playing with dictionaries ijcoord = R_TO_IJ[numroll] cc = colors[ R_TO_COUNT[numroll] ] R_TO_COUNT[numroll] += 1 axarr[ijcoord].plot(xcoord, mean_ret, lw=lw, color=cc, label=dd) axarr[ijcoord].fill_between(xcoord, mean_ret-std_ret, mean_ret+std_ret, alpha=error_region_alpha, facecolor=cc) # Cram the training and validation losses on these subplots. axarr[0,0].plot(xcoord, tr_loss, lw=lw, label=dd) axarr[0,1].plot(xcoord, val_loss, lw=lw, label=dd) boring_stuff(axarr, edir) plt.tight_layout() plt.savefig(FIGDIR+edir+".png") def boring_stuff(axarr, edir): """ Axes, titles, legends, etc. Yeah yeah ... """ for i in range(2): for j in range(3): if i == 0 and j == 0: axarr[i,j].set_ylabel("Loss Training MBs", fontsize=ysize) if i == 0 and j == 1: axarr[i,j].set_ylabel("Loss Validation Set", fontsize=ysize) else: axarr[i,j].set_ylabel("Average Return", fontsize=ysize) axarr[i,j].set_xlabel("Training Minibatches", fontsize=xsize) axarr[i,j].tick_params(axis='x', labelsize=tick_size) axarr[i,j].tick_params(axis='y', labelsize=tick_size) axarr[i,j].legend(loc="best", prop={'size':legend_size}) axarr[i,j].legend(loc="best", prop={'size':legend_size}) axarr[0,0].set_title(edir+", Training Losses", fontsize=title_size) axarr[0,1].set_title(edir+", Validation Losses", fontsize=title_size) axarr[0,0].set_yscale('log') axarr[0,1].set_yscale('log') def plot_bc(e): """ Split into cases. It makes things easier for me. """ env_to_method = {'Ant-v1': plot_bc_modern, 'HalfCheetah-v1': plot_bc_modern, 'Hopper-v1': plot_bc_modern, 'Walker2d-v1': plot_bc_modern, 'Reacher-v1': plot_bc_modern, 'Humanoid-v1': plot_bc_humanoid} env_to_method[e](e) if __name__ == "__main__": env_dirs = [e for e in os.listdir(LOGDIR) if "text" not in e] print("Plotting with one figure per env_dirs = {}".format(env_dirs)) for e in env_dirs: plot_bc(e) ================================================ FILE: bc/random_logs/gen_exp_data.text ================================================ loading and building expert policy ('obs', (1, 11), (1, 11)) loaded and built ('roll/traj', 0) ('roll/traj', 1) ('roll/traj', 2) ('roll/traj', 3) ('steps', [50, 50, 50, 50]) ('returns', [-5.0986563080722789, -2.3470820413705882, -3.4806488084021563, -5.3923959304937243]) ('mean return', -4.0796957720846869) ('std of return', 1.2371610530104316) obs.shape = (4, 50, 11) act.shape = (4, 50, 2) expert data has been saved. loading and building expert policy ('obs', (1, 11), (1, 11)) loaded and built ('roll/traj', 0) ('roll/traj', 1) ('roll/traj', 2) ('roll/traj', 3) ('roll/traj', 4) ('roll/traj', 5) ('roll/traj', 6) ('roll/traj', 7) ('roll/traj', 8) ('roll/traj', 9) ('roll/traj', 10) ('steps', [50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50]) ('returns', [-6.0676632781974975, -4.8437408266431676, -2.7646205850154639, -4.8883500259847468, -5.5523827036728743, -3.5066640100964652, -2.8579657682413164, -3.5256961737336221, -1.9851548296485364, -5.7610544616077641, -4.4387894971172894]) ('mean return', -4.1992801963598856) ('std of return', 1.2933939982797207) obs.shape = (11, 50, 11) act.shape = (11, 50, 2) expert data has been saved. loading and building expert policy ('obs', (1, 11), (1, 11)) loaded and built ('roll/traj', 0) ('roll/traj', 1) ('roll/traj', 2) ('roll/traj', 3) ('roll/traj', 4) ('roll/traj', 5) ('roll/traj', 6) ('roll/traj', 7) ('roll/traj', 8) ('roll/traj', 9) ('roll/traj', 10) ('roll/traj', 11) ('roll/traj', 12) ('roll/traj', 13) ('roll/traj', 14) ('roll/traj', 15) ('roll/traj', 16) ('roll/traj', 17) ('steps', [50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50]) ('returns', [-7.0167051921724175, -2.2971802003578921, -3.0596530187565367, -4.6337475094076872, -4.3389105427312291, -2.0122997124407345, -6.936080880634159, -3.8789676786284204, -3.0374973076118006, -5.8012000292179202, -5.282670215008328, -1.4478122702743865, -1.6729209302580386, -2.0049694364017943, -3.4791679686749157, -4.537885362741525, -6.7110718543266392, -2.6161470377728997]) ('mean return', -3.9313826193009627) ('std of return', 1.7865687858709369) obs.shape = (18, 50, 11) act.shape = (18, 50, 2) expert data has been saved. loading and building expert policy ('obs', (1, 17), (1, 17)) loaded and built ('roll/traj', 0) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 1) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 2) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 3) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('steps', [1000, 1000, 1000, 1000]) ('returns', [4142.2707122049451, 4073.2926100930354, 4160.2641919997595, 4137.3904574971248]) ('mean return', 4128.304492948716) ('std of return', 32.8836572283574) obs.shape = (4, 1000, 17) act.shape = (4, 1000, 6) expert data has been saved. loading and building expert policy ('obs', (1, 17), (1, 17)) loaded and built ('roll/traj', 0) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 1) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 2) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 3) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 4) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 5) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 6) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 7) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 8) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 9) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 10) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('steps', [1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000]) ('returns', [4163.926640414601, 4082.5260763172073, 4088.7890907004175, 4261.2822528361958, 4258.7193259277701, 4003.5642815073606, 4014.550963767033, 4236.2386061269835, 4110.2043680589713, 3979.6921007858841, 4082.8862028719918]) ('mean return', 4116.5799917558552) ('std of return', 96.639402441900685) obs.shape = (11, 1000, 17) act.shape = (11, 1000, 6) expert data has been saved. loading and building expert policy ('obs', (1, 17), (1, 17)) loaded and built ('roll/traj', 0) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 1) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 2) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 3) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 4) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 5) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 6) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 7) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 8) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 9) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 10) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 11) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 12) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 13) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 14) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 15) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 16) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 17) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('steps', [1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000]) ('returns', [4086.4099200096416, 4131.2709022133558, 4093.6069841238532, 4224.1550004107994, 4040.8835600400148, 4205.8018352881491, 4121.3078008257926, 4059.7018278076703, 4068.9727601843761, 4158.2791893541871, 4133.9336557521283, 4248.6852156813147, 4117.3499451442194, 4105.1856269166738, 4118.7413080467604, 3999.8364477747714, 4070.2510751567406, 3998.4489992439239]) ('mean return', 4110.156780776354) ('std of return', 67.104887652297236) obs.shape = (18, 1000, 17) act.shape = (18, 1000, 6) expert data has been saved. loading and building expert policy ('obs', (1, 17), (1, 17)) loaded and built ('roll/traj', 0) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 1) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 2) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 3) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 4) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 5) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 6) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 7) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 8) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 9) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 10) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 11) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 12) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 13) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 14) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 15) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 16) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 17) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 18) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 19) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 20) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 21) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 22) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 23) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 24) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('steps', [1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000]) ('returns', [4277.0649177373989, 4134.9545756917705, 4137.2293138303303, 4303.8329029398674, 4129.3451831595994, 4141.294405302373, 4113.7214986254858, 4160.1223028192289, 4097.0754966253426, 4027.5727894938377, 4076.0161230017616, 4066.6420436003768, 4110.8062524027519, 3910.0827144736527, 4301.7383387989348, 4050.0758406333521, 4244.5068451702218, 4211.9442440870207, 4198.5772637205318, 4113.6755909929007, 4058.1256463993295, 4174.9359832628352, 4110.7459681292185, 4143.0735610903803, 4032.5424608180847]) ('mean return', 4133.0280905122636) ('std of return', 89.024141578474556) obs.shape = (25, 1000, 17) act.shape = (25, 1000, 6) expert data has been saved. loading and building expert policy ('obs', (1, 11), (1, 11)) loaded and built ('roll/traj', 0) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 1) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 2) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 3) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('steps', [1000, 1000, 1000, 1000]) ('returns', [3768.6945100631256, 3780.2323839436513, 3783.3528713417622, 3776.522165486092]) ('mean return', 3777.200482708658) ('std of return', 5.4739381594168464) obs.shape = (4, 1000, 11) act.shape = (4, 1000, 3) expert data has been saved. loading and building expert policy ('obs', (1, 11), (1, 11)) loaded and built ('roll/traj', 0) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 1) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 2) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 3) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 4) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 5) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 6) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 7) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 8) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 9) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 10) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('steps', [1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000]) ('returns', [3776.8642751689367, 3781.2504298398662, 3780.2410426724359, 3775.3710552957577, 3779.3902980707512, 3773.8634184348703, 3770.8866723075585, 3780.4207844762936, 3781.523831445847, 3774.5681184694013, 3779.319841231219]) ('mean return', 3777.6090697648124) ('std of return', 3.3513734023594219) obs.shape = (11, 1000, 11) act.shape = (11, 1000, 3) expert data has been saved. loading and building expert policy ('obs', (1, 11), (1, 11)) loaded and built ('roll/traj', 0) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 1) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 2) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 3) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 4) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 5) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 6) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 7) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 8) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 9) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 10) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 11) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 12) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 13) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 14) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 15) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 16) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 17) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('steps', [1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000]) ('returns', [3779.7050891800823, 3781.8261053421129, 3777.0348874624683, 3780.7564369429952, 3783.8023742794289, 3778.8540765735338, 3778.2838509060907, 3772.0179836318312, 3776.0436511846556, 3777.2671771759328, 3769.7932782467242, 3778.1612858500016, 3774.5197732402466, 3777.4526753302625, 3782.1303038320079, 3774.1603254054885, 3775.4472428566828, 3780.9678196906061]) ('mean return', 3777.6791298406192) ('std of return', 3.5415301661767842) obs.shape = (18, 1000, 11) act.shape = (18, 1000, 3) expert data has been saved. loading and building expert policy ('obs', (1, 11), (1, 11)) loaded and built ('roll/traj', 0) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 1) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 2) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 3) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 4) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 5) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 6) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 7) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 8) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 9) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 10) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 11) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 12) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 13) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 14) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 15) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 16) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 17) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 18) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 19) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 20) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 21) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 22) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 23) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 24) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('steps', [1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000]) ('returns', [3778.8392263167784, 3779.5006762211756, 3775.2079612199959, 3775.2461797134188, 3786.7071619918361, 3774.4111189507835, 3781.201699112346, 3775.7413417770244, 3784.7917817859939, 3775.7521896648682, 3782.0005480505479, 3774.0474578427702, 3777.5763692084424, 3778.0439539881991, 3779.5541702189857, 3778.9263420042721, 3770.50841808619, 3770.0953559770523, 3776.9041194485176, 3778.243240343334, 3775.3263028992428, 3779.0318634723531, 3775.5486034283458, 3777.3872833124879, 3773.873717042653]) ('mean return', 3777.3786832831042) ('std of return', 3.7442553033205863) obs.shape = (25, 1000, 11) act.shape = (25, 1000, 3) expert data has been saved. loading and building expert policy ('obs', (1, 17), (1, 17)) loaded and built ('roll/traj', 0) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 1) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 2) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 3) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('steps', [1000, 1000, 1000, 1000]) ('returns', [5547.48969243587, 5540.1468480510594, 5564.1321310009662, 5581.989894252667]) ('mean return', 5558.4396414351404) ('std of return', 16.136499915608695) obs.shape = (4, 1000, 17) act.shape = (4, 1000, 6) expert data has been saved. loading and building expert policy ('obs', (1, 17), (1, 17)) loaded and built ('roll/traj', 0) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 1) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 2) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 3) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 4) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 5) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 6) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 7) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 8) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 9) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 10) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('steps', [1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000]) ('returns', [5502.1118374247135, 5578.4542887283278, 5512.2398724981667, 5480.4346126756691, 5449.5133369384566, 5502.7943665881003, 5480.9276296636272, 5569.665719912703, 5327.7553945147201, 5572.5422903562821, 5457.7194853879455]) ('mean return', 5494.0144395171556) ('std of return', 67.950561745997945) obs.shape = (11, 1000, 17) act.shape = (11, 1000, 6) expert data has been saved. loading and building expert policy ('obs', (1, 17), (1, 17)) loaded and built ('roll/traj', 0) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 1) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 2) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 3) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 4) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 5) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 6) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 7) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 8) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 9) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 10) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 11) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 12) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 13) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 14) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 15) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 16) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 17) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('steps', [1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000]) ('returns', [5585.8651523527433, 5545.8725858595817, 5593.3858841511465, 5541.4536733079985, 5536.8433392198067, 5548.443185949196, 5498.6548050012489, 5475.8332938827907, 5549.4836474238609, 5459.2497362763906, 5459.0740379511835, 5528.9377877374009, 5556.5587139897398, 5604.7437566384324, 5514.978407911135, 5558.1672712935715, 5537.4998013342065, 5471.741539720425]) ('mean return', 5531.4881455556042) ('std of return', 42.793853572241282) obs.shape = (18, 1000, 17) act.shape = (18, 1000, 6) expert data has been saved. loading and building expert policy ('obs', (1, 17), (1, 17)) loaded and built ('roll/traj', 0) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 1) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 2) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 3) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 4) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 5) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 6) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 7) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 8) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 9) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 10) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 11) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 12) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 13) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 14) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 15) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 16) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 17) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 18) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 19) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 20) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 21) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 22) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 23) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 24) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('steps', [1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000]) ('returns', [5578.8211167348663, 5426.9564890547936, 5495.0790952607813, 5543.7290426113759, 5592.7433886393574, 5575.0766246791163, 5600.9145321887609, 5453.7858257194703, 5537.535331383041, 5528.1267955137027, 5535.8466728732074, 5583.1063825847186, 5560.6501884998252, 5544.3170077259037, 5485.541927125917, 5485.1008470458091, 5497.1258420310323, 5569.4957143113088, 5564.0545535147003, 5531.2521617385428, 5543.0294088290084, 5527.3037651829809, 5541.4369527234321, 5575.584682356186, 5532.5198507543264]) ('mean return', 5536.3653679632871) ('std of return', 42.16551288781001) obs.shape = (25, 1000, 17) act.shape = (25, 1000, 6) expert data has been saved. loading and building expert policy ('obs', (1, 111), (1, 111)) loaded and built ('roll/traj', 0) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 1) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 2) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 3) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('steps', [1000, 1000, 1000, 1000]) ('returns', [4681.2816282568092, 4787.1411375950993, 4962.6022230934659, 4718.8344067219441]) ('mean return', 4787.4648489168294) ('std of return', 108.00256775663604) obs.shape = (4, 1000, 111) act.shape = (4, 1000, 8) expert data has been saved. loading and building expert policy ('obs', (1, 111), (1, 111)) loaded and built ('roll/traj', 0) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 1) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 2) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 3) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 4) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 5) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 6) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 7) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 8) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 9) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 10) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('steps', [1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000]) ('returns', [4970.2446233142127, 4791.8262870918743, 4990.8731983221924, 4874.367844853743, 4782.723353789389, 4945.6210534642532, 4865.8945243349517, 4671.9618293821068, 4867.5339908714595, 4704.239325228632, 4938.5563507509096]) ('mean return', 4854.8947619457931) ('std of return', 101.36973550237347) obs.shape = (11, 1000, 111) act.shape = (11, 1000, 8) expert data has been saved. loading and building expert policy ('obs', (1, 111), (1, 111)) loaded and built ('roll/traj', 0) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 1) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 2) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 3) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 4) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 5) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 6) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 7) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 8) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 9) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 10) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 11) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 12) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 13) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 14) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 15) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 16) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 17) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('steps', [1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000]) ('returns', [4848.5062792733324, 4807.5472169657733, 5071.3916076107216, 4772.4505171213505, 4783.1801074056393, 4963.8292362281081, 4852.7933288258428, 4622.0828740587067, 4818.8940558568593, 4794.9563490573546, 4886.5164229950242, 4845.0225230289898, 4921.775360975862, 4793.3807631300815, 4778.7836096652518, 4896.3028634806333, 4806.808013490021, 4604.2827672429912]) ('mean return', 4826.0279942451407) ('std of return', 105.15493988748189) obs.shape = (18, 1000, 111) act.shape = (18, 1000, 8) expert data has been saved. loading and building expert policy ('obs', (1, 111), (1, 111)) loaded and built ('roll/traj', 0) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 1) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 2) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 3) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 4) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 ('roll/traj', 5) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 6) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 7) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 8) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 9) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 10) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 11) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 12) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 13) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 14) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 15) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 16) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 17) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 18) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 19) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 20) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 21) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 22) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 23) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 24) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('steps', [1000, 1000, 1000, 1000, 848, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000]) ('returns', [4754.9337197919212, 4670.2130539203708, 4819.8898310669547, 4791.8539952710335, 4064.1610110578895, 4846.4238471685767, 4628.0066176373239, 4950.5587981938552, 4689.3336871789252, 4954.1424188340579, 4885.0033215638896, 4884.1785356975643, 4956.1598758246191, 4878.141685788979, 4668.1071285208754, 5037.0403488927232, 4775.9386244523239, 4720.482841645392, 4917.8331223202549, 4637.6262947721079, 4864.5559085830791, 4765.1690928682719, 4984.9211936248039, 4695.7990112358748, 4797.3908402291418]) ('mean return', 4785.5145922456322) ('std of return', 185.68178387438161) obs.shape = (25, 1000, 111) act.shape = (25, 1000, 8) expert data has been saved. loading and building expert policy ('obs', (1, 376), (1, 376)) loaded and built ('roll/traj', 0) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 1) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 2) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 3) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 4) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 5) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 6) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 7) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 8) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 9) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 10) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 11) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 12) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 13) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 14) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 15) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 16) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 17) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 18) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 19) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 20) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 21) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 22) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 23) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 24) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 25) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 26) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 27) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 28) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 29) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 30) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 31) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 32) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 33) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 34) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 35) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 36) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 37) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 38) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 39) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 40) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 41) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 42) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 43) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 44) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 45) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 46) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 47) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 48) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 49) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 50) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 51) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 52) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 53) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 54) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 55) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 56) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 57) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 58) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 59) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 60) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 61) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 62) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 63) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 64) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 65) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 66) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 67) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 68) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 69) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 70) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 71) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 72) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 73) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 74) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 75) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 76) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 77) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 78) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 79) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('steps', [1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000]) ('returns', [10329.628439115793, 10398.40769333656, 10363.937395680477, 10427.772885501496, 10397.027566319715, 10480.217506670058, 10479.585647890968, 10424.793655049612, 10373.86341372582, 10451.954902520636, 10305.228173137471, 10387.087426785178, 10453.408417209403, 10459.609235675824, 10356.678362034181, 10398.510234478614, 10515.361236604076, 10436.751276740513, 10498.396235392102, 10370.772157990363, 10430.672213922286, 10426.891222992064, 10394.551522552107, 10445.06614354315, 10435.721466817618, 10386.238617037665, 10471.072341941242, 10402.245401001908, 10418.328439772547, 10379.46465504614, 10419.762363070555, 10392.181917234415, 10413.336573901424, 10402.971022743775, 10425.173517290992, 10397.79028895028, 10452.611322512847, 10386.092300526794, 10419.523884184186, 10301.980675007637, 10410.288969989273, 10385.581664314219, 10435.416096147484, 10429.411956568458, 10469.366651914606, 10498.987284536122, 10335.452597636722, 10361.801676501475, 10415.437900649116, 10359.093623262546, 10434.897488733488, 10489.523672114101, 10490.177397527659, 10388.55547638126, 10401.568538908834, 10439.716990475352, 10456.447774649503, 10436.715874399713, 10445.687900447212, 10459.767461022309, 10450.912812774666, 10400.022412165528, 10380.876547571568, 10404.604527280735, 10427.798994291225, 10465.62206122202, 10410.584622564747, 10379.356959054881, 10378.237741675022, 10351.699958008387, 10430.091076781926, 10447.880494902356, 10420.966302199247, 10376.269492971323, 10415.708895867358, 10387.617975152678, 10429.483388870894, 10468.32437980919, 10370.386539478415, 10366.425967282034]) ('mean return', 10415.217948725152) ('std of return', 43.377480472278329) obs.shape = (80, 1000, 376) act.shape = (80, 1000, 17) expert data has been saved. loading and building expert policy ('obs', (1, 376), (1, 376)) loaded and built ('roll/traj', 0) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 1) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 2) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 3) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 4) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 5) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 6) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 7) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 8) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 9) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 10) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 11) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 12) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 13) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 14) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 15) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 16) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 17) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 18) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 19) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 20) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 21) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 22) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 23) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 24) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 25) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 26) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 27) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 28) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 29) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 30) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 31) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 32) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 33) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 34) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 35) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 36) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 37) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 38) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 39) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 40) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 41) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 42) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 43) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 44) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 45) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 46) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 47) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 48) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 49) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 50) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 51) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 52) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 53) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 54) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 55) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 56) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 57) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 58) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 59) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 60) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 61) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 62) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 63) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 64) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 65) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 66) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 67) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 68) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 69) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 70) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 71) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 72) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 73) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 74) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 75) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 76) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 77) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 78) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 79) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 80) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 81) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 82) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 83) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 84) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 85) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 86) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 87) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 88) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 89) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 90) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 91) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 92) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 93) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 94) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 95) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 96) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 97) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 98) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 99) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 100) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 101) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 102) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 103) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 104) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 105) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 106) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 107) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 108) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 109) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 110) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 111) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 112) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 113) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 114) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 115) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 116) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 117) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 118) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 119) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 120) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 121) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 122) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 123) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 124) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 125) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 126) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 127) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 128) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 129) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 130) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 131) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 132) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 133) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 134) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 135) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 136) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 137) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 138) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 139) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 140) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 141) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 142) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 143) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 144) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 145) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 146) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 147) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 148) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 149) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 150) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 151) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 152) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 153) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 154) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 155) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 156) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 157) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 158) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 159) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('steps', [1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 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10400.632701710943, 10416.669621179697, 10276.638537743353, 10451.765342424274, 10325.61237757394, 10403.698634306571, 10440.485902785687, 10396.395646652431, 10381.090569354996, 10383.448626388783, 10409.214568325104, 10456.129785239242, 10396.076454384205, 10446.146525160382, 10444.714613069202, 10481.364722831169, 10482.614076759115, 10359.848966341915, 10448.104631147276, 10429.533199705202, 10349.853354558692, 10380.280019446729, 10423.117348283296, 10413.211546436249, 10346.124737317437, 10510.825034017083, 10486.313543964088, 10396.845288704506, 10401.494402286997, 10445.340512404808, 10434.403029474815, 10388.302267239604, 10412.754505634748, 10376.949443825182, 10393.018126891815, 10433.213791286351, 10421.152012774721, 10186.344624600346, 10344.49120008269, 10343.996541394808, 10413.359454476365, 10447.690701808311, 10328.198282106212, 10444.002274656699, 10379.84969917434, 10321.388161571269, 10433.82289639493, 10412.841410261621, 10371.889775892891, 10427.675977073088, 10390.497618321489, 10498.835163041722, 10394.654046401409, 10495.03278044463, 10427.264469624544, 10516.451735081086, 10440.677304396846, 10451.095179872322, 10339.01297731753, 10416.064377272531, 10385.277856504987, 10446.098255812693, 10477.363623988707, 10337.205649890591, 10433.560924742498, 10398.504043972673, 10444.008071255814, 10457.594456360755, 10486.141167680124, 10380.752306783172, 10341.330094617224, 10367.67561648005, 10440.95883467388, 10306.306715833394, 10445.656606537519, 10454.775640870996, 10290.568617039909, 10389.119039196186, 10325.744217813219, 10237.367014540105, 10261.826908243245, 10360.634578658341, 10471.424822712983, 10381.783356597498, 10433.17492549642, 10485.087354167277, 10354.714589113271, 10421.06611572363, 10424.774213241597, 10467.833384009717, 10371.979916305236, 10466.426261599292, 10409.449391913096, 10380.835049441102, 10410.073548892695, 10367.193723856519, 10379.088885072279, 10467.696826160809, 10464.128752202789, 10386.74244888222, 10293.183884741829, 10483.476608244462, 10458.08024040571, 10460.411334963712, 10433.210048462861, 10441.604706421725, 10389.483742197081, 10431.16297873758, 10440.07447219877, 10396.946535618768, 10418.482020834617, 10322.581761528521, 10414.728150806106, 10460.013436823698, 10415.143800597814, 10470.79940001007, 10548.32062854944, 10470.238487157361, 10366.476299413807, 10344.317240008371, 10305.634106269641, 10389.172160090457, 10354.519992588741, 10428.600682833614, 10319.42771561299, 10435.137602835264, 10421.888519098182, 10299.99848375721, 10415.667749808155, 10398.732209077052, 10469.894975191943, 10410.759600796177, 10453.668437671595, 10341.595393798012, 10393.504827407303, 10425.195590290434, 10428.973097295844, 10400.902230025302, 10405.970511773929, 10291.347476732706, 10356.527889358571, 10450.119402821079, 10455.911873713219, 10407.519825571921, 10390.519102863, 10331.557411498277, 10315.628237308545, 10360.292187022018, 10448.939786697532, 10376.699852817559, 10449.694663498973, 10326.637755602909, 10388.850561283136, 10425.00257666954]) ('mean return', 10403.972335063499) ('std of return', 56.536074597213009) obs.shape = (160, 1000, 376) act.shape = (160, 1000, 17) expert data has been saved. loading and building expert policy ('obs', (1, 376), (1, 376)) loaded and built ('roll/traj', 0) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 1) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 2) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 3) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 4) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 5) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 6) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 7) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 8) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 9) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 10) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 11) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 12) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 13) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 14) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 15) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 16) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 17) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 18) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 19) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 20) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 21) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 22) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 23) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 24) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 25) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 26) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 27) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 28) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 29) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 30) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 31) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 32) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 33) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 34) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 35) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 36) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 37) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 38) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 39) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 40) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 41) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 42) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 43) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 44) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 45) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 46) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 47) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 48) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 49) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 50) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 51) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 52) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 53) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 54) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 55) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 56) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 57) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 58) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 59) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 60) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 61) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 62) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 63) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 64) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 65) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 66) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 67) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 68) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 69) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 70) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 71) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 72) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 73) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 74) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 75) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 76) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 77) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 78) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 79) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 80) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 81) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 82) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 83) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 84) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 85) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 86) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 87) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 88) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 89) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 90) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 91) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 92) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 93) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 94) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 95) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 96) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 97) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 98) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 99) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 100) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 101) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 102) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 103) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 104) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 105) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 106) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 107) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 108) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 109) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 110) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 111) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 112) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 113) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 114) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 115) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 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('roll/traj', 125) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 126) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 127) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 128) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 129) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 130) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 131) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 132) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 133) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 134) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 135) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 136) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 137) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 138) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 139) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 140) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 141) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 142) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 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('roll/traj', 233) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 234) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 235) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 236) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 237) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 238) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('roll/traj', 239) 100/1000 200/1000 300/1000 400/1000 500/1000 600/1000 700/1000 800/1000 900/1000 1000/1000 ('steps', [1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 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10482.403526232087, 10461.183494042869]) ('mean return', 10399.47124849325) ('std of return', 54.868826640962055) obs.shape = (240, 1000, 376) act.shape = (240, 1000, 17) expert data has been saved. ================================================ FILE: bc/run_expert.py ================================================ """ Code to load an expert policy and generate roll-out data for behavioral cloning. Example usage: python run_expert.py experts/Humanoid-v1.pkl Humanoid-v1 --render \ --num_rollouts 20 Author of this script and included expert policies: Jonathan Ho (hoj@openai.com) (Daniel) I save an array of trajectories of shape (numtrajs, numtimes, obs_dim) // observations (numtrajs, numtimes, act_dim) // actions, squeezed as needed // and also a list of returns and steps, each of length `numtrajs`. However this requires padding some zeros at the end for trajectories that didn't manage to finish (should be rare with experts, but it can still happen). Thus, I also save a trajectory *lengths* array which can tell us when to stop dealing with a trajectory. """ import pickle import tensorflow as tf import numpy as np import tf_util import gym import load_policy def main(): import argparse parser = argparse.ArgumentParser() parser.add_argument('expert_policy_file', type=str) parser.add_argument('envname', type=str) parser.add_argument('--render', action='store_true') parser.add_argument('--save', action='store_true') parser.add_argument('--max_timesteps', type=int) parser.add_argument('--num_rollouts', type=int, default=20, help='Number of expert roll outs') args = parser.parse_args() print('loading and building expert policy') policy_fn = load_policy.load_policy(args.expert_policy_file) print('loaded and built') with tf.Session(): tf_util.initialize() import gym env = gym.make(args.envname) max_steps = args.max_timesteps or env.spec.timestep_limit all_observations = [] all_actions = [] all_steps = [] all_returns = [] for i in range(args.num_rollouts): print('roll/traj', i) obs = env.reset() done = False totalr = 0. steps = 0 observations = [] actions = [] while not done: action = policy_fn(obs[None,:]) observations.append(obs) actions.append(action) obs, r, done, _ = env.step(action) totalr += r steps += 1 if args.render: env.render() if steps % 100 == 0: print("%i/%i"%(steps, max_steps)) if steps >= max_steps: break all_returns.append(totalr) all_steps.append(steps) # Ensure that observations and actions lengths are at max_steps. # To make it easy, just append the last obs/action since they are # automatically the correct dimension, reduces headaches. while steps < max_steps: observations.append(obs) actions.append(action) steps += 1 assert len(observations) == max_steps, "{}".format(len(observations)) assert len(actions) == max_steps, "{}".format(len(actions)) all_observations.append(observations) all_actions.append(actions) # Squeezing since we know MuJoCo does some (1,D)-dim actions. expert_data = {'observations': np.array(all_observations), 'actions': np.squeeze(np.array(all_actions)), 'returns': all_returns, 'steps': all_steps} print('steps', all_steps) print('returns', all_returns) print('mean return', np.mean(all_returns)) print('std of return', np.std(all_returns)) print("obs.shape = {}".format(expert_data['observations'].shape)) print("act.shape = {}".format(expert_data['actions'].shape)) if args.save: str_roll = str(args.num_rollouts).zfill(3) np.save("expert_data/" +args.envname+ "_" +str_roll, expert_data) print("expert data has been saved.") if __name__ == '__main__': main() ================================================ FILE: bc/tf_util.py ================================================ import numpy as np import tensorflow as tf # pylint: ignore-module #import builtins import functools import copy import os import collections # ================================================================ # Import all names into common namespace # ================================================================ clip = tf.clip_by_value # Make consistent with numpy # ---------------------------------------- def sum(x, axis=None, keepdims=False): return tf.reduce_sum(x, reduction_indices=None if axis is None else [axis], keep_dims = keepdims) def mean(x, axis=None, keepdims=False): return tf.reduce_mean(x, reduction_indices=None if axis is None else [axis], keep_dims = keepdims) def var(x, axis=None, keepdims=False): meanx = mean(x, axis=axis, keepdims=keepdims) return mean(tf.square(x - meanx), axis=axis, keepdims=keepdims) def std(x, axis=None, keepdims=False): return tf.sqrt(var(x, axis=axis, keepdims=keepdims)) def max(x, axis=None, keepdims=False): return tf.reduce_max(x, reduction_indices=None if axis is None else [axis], keep_dims = keepdims) def min(x, axis=None, keepdims=False): return tf.reduce_min(x, reduction_indices=None if axis is None else [axis], keep_dims = keepdims) def concatenate(arrs, axis=0): return tf.concat(axis, arrs) def argmax(x, axis=None): return tf.argmax(x, dimension=axis) def switch(condition, then_expression, else_expression): '''Switches between two operations depending on a scalar value (int or bool). Note that both `then_expression` and `else_expression` should be symbolic tensors of the *same shape*. # Arguments condition: scalar tensor. then_expression: TensorFlow operation. else_expression: TensorFlow operation. ''' x_shape = copy.copy(then_expression.get_shape()) x = tf.cond(tf.cast(condition, 'bool'), lambda: then_expression, lambda: else_expression) x.set_shape(x_shape) return x # Extras # ---------------------------------------- def l2loss(params): if len(params) == 0: return tf.constant(0.0) else: return tf.add_n([sum(tf.square(p)) for p in params]) def lrelu(x, leak=0.2): f1 = 0.5 * (1 + leak) f2 = 0.5 * (1 - leak) return f1 * x + f2 * abs(x) def categorical_sample_logits(X): # https://github.com/tensorflow/tensorflow/issues/456 U = tf.random_uniform(tf.shape(X)) return argmax(X - tf.log(-tf.log(U)), axis=1) # ================================================================ # Global session # ================================================================ def get_session(): return tf.get_default_session() def single_threaded_session(): tf_config = tf.ConfigProto( inter_op_parallelism_threads=1, intra_op_parallelism_threads=1) return tf.Session(config=tf_config) def make_session(num_cpu): tf_config = tf.ConfigProto( inter_op_parallelism_threads=num_cpu, intra_op_parallelism_threads=num_cpu) return tf.Session(config=tf_config) ALREADY_INITIALIZED = set() def initialize(): new_variables = set(tf.global_variables()) - ALREADY_INITIALIZED get_session().run(tf.variables_initializer(new_variables)) ALREADY_INITIALIZED.update(new_variables) def eval(expr, feed_dict=None): if feed_dict is None: feed_dict = {} return get_session().run(expr, feed_dict=feed_dict) def set_value(v, val): get_session().run(v.assign(val)) def load_state(fname): saver = tf.train.Saver() saver.restore(get_session(), fname) def save_state(fname): os.makedirs(os.path.dirname(fname), exist_ok=True) saver = tf.train.Saver() saver.save(get_session(), fname) # ================================================================ # Model components # ================================================================ def normc_initializer(std=1.0): def _initializer(shape, dtype=None, partition_info=None): #pylint: disable=W0613 out = np.random.randn(*shape).astype(np.float32) out *= std / np.sqrt(np.square(out).sum(axis=0, keepdims=True)) return tf.constant(out) return _initializer def conv2d(x, num_filters, name, filter_size=(3, 3), stride=(1, 1), pad="SAME", dtype=tf.float32, collections=None, summary_tag=None): with tf.variable_scope(name): stride_shape = [1, stride[0], stride[1], 1] filter_shape = [filter_size[0], filter_size[1], int(x.get_shape()[3]), num_filters] # there are "num input feature maps * filter height * filter width" # inputs to each hidden unit fan_in = intprod(filter_shape[:3]) # each unit in the lower layer receives a gradient from: # "num output feature maps * filter height * filter width" / # pooling size fan_out = intprod(filter_shape[:2]) * num_filters # initialize weights with random weights w_bound = np.sqrt(6. / (fan_in + fan_out)) w = tf.get_variable("W", filter_shape, dtype, tf.random_uniform_initializer(-w_bound, w_bound), collections=collections) b = tf.get_variable("b", [1, 1, 1, num_filters], initializer=tf.zeros_initializer, collections=collections) if summary_tag is not None: tf.image_summary(summary_tag, tf.transpose(tf.reshape(w, [filter_size[0], filter_size[1], -1, 1]), [2, 0, 1, 3]), max_images=10) return tf.nn.conv2d(x, w, stride_shape, pad) + b def dense(x, size, name, weight_init=None, bias=True): w = tf.get_variable(name + "/w", [x.get_shape()[1], size], initializer=weight_init) ret = tf.matmul(x, w) if bias: b = tf.get_variable(name + "/b", [size], initializer=tf.zeros_initializer) return ret + b else: return ret def wndense(x, size, name, init_scale=1.0): v = tf.get_variable(name + "/V", [int(x.get_shape()[1]), size], initializer=tf.random_normal_initializer(0, 0.05)) g = tf.get_variable(name + "/g", [size], initializer=tf.constant_initializer(init_scale)) b = tf.get_variable(name + "/b", [size], initializer=tf.constant_initializer(0.0)) # use weight normalization (Salimans & Kingma, 2016) x = tf.matmul(x, v) scaler = g / tf.sqrt(sum(tf.square(v), axis=0, keepdims=True)) return tf.reshape(scaler, [1, size]) * x + tf.reshape(b, [1, size]) def densenobias(x, size, name, weight_init=None): return dense(x, size, name, weight_init=weight_init, bias=False) def dropout(x, pkeep, phase=None, mask=None): mask = tf.floor(pkeep + tf.random_uniform(tf.shape(x))) if mask is None else mask if phase is None: return mask * x else: return switch(phase, mask*x, pkeep*x) def batchnorm(x, name, phase, updates, gamma=0.96): k = x.get_shape()[1] runningmean = tf.get_variable(name+"/mean", shape=[1, k], initializer=tf.constant_initializer(0.0), trainable=False) runningvar = tf.get_variable(name+"/var", shape=[1, k], initializer=tf.constant_initializer(1e-4), trainable=False) testy = (x - runningmean) / tf.sqrt(runningvar) mean_ = mean(x, axis=0, keepdims=True) var_ = mean(tf.square(x), axis=0, keepdims=True) std = tf.sqrt(var_) trainy = (x - mean_) / std updates.extend([ tf.assign(runningmean, runningmean * gamma + mean_ * (1 - gamma)), tf.assign(runningvar, runningvar * gamma + var_ * (1 - gamma)) ]) y = switch(phase, trainy, testy) out = y * tf.get_variable(name+"/scaling", shape=[1, k], initializer=tf.constant_initializer(1.0), trainable=True)\ + tf.get_variable(name+"/translation", shape=[1,k], initializer=tf.constant_initializer(0.0), trainable=True) return out # ================================================================ # Basic Stuff # ================================================================ def function(inputs, outputs, updates=None, givens=None): if isinstance(outputs, list): return _Function(inputs, outputs, updates, givens=givens) elif isinstance(outputs, (dict, collections.OrderedDict)): f = _Function(inputs, outputs.values(), updates, givens=givens) return lambda *inputs : type(outputs)(zip(outputs.keys(), f(*inputs))) else: f = _Function(inputs, [outputs], updates, givens=givens) return lambda *inputs : f(*inputs)[0] class _Function(object): def __init__(self, inputs, outputs, updates, givens, check_nan=False): assert all(len(i.op.inputs)==0 for i in inputs), "inputs should all be placeholders" self.inputs = inputs updates = updates or [] self.update_group = tf.group(*updates) self.outputs_update = list(outputs) + [self.update_group] self.givens = {} if givens is None else givens self.check_nan = check_nan def __call__(self, *inputvals): assert len(inputvals) == len(self.inputs) feed_dict = dict(zip(self.inputs, inputvals)) feed_dict.update(self.givens) results = get_session().run(self.outputs_update, feed_dict=feed_dict)[:-1] if self.check_nan: if any(np.isnan(r).any() for r in results): raise RuntimeError("Nan detected") return results def mem_friendly_function(nondata_inputs, data_inputs, outputs, batch_size): if isinstance(outputs, list): return _MemFriendlyFunction(nondata_inputs, data_inputs, outputs, batch_size) else: f = _MemFriendlyFunction(nondata_inputs, data_inputs, [outputs], batch_size) return lambda *inputs : f(*inputs)[0] class _MemFriendlyFunction(object): def __init__(self, nondata_inputs, data_inputs, outputs, batch_size): self.nondata_inputs = nondata_inputs self.data_inputs = data_inputs self.outputs = list(outputs) self.batch_size = batch_size def __call__(self, *inputvals): assert len(inputvals) == len(self.nondata_inputs) + len(self.data_inputs) nondata_vals = inputvals[0:len(self.nondata_inputs)] data_vals = inputvals[len(self.nondata_inputs):] feed_dict = dict(zip(self.nondata_inputs, nondata_vals)) n = data_vals[0].shape[0] for v in data_vals[1:]: assert v.shape[0] == n for i_start in range(0, n, self.batch_size): slice_vals = [v[i_start:min(i_start+self.batch_size, n)] for v in data_vals] for (var,val) in zip(self.data_inputs, slice_vals): feed_dict[var]=val results = tf.get_default_session().run(self.outputs, feed_dict=feed_dict) if i_start==0: sum_results = results else: for i in range(len(results)): sum_results[i] = sum_results[i] + results[i] for i in range(len(results)): sum_results[i] = sum_results[i] / n return sum_results # ================================================================ # Modules # ================================================================ class Module(object): def __init__(self, name): self.name = name self.first_time = True self.scope = None self.cache = {} def __call__(self, *args): if args in self.cache: print("(%s) retrieving value from cache"%self.name) return self.cache[args] with tf.variable_scope(self.name, reuse=not self.first_time): scope = tf.get_variable_scope().name if self.first_time: self.scope = scope print("(%s) running function for the first time"%self.name) else: assert self.scope == scope, "Tried calling function with a different scope" print("(%s) running function on new inputs"%self.name) self.first_time = False out = self._call(*args) self.cache[args] = out return out def _call(self, *args): raise NotImplementedError @property def trainable_variables(self): assert self.scope is not None, "need to call module once before getting variables" return tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, self.scope) @property def variables(self): assert self.scope is not None, "need to call module once before getting variables" return tf.get_collection(tf.GraphKeys.VARIABLES, self.scope) def module(name): @functools.wraps def wrapper(f): class WrapperModule(Module): def _call(self, *args): return f(*args) return WrapperModule(name) return wrapper # ================================================================ # Graph traversal # ================================================================ VARIABLES = {} def get_parents(node): return node.op.inputs def topsorted(outputs): """ Topological sort via non-recursive depth-first search """ assert isinstance(outputs, (list,tuple)) marks = {} out = [] stack = [] #pylint: disable=W0621 # i: node # jidx = number of children visited so far from that node # marks: state of each node, which is one of # 0: haven't visited # 1: have visited, but not done visiting children # 2: done visiting children for x in outputs: stack.append((x,0)) while stack: (i,jidx) = stack.pop() if jidx == 0: m = marks.get(i,0) if m == 0: marks[i] = 1 elif m == 1: raise ValueError("not a dag") else: continue ps = get_parents(i) if jidx == len(ps): marks[i] = 2 out.append(i) else: stack.append((i,jidx+1)) j = ps[jidx] stack.append((j,0)) return out # ================================================================ # Flat vectors # ================================================================ def var_shape(x): out = [k.value for k in x.get_shape()] assert all(isinstance(a, int) for a in out), \ "shape function assumes that shape is fully known" return out def numel(x): return intprod(var_shape(x)) def intprod(x): return int(np.prod(x)) def flatgrad(loss, var_list): grads = tf.gradients(loss, var_list) return tf.concat(0, [tf.reshape(grad, [numel(v)]) for (v, grad) in zip(var_list, grads)]) class SetFromFlat(object): def __init__(self, var_list, dtype=tf.float32): assigns = [] shapes = list(map(var_shape, var_list)) total_size = np.sum([intprod(shape) for shape in shapes]) self.theta = theta = tf.placeholder(dtype,[total_size]) start=0 assigns = [] for (shape,v) in zip(shapes,var_list): size = intprod(shape) assigns.append(tf.assign(v, tf.reshape(theta[start:start+size],shape))) start+=size self.op = tf.group(*assigns) def __call__(self, theta): get_session().run(self.op, feed_dict={self.theta:theta}) class GetFlat(object): def __init__(self, var_list): self.op = tf.concat(0, [tf.reshape(v, [numel(v)]) for v in var_list]) def __call__(self): return get_session().run(self.op) # ================================================================ # Misc # ================================================================ def fancy_slice_2d(X, inds0, inds1): """ like numpy X[inds0, inds1] XXX this implementation is bad """ inds0 = tf.cast(inds0, tf.int64) inds1 = tf.cast(inds1, tf.int64) shape = tf.cast(tf.shape(X), tf.int64) ncols = shape[1] Xflat = tf.reshape(X, [-1]) return tf.gather(Xflat, inds0 * ncols + inds1) def scope_vars(scope, trainable_only): """ Get variables inside a scope The scope can be specified as a string """ return tf.get_collection( tf.GraphKeys.TRAINABLE_VARIABLES if trainable_only else tf.GraphKeys.VARIABLES, scope=scope if isinstance(scope, str) else scope.name ) def lengths_to_mask(lengths_b, max_length): """ Turns a vector of lengths into a boolean mask Args: lengths_b: an integer vector of lengths max_length: maximum length to fill the mask Returns: a boolean array of shape (batch_size, max_length) row[i] consists of True repeated lengths_b[i] times, followed by False """ lengths_b = tf.convert_to_tensor(lengths_b) assert lengths_b.get_shape().ndims == 1 mask_bt = tf.expand_dims(tf.range(max_length), 0) < tf.expand_dims(lengths_b, 1) return mask_bt def in_session(f): @functools.wraps(f) def newfunc(*args, **kwargs): with tf.Session(): f(*args, **kwargs) return newfunc _PLACEHOLDER_CACHE = {} # name -> (placeholder, dtype, shape) def get_placeholder(name, dtype, shape): print("calling get_placeholder", name) if name in _PLACEHOLDER_CACHE: out, dtype1, shape1 = _PLACEHOLDER_CACHE[name] assert dtype1==dtype and shape1==shape return out else: out = tf.placeholder(dtype=dtype, shape=shape, name=name) _PLACEHOLDER_CACHE[name] = (out,dtype,shape) return out def get_placeholder_cached(name): return _PLACEHOLDER_CACHE[name][0] def flattenallbut0(x): return tf.reshape(x, [-1, intprod(x.get_shape().as_list()[1:])]) def reset(): global _PLACEHOLDER_CACHE global VARIABLES _PLACEHOLDER_CACHE = {} VARIABLES = {} tf.reset_default_graph() ================================================ FILE: ddpg/README.md ================================================ # Deep Deterministic Policy Gradients - Python 3.5 - Tensorflow 1.2 I'm following the original DDPG paper as much as possible, and using their "low-dimensional" representation, not the pixels-based one. ## Pendulum-v0 Action space: -2 to 2. ``` python main.py Pendulum-v0 ``` Status: not yet working. I think it's done but alas there is some bug somewhere. Ugh. ## References (These might be useful to supplement the original paper.) - http://pemami4911.github.io/blog/2016/08/21/ddpg-rl.html - https://github.com/rmst/ddpg - https://github.com/openai/rllab - https://github.com/yukezhu/tensorflow-reinforce - https://github.com/stevenpjg/ddpg-aigym ================================================ FILE: ddpg/ddpg.py ================================================ """ Deep Deterministic Policy Gradients Make Actor and Critic subclasses of a NNet class? Not sure ... for now, I'll put everything here but that might take a lot. """ import gym import numpy as np import sys import tensorflow as tf import tensorflow.contrib.layers as layers import time from replay_buffer import ReplayBuffer from collections import defaultdict sys.path.append("../") from utils import logz class DDPGAgent(object): def __init__(self, sess, env, test_env, args): self.sess = sess self.args = args self.env = env self.test_env = test_env self.ob_dim = env.observation_space.shape[0] self.ac_dim = env.action_space.shape[0] # Construct the networks and the experience replay buffer. self.actor = Actor(sess, env, args) self.critic = Critic(sess, env, args) self.rbuffer = ReplayBuffer(args.replay_size, self.ob_dim, self.ac_dim) # Initialize then run, also setting current=target to start. self._debug_print() self.sess.run(tf.global_variables_initializer()) self.actor.update_target_net(smooth=False) self.critic.update_target_net(smooth=False) def train(self): """ Algorithm 1 in the DDPG paper. """ num_episodes = 0 t_start = time.time() obs = self.env.reset() for t in range(self.args.n_iter): if (t % self.args.log_every_t_iter == 0) and (t > self.args.wait_until_rbuffer): print("\n*** DDPG Iteration {} ***".format(t)) # Sample actions with noise injection and manage buffer. act = self.actor.sample_action(obs, train=True) new_obs, rew, done, info = self.env.step(act) self.rbuffer.add_sample(s=obs, a=act, r=rew, done=done) if done: obs = self.env.reset() num_episodes += 1 else: obs = new_obs if (t > self.args.wait_until_rbuffer) and (t % self.args.learning_freq == 0): # Sample from the replay buffer. states_t_BO, actions_t_BA, rewards_t_B, states_tp1_BO, done_mask_B = \ self.rbuffer.sample(num=self.args.batch_size) feed = {'obs_t_BO': states_t_BO, 'act_t_BA': actions_t_BA, 'rew_t_B': rewards_t_B, 'obs_tp1_BO': states_tp1_BO, 'done_mask_B': done_mask_B} # Update the critic, get sampled policy gradients, update actor. a_grads_BA, l2_error = self.critic.update_weights(feed) actor_gradients = self.actor.update_weights(feed, a_grads_BA) # Update both target networks. self.critic.update_target_net() self.actor.update_target_net() if (t % self.args.log_every_t_iter == 0) and (t > self.args.wait_until_rbuffer): # Do some rollouts here and then record statistics. Note that # some of these stats rely on stuff computed from sampling the # replay buffer, so be careful interpreting these. The code # probably needs to guard against this case as well. stats = self._do_rollouts() hours = (time.time()-t_start) / (60*60.) logz.log_tabular("MeanReward", np.mean(stats['reward'])) logz.log_tabular("MaxReward", np.max(stats['reward'])) logz.log_tabular("MinReward", np.min(stats['reward'])) logz.log_tabular("StdReward", np.std(stats['reward'])) logz.log_tabular("MeanLength", np.mean(stats['length'])) logz.log_tabular("NumTrainingEps", num_episodes) logz.log_tabular("L2ErrorCritic", l2_error) logz.log_tabular("QaGradL2Norm", np.linalg.norm(a_grads_BA)) logz.log_tabular("TimeHours", hours) logz.log_tabular("Iterations", t) logz.dump_tabular() def _do_rollouts(self): """ Some rollouts to evaluate the agent's progress. Returns a dictionary containing relevant statistics. Later, I should parallelize this using an array of environments. """ num_episodes = 50 stats = defaultdict(list) for i in range(num_episodes): obs = self.test_env.reset() ep_time = 0 ep_reward = 0 # Run one episode ... while True: act = self.actor.sample_action(obs, train=False) new_obs, rew, done, info = self.test_env.step(act) ep_time += 1 ep_reward += rew if done: break # ... and collect its information here. stats['length'].append(ep_time) stats['reward'].append(ep_reward) return stats def _debug_print(self): print("\n\t(A bunch of debug prints)\n") print("\nActor weights") for v in self.actor.weights: shp = v.get_shape().as_list() print("- {} shape:{} size:{}".format(v.name, shp, np.prod(shp))) print("Total # of weights: {}.".format(self.actor.num_weights)) print("\nCritic weights") for v in self.critic.weights: shp = v.get_shape().as_list() print("- {} shape:{} size:{}".format(v.name, shp, np.prod(shp))) print("Total # of weights: {}.".format(self.critic.num_weights)) class Network(object): """ Just so the Actor and Critic nets don't have more duplicate code. This way they can refer to the similar sets of placeholders (but not the exact same ones in memory, just a copy) and I can change it easily here. """ def __init__(self, sess, env, args): self.sess = sess self.args = args # Some random stuff. assert len(env.observation_space.shape) == 1 assert len(env.action_space.shape) == 1 self.ob_dim = env.observation_space.shape[0] self.ac_dim = env.action_space.shape[0] self.ac_high = env.action_space.high self.ac_low = env.action_space.low # Placeholders for minibatches of data. End of episode = 1 for mask. self.obs_t_BO = tf.placeholder(tf.float32, [None,self.ob_dim]) self.act_t_BA = tf.placeholder(tf.float32, [None,self.ac_dim]) self.rew_t_B = tf.placeholder(tf.float32, [None]) self.obs_tp1_BO = tf.placeholder(tf.float32, [None,self.ob_dim]) self.done_mask_B = tf.placeholder(tf.float32, [None]) class Actor(Network): """ Given input as a batch of states, the actor deterministically provides us with actions, indicated as "mu" in the paper. Since DDPG is off-policy, we can treat the problem of exploration independently from the learning algorithm. External to this class, I add Gaussian noise for this purpose. """ def __init__(self, sess, env, args): super().__init__(sess, env, args) # The action network and its corresponding taget. self.actions_BA = self._build_net(self.obs_t_BO, scope='ActorNet') self.actions_targ_BA = self._build_net(self.obs_t_BO, scope='TargActorNet') # Collect weights since it's generally convenient to do so. self.weights = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='ActorNet') self.weights_targ = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='TargActorNet') self.weights_v = tf.concat([tf.reshape(w, [-1]) for w in self.weights], axis=0) self.weights_v_targ = tf.concat([tf.reshape(w, [-1]) for w in self.weights_targ], axis=0) # These should be the same among both nets. self.w_shapes = [w.get_shape().as_list() for w in self.weights] self.num_weights = np.sum([np.prod(sh) for sh in self.w_shapes]) # Update the target action network. Provide hard and smooth updates. target_smooth = [] target_hard = [] for var, var_target in zip(sorted(self.weights, key=lambda v: v.name), sorted(self.weights_targ, key=lambda v: v.name)): update_sm = self.args.tau * var + (1 - self.args.tau) * var_target target_smooth.append(var_target.assign(update_sm)) target_hard.append(var_target.assign(var)) self.update_target_smooth = tf.group(*target_smooth) self.update_target_hard = tf.group(*target_hard) # The Actor _update_, with one gradient provided by the critic which # serves as initialization (I think) since we need to multiply. Negate # it (I think) since we we want to minimize a loss function. self.a_grads_BA = tf.placeholder(tf.float32, [None,self.ac_dim]) self.actor_gradients = tf.gradients(self.actions_BA, self.weights, -self.a_grads_BA) self.optimize_a = tf.train.AdamOptimizer(self.args.step_size_actor).\ apply_gradients(zip(self.actor_gradients, self.weights)) def _build_net(self, input_BO, scope): """ The Actor network. Uses ReLUs for all hidden layers, but a tanh to the output to bound the action. This follows their 'low-dimensional networks' using 400 and 300 units for the hidden layers. Set `reuse=False`. I don't use batch normalization or their precise weight initialization. """ with tf.variable_scope(scope, reuse=False): hidden1 = layers.fully_connected(input_BO, num_outputs=400, weights_initializer=layers.xavier_initializer(), activation_fn=tf.nn.relu) hidden2 = layers.fully_connected(hidden1, num_outputs=300, weights_initializer=layers.xavier_initializer(), activation_fn=tf.nn.relu) actions_BA = layers.fully_connected(hidden2, num_outputs=self.ac_dim, weights_initializer=layers.xavier_initializer(), activation_fn=tf.nn.tanh) # Note the tanh! # This should broadcast, but haven't tested with ac_dim > 1. actions_BA = tf.multiply(actions_BA, self.ac_high) return actions_BA def sample_action(self, obs, train=True): """ Samples an action. TODO we don't have their exact Gaussian noise injection process because I can't figure out how to implement it. :-( Parameters ---------- obs: [np.array] Represents current states. We assume we need to expand it. train: [boolean] True means we need to inject noise. False is for test evaluation. """ act = self.sess.run(self.actions_BA, {self.obs_t_BO: obs[None]}) act = act[0] assert self.ac_low < act < self.ac_high if train: return act + np.random.normal(loc=self.args.ou_noise_theta, scale=self.args.ou_noise_sigma, size=act.shape) else: return act def update_target_net(self, smooth=True): """ Update the target network based on the current weights. Normally we do this with smooth=True except for the first step, or unless we want to see how poorly hard updates perform generally. """ if smooth: self.sess.run(self.update_target_smooth) else: self.sess.run(self.update_target_hard) def update_weights(self, f, a_grads_BA): """ Gradient-based update of current actor parameters. """ feed = {self.obs_t_BO: f['obs_t_BO'], self.a_grads_BA: a_grads_BA} _, actor_gradients = self.sess.run([self.optimize_a, \ self.actor_gradients], feed) return actor_gradients class Critic(Network): """ Computes Q(s,a) values to encourage the Actor to learn better policies. This is colloquially referred to as 'Q' in the paper. """ def __init__(self, sess, env, args): super().__init__(sess, env, args) # The critic network (i.e. Q-values) and its corresponding target. self.qvals_B = self._build_net(self.obs_t_BO, self.act_t_BA, scope='CriticNet') self.qvals_targ_B = self._build_net(self.obs_t_BO, self.act_t_BA, scope='TargCriticNet') # Collect weights since it's generally convenient to do so. self.weights = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='CriticNet') self.weights_targ = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='TargCriticNet') self.weights_v = tf.concat([tf.reshape(w, [-1]) for w in self.weights], axis=0) self.weights_v_targ = tf.concat([tf.reshape(w, [-1]) for w in self.weights_targ], axis=0) # These should be the same among both nets. self.w_shapes = [w.get_shape().as_list() for w in self.weights] self.num_weights = np.sum([np.prod(sh) for sh in self.w_shapes]) # Update the target action network. Provide hard and smooth updates. target_smooth = [] target_hard = [] for var, var_target in zip(sorted(self.weights, key=lambda v: v.name), sorted(self.weights_targ, key=lambda v: v.name)): update_sm = self.args.tau * var + (1 - self.args.tau) * var_target target_smooth.append(var_target.assign(update_sm)) target_hard.append(var_target.assign(var)) self.update_target_smooth = tf.group(*target_smooth) self.update_target_hard = tf.group(*target_hard) # The _critic_ uses y_i, the target for its loss. Depends on `done` mask! self.target_val_B = self.rew_t_B + (self.args.Q_gamma * self.qvals_targ_B) * (1 - self.done_mask_B) self.l2_error = tf.reduce_mean(tf.square(self.target_val_B - self.qvals_B)) # TODO l2 weight decay? # Use the built-in Adam optimizer, but might want to try gradient clipping? self.optimize_c = tf.train.AdamOptimizer(self.args.step_size_critic).minimize(self.l2_error) # Then return this in the gradient step to provide to the Actor. # TODO should check this, it _should_ deal with gradients row-wise, and # then the gradient can be summed over B. Where is the summing over B? # Is this also equivalent if I did targ = tf.reduce_sum(self.qvals_B)? I # think so because it doesn't matter if we sum, action in b-th minibatch # only (directly) affects the b-th Q-value and has a gradient, right? self.act_grads_BA = tf.gradients(self.qvals_B, self.act_t_BA) def _build_net(self, input_BO, acts_BO, scope): """ The critic network. Use ReLUs for all hidden layers. The output consists of one Q-value for each batch. Set `reuse=False`. I don't use batch normalization or their precise weight initialization. Unlike the critic, it uses actions here but they are NOT included in the first hidden layer. In addition, we do a tf.reshape to get an output of shape (B,), not (B,1). Seems like tf.squeeze doesn't work with `?`. """ with tf.variable_scope(scope, reuse=False): hidden1 = layers.fully_connected(input_BO, num_outputs=400, weights_initializer=layers.xavier_initializer(), activation_fn=tf.nn.relu) # Insert the concatenation here. This should be fine, I think. state_action = tf.concat(axis=1, values=[hidden1, acts_BO]) hidden2 = layers.fully_connected(state_action, num_outputs=300, weights_initializer=layers.xavier_initializer(), activation_fn=tf.nn.relu) qvals_B = layers.fully_connected(hidden2, num_outputs=1, weights_initializer=layers.xavier_initializer(), activation_fn=None) return tf.reshape(qvals_B, shape=[-1]) def update_target_net(self, smooth=True): """ Update the target network based on the current weights. Normally we do this with smooth=True except for the first step, or unless we want to see how poorly hard updates perform generally. """ if smooth: self.sess.run(self.update_target_smooth) else: self.sess.run(self.update_target_hard) def update_weights(self, f): """ Gradient-based update of current Critic parameters. Also return the action gradients for the Actor update later. This is the dQ/da in the paper, and Q is the current Q network, not the target Q network. """ feed = { self.obs_t_BO: f['obs_t_BO'], self.act_t_BA: f['act_t_BA'], self.rew_t_B: f['rew_t_B'], self.obs_tp1_BO: f['obs_tp1_BO'], self.done_mask_B: f['done_mask_B'] } action_grads_BA, _, l2_error = self.sess.run([self.act_grads_BA, \ self.optimize_c, self.l2_error], feed) # We assume that the only item in the list has what we want. assert len(action_grads_BA) == 1 return action_grads_BA[0], l2_error ================================================ FILE: ddpg/main.py ================================================ """ Main script for DDPG code, for CONTINUOUS control environments. (c) 2017 by Daniel Seita, though mostly building upon other code as usual, with credit attrbuted to in the DDPG's README. """ from ddpg import DDPGAgent import argparse import gym import numpy as np np.set_printoptions(suppress=True, precision=5, edgeitems=10) import pickle import sys import tensorflow as tf if "../" not in sys.path: sys.path.append("../") from utils import utils_pg as utils from utils import value_functions as vfuncs from utils import logz from utils import policies if __name__ == "__main__": p = argparse.ArgumentParser() p.add_argument('envname', type=str) # DDPG stuff, all directly from the paper. p.add_argument('--batch_size', type=int, default=64) # Used 16 on pixels. p.add_argument('--ou_noise_theta', type=float, default=0.15) p.add_argument('--ou_noise_sigma', type=float, default=0.2) p.add_argument('--Q_gamma', type=float, default=0.99) p.add_argument('--Q_l2_weight_decay', type=float, default=1e-2) p.add_argument('--replay_size', type=int, default=1000000) p.add_argument('--step_size_actor', type=float, default=1e-4) p.add_argument('--step_size_critic', type=float, default=1e-3) p.add_argument('--tau', type=float, default=0.001) # Other stuff that I use for my own or based on other code. p.add_argument('--do_not_save', action='store_true') p.add_argument('--learning_freq', type=int, default=50) p.add_argument('--log_every_t_iter', type=int, default=50) p.add_argument('--max_gradient', type=float, default=10.0) p.add_argument('--n_iter', type=int, default=10000) p.add_argument('--seed', type=int, default=0) p.add_argument('--wait_until_rbuffer', type=int, default=1000) args = p.parse_args() # Handle the log directory and save the arguments. logdir = 'out/' +args.envname+ '/seed' +str(args.seed).zfill(2) if args.do_not_save: logdir = None logz.configure_output_dir(logdir) if logdir is not None: with open(logdir+'/args.pkl', 'wb') as f: pickle.dump(args, f) print("Saving in logdir: {}".format(logdir)) # Other stuff for seeding and getting things set up. tf.set_random_seed(args.seed) np.random.seed(args.seed) env = gym.make(args.envname) test_env = gym.make(args.envname) tf_config = tf.ConfigProto(inter_op_parallelism_threads=1, intra_op_parallelism_threads=1) sess = tf.Session(config=tf_config) ddpg = DDPGAgent(sess, env, test_env, args) ddpg.train() ================================================ FILE: ddpg/replay_buffer.py ================================================ import numpy as np import sys class ReplayBuffer(object): def __init__(self, size, ob_dim, ac_dim): """ A replay buffer to store transitions (s,a,r,s') for DDPG. - We save with numpy arrays, because there doesn't seem to be a better alternative. (I'm not sure if deques are memory efficient.) Create a *fixed* numpy array to start. - Use `self.end_idx` to identify the index of the most recent transition. It starts at 0, increases towards the buffer limit, then wraps around 0 as needed. Instead of "throwing transitions away" we simply override them. - It can be tricky to think about successor states. Since a full observation sequence is {s0,a0,r0,s1,a1,r1,s2,...} we should always have an equal amount of states, actions, and reward stored, but the successor state will be "known" before the action and the reward at its correponding index. I think the easiest way to resolve this is to limit the API of `add_sample` so that it doesn't have to worry about successor states. Then here, we have a "done" mask which can tell us when to ignore the successor. In general, there will still _be_ a state stored at the next time index, but the "done" mask informs us about if it's actually a successor, or simply the beginning state of the next episode. - Values are 1 in the "done mask" if the next state corresponds to the end of an episode when doing env.step(), which is equivalent to saying that the next state stored in this buffer is a start state. Parameters ---------- size: [int] Maximum number of transitions to store in the buffer. When the buffer overflows the old memories are over-written. ob_dim: [int] State dimension, assumes an integer and not a list or tuple. ac_dim: [int] Action dimension, assumes an integer and not a list or tuple. """ self.next_idx = 0 self.num_in_buffer = 0 self.size = size self.states_NO = np.zeros((size, ob_dim), dtype=np.float32) self.actions_NA = np.zeros((size, ac_dim), dtype=np.float32) self.rewards_N = np.zeros((size,), dtype=np.float32) self.done_N = np.zeros((size,), dtype=np.uint8) def add_sample(self, s, a, r, done): """ Stores transition (s,a,r) along with the `done` boolean. States (`ob`) that exist as a result of `ob = env.reset()` should be added like usual states. The action and reward as a result of `obsucc, rew, done, _ = env.step(act)` should be added to the same index as `ob`. Successor states (`obsucc` here) are stored in the _next_ index, which in rare cases wraps around the buffer size to be zero. However, we add the successor state in the next set of calls outside the code. Use `self.next_idx` to store the index, NOT `self.num_in_buffer`. The former will automatically override old samples. """ self.states_NO[self.next_idx] = s self.actions_NA[self.next_idx] = a self.rewards_N[self.next_idx] = r self.done_N[self.next_idx] = int(done) self.num_in_buffer += 1 self.next_idx = (self.next_idx + 1) % self.size def sample(self, num): """ Sample `num` transitions (s,a,r,s') for a minibatch. We can use the minimum of the number we've added so far and the max buffer size to determine the range of indices to consider when sampling (_without_ replacement). When taking the successor states, we increment the indices by one and wrap to zero as needed. Don't forget the `done` mask! This means we ignore the state at time t plus one ("tp1", i.e. the successor state) since it is ignored with the loss function. And the successor at that point would actually be the start of the _next_ episode. The `-1` in the `max_index` computation handles annoying corner case of having buffer partially filled and avoiding an un-touched index. """ assert num < self.num_in_buffer max_index = min(self.num_in_buffer-1, self.size) indices = np.random.choice(max_index, num, replace=False) # Make next indices (+1) equal to index `self.size` back to zero. below_thresh = ((indices+1) < self.size).astype(int) indices_next = (indices+1) * below_thresh # Get the minibatches for training purposes. states_t_BO = self.states_NO[indices] actions_t_BA = self.actions_NA[indices] rewards_t_B = self.rewards_N[indices] states_tp1_BO = self.states_NO[indices_next] done_mask_B = self.done_N[indices] return (states_t_BO, actions_t_BA, rewards_t_B, states_tp1_BO, done_mask_B) ================================================ FILE: dqn/README.md ================================================ # Deep Q-Networks The starter code is from UC Berkeley's Deep Reinforcement Learning class. These are their comments: > See http://rll.berkeley.edu/deeprlcourse/docs/hw3.pdf for instructions > > The starter code was based on an implementation of Q-learning for Atari > generously provided by Szymon Sidor from OpenAI The rest of this README contains my comments and results. # Usage, Games, etc. First, here is example usage (slashes are only for readability here): ``` python run_dqn_atari.py --game Pong --seed 1 --num_timesteps 30000000 | tee logs_text/Pong_s001.text ``` With these settings, the statistics for plotting data will be stored in the `log_pkls/Pong_s001.pkl` file. Here are some of the `task` stuff in the code, ordered by index (i.e. 0, 1, etc.). ``` Task Task Task Task Task ``` The default for these is 40 million episodes, but that's not always needed for the easier games. The `num_timesteps` parameter corresponds to the number of steps in the "underlying" environment, *not* the "wrapped" environment. See the stopping criterion: ``` def stopping_criterion(env, t): # notice that here t is the number of steps of the wrapped env, # which is different from the number of steps in the underlying env return get_wrapper_by_name(env, "Monitor").get_total_steps() >= num_timesteps ``` The `t` here is what I think of as "the number of steps." It's confusing, I know. There might be a better way to handle this. From now on, when I say "steps", it refers to the `t`-like number, and *not* the `num_timesteps` parameter. # Results Notes: - Timing results are based on running with an NVIDIA Titan X with Pascal GPU. - Scores per Episode indicate scores for every episode. - Scores per Timestep indicate the score of the current episode at a given timestep; each episode requires some number of timesteps for the agent to complete it. Theres' a lot of them, so I take every 10,000. - Blocks mean taking an interval of some size (100) and taking the mean. ## Pong Commands: ``` python run_dqn_atari.py --game Pong --seed 1 --num_timesteps 30000000 | tee logs_text/Pong_s001.text python run_dqn_atari.py --game Pong --seed 2 --num_timesteps 30000000 | tee logs_text/Pong_s002.text ``` - `num_timesteps`: 30 million - Training steps: about 7.5 million - Episodes: about 4200 - Time: about 9.0 hours ![pong](figures/Pong.png?raw=true) ## Breakout Command: ``` python run_dqn_atari.py --game Breakout --seed 1 --num_timesteps 40000000 | tee logs_text/Breakout_s001.text python run_dqn_atari.py --game Breakout --seed 2 --num_timesteps 40000000 | tee logs_text/Breakout_s002.text ``` - `num_timesteps`: 40 million - Training steps: about 9.7 million - Episodes: about 10,200 - Time: about 11.8 hours I have no idea why the performance plummeted after 4500 episodes. I may need to investigate. Note that a few times we get the absolute perfect highest score (two full boards cleared); I think the "800" value as the maximum score is wrong ... ![breakout](figures/Breakout.png?raw=true) ## BeamRider Command: ``` python run_dqn_atari.py --game BeamRider --seed 1 --num_timesteps 40000000 | tee logs_text/BeamRider_s001.text python run_dqn_atari.py --game BeamRider --seed 2 --num_timesteps 40000000 | tee logs_text/BeamRider_s002.text ``` - `num_timesteps`: 40 million - Training steps: about 10.0 million - Episodes: about 2,500 to 4,000 - Time: about 12.5 hours The results look different among the seeds since seed 2 apparently had better peformance earlier, thus meaning its episodes became longer sooner than the seed 1 version. At least they look reasonably good. A3C got roughly 13k on this game. ![beamrider](figures/BeamRider.png?raw=true) ## Enduro Command: ``` python run_dqn_atari.py --game Enduro --seed 1 --num_timesteps 40000000 | tee logs_text/Enduro_s001.text python run_dqn_atari.py --game Enduro --seed 2 --num_timesteps 40000000 | tee logs_text/Enduro_s002.text ``` - `num_timesteps`: 40 million - Training steps: about 10.0 million - Episodes: about 2,500 to 3,000 - Time: about 12.5 hours Ack, what happened to seed 2?!? The first seed matches the DQN result (475.6) from the Nature script, yet I don't know why the second one failed to learn much. It's worth noting, though, that the A3C paper actually reported -82.2 on this game (really?!?). Oh well ... ![enduro](figures/Enduro.png?raw=true) ================================================ FILE: dqn/atari_wrappers.py ================================================ import cv2 import numpy as np from collections import deque import gym from gym import spaces class NoopResetEnv(gym.Wrapper): def __init__(self, env=None, noop_max=30): """Sample initial states by taking random number of no-ops on reset. No-op is assumed to be action 0. """ super(NoopResetEnv, self).__init__(env) self.noop_max = noop_max assert env.unwrapped.get_action_meanings()[0] == 'NOOP' def _reset(self): """ Do no-op action for a number of steps in [1, noop_max].""" self.env.reset() noops = np.random.randint(1, self.noop_max + 1) for _ in range(noops): obs, _, _, _ = self.env.step(0) return obs class FireResetEnv(gym.Wrapper): def __init__(self, env=None): """Take action on reset for environments that are fixed until firing.""" super(FireResetEnv, self).__init__(env) assert env.unwrapped.get_action_meanings()[1] == 'FIRE' assert len(env.unwrapped.get_action_meanings()) >= 3 def _reset(self): self.env.reset() obs, _, _, _ = self.env.step(1) obs, _, _, _ = self.env.step(2) return obs class EpisodicLifeEnv(gym.Wrapper): def __init__(self, env=None): """Make end-of-life == end-of-episode, but only reset on true game over. Done by DeepMind for the DQN and co. since it helps value estimation. """ super(EpisodicLifeEnv, self).__init__(env) self.lives = 0 self.was_real_done = True self.was_real_reset = False def _step(self, action): obs, reward, done, info = self.env.step(action) self.was_real_done = done # check current lives, make loss of life terminal, # then update lives to handle bonus lives lives = self.env.unwrapped.ale.lives() if lives < self.lives and lives > 0: # for Qbert somtimes we stay in lives == 0 condtion for a few frames # so its important to keep lives > 0, so that we only reset once # the environment advertises done. done = True self.lives = lives return obs, reward, done, info def _reset(self): """Reset only when lives are exhausted. This way all states are still reachable even though lives are episodic, and the learner need not know about any of this behind-the-scenes. """ if self.was_real_done: obs = self.env.reset() self.was_real_reset = True else: # no-op step to advance from terminal/lost life state obs, _, _, _ = self.env.step(0) self.was_real_reset = False self.lives = self.env.unwrapped.ale.lives() return obs class MaxAndSkipEnv(gym.Wrapper): def __init__(self, env=None, skip=4): """Return only every `skip`-th frame""" super(MaxAndSkipEnv, self).__init__(env) # most recent raw observations (for max pooling across time steps) self._obs_buffer = deque(maxlen=2) self._skip = skip def _step(self, action): total_reward = 0.0 done = None for _ in range(self._skip): obs, reward, done, info = self.env.step(action) self._obs_buffer.append(obs) total_reward += reward if done: break max_frame = np.max(np.stack(self._obs_buffer), axis=0) return max_frame, total_reward, done, info def _reset(self): """Clear past frame buffer and init. to first obs. from inner env.""" self._obs_buffer.clear() obs = self.env.reset() self._obs_buffer.append(obs) return obs def _process_frame84(frame): img = np.reshape(frame, [210, 160, 3]).astype(np.float32) img = img[:, :, 0] * 0.299 + img[:, :, 1] * 0.587 + img[:, :, 2] * 0.114 resized_screen = cv2.resize(img, (84, 110), interpolation=cv2.INTER_LINEAR) x_t = resized_screen[18:102, :] x_t = np.reshape(x_t, [84, 84, 1]) return x_t.astype(np.uint8) class ProcessFrame84(gym.Wrapper): def __init__(self, env=None): super(ProcessFrame84, self).__init__(env) self.observation_space = spaces.Box(low=0, high=255, shape=(84, 84, 1)) def _step(self, action): obs, reward, done, info = self.env.step(action) return _process_frame84(obs), reward, done, info def _reset(self): return _process_frame84(self.env.reset()) class ClippedRewardsWrapper(gym.Wrapper): def _step(self, action): obs, reward, done, info = self.env.step(action) return obs, np.sign(reward), done, info def wrap_deepmind_ram(env): env = EpisodicLifeEnv(env) env = NoopResetEnv(env, noop_max=30) env = MaxAndSkipEnv(env, skip=4) if 'FIRE' in env.unwrapped.get_action_meanings(): env = FireResetEnv(env) env = ClippedRewardsWrapper(env) return env def wrap_deepmind(env): assert 'NoFrameskip' in env.spec.id env = EpisodicLifeEnv(env) env = NoopResetEnv(env, noop_max=30) env = MaxAndSkipEnv(env, skip=4) if 'FIRE' in env.unwrapped.get_action_meanings(): env = FireResetEnv(env) env = ProcessFrame84(env) env = ClippedRewardsWrapper(env) return env ================================================ FILE: dqn/dqn.py ================================================ import sys import time import pickle import gym.spaces import itertools import numpy as np import random import tensorflow as tf import tensorflow.contrib.layers as layers from collections import namedtuple from dqn_utils import * OptimizerSpec = namedtuple("OptimizerSpec", ["constructor", "kwargs", "lr_schedule"]) def learn(env, q_func, optimizer_spec, session, exploration=LinearSchedule(1000000, 0.1), stopping_criterion=None, replay_buffer_size=1000000, batch_size=32, gamma=0.99, learning_starts=50000, learning_freq=4, frame_history_len=4, target_update_freq=10000, grad_norm_clipping=10, log_file='./logs_pkls/rewards.pkl'): """Run Deep Q-learning algorithm. You can specify your own convnet using q_func. All schedules are w.r.t. total number of steps taken in the environment. Parameters ---------- env: gym.Env gym environment to train on. q_func: function Model to use for computing the q function. It should accept the following named arguments: img_in: tf.Tensor tensorflow tensor representing the input image num_actions: int number of actions scope: str scope in which all the model related variables should be created reuse: bool whether previously created variables should be reused. optimizer_spec: OptimizerSpec Specifying the constructor and kwargs, as well as learning rate schedule for the optimizer session: tf.Session tensorflow session to use. exploration: rl_algs.deepq.utils.schedules.Schedule schedule for probability of chosing random action. stopping_criterion: (env, t) -> bool should return true when it's ok for the RL algorithm to stop. takes in env and the number of steps executed so far. replay_buffer_size: int How many memories to store in the replay buffer. batch_size: int How many transitions to sample each time experience is replayed. gamma: float Discount Factor learning_starts: int After how many environment steps to start replaying experiences learning_freq: int How many steps of environment to take between every experience replay frame_history_len: int How many past frames to include as input to the model. target_update_freq: int How many experience replay rounds (not steps!) to perform between each update to the target Q network grad_norm_clipping: float or None If not None gradients' norms are clipped to this value. log_file: string Indicates where to save the resulting scores, for plotting later. """ assert type(env.observation_space) == gym.spaces.Box assert type(env.action_space) == gym.spaces.Discrete ############### # BUILD MODEL # ############### if len(env.observation_space.shape) == 1: # This means we are running on low-dimensional observations (e.g. RAM) input_shape = env.observation_space.shape else: img_h, img_w, img_c = env.observation_space.shape input_shape = (img_h, img_w, frame_history_len * img_c) num_actions = env.action_space.n # set up placeholders # placeholder for current observation (or state) obs_t_ph = tf.placeholder(tf.uint8, [None] + list(input_shape)) # placeholder for current action act_t_ph = tf.placeholder(tf.int32, [None]) # placeholder for current reward rew_t_ph = tf.placeholder(tf.float32, [None]) # placeholder for next observation (or state) obs_tp1_ph = tf.placeholder(tf.uint8, [None] + list(input_shape)) # placeholder for end of episode mask # this value is 1 if the next state corresponds to the end of an episode, # in which case there is no Q-value at the next state; at the end of an # episode, only the current state reward contributes to the target, not the # next state Q-value (i.e. target is just rew_t_ph, not rew_t_ph + gamma * q_tp1) done_mask_ph = tf.placeholder(tf.float32, [None]) # casting to float on GPU ensures lower data transfer times. obs_t_float = tf.cast(obs_t_ph, tf.float32) / 255.0 obs_tp1_float = tf.cast(obs_tp1_ph, tf.float32) / 255.0 # Here, you should fill in your own code to compute the Bellman error. This requires # evaluating the current and next Q-values and constructing the corresponding error. # TensorFlow will differentiate this error for you, you just need to pass it to the # optimizer. See assignment text for details. # Your code should produce one scalar-valued tensor: total_error # This will be passed to the optimizer in the provided code below. # Your code should also produce two collections of variables: # q_func_vars # target_q_func_vars # These should hold all of the variables of the Q-function network and target network, # respectively. A convenient way to get these is to make use of TF's "scope" feature. # For example, you can create your Q-function network with the scope "q_func" like this: # = q_func(obs_t_float, num_actions, scope="q_func", reuse=False) # And then you can obtain the variables like this: # q_func_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='q_func') # Older versions of TensorFlow may require using "VARIABLES" instead of "GLOBAL_VARIABLES" ###### # Building the networks. Have current_net so I can also call Q-values. act_one_hot = tf.one_hot(act_t_ph, num_actions, on_value=1.0, off_value=0.0) current_net = q_func(obs_t_float, num_actions, scope="q_func") current_q = tf.reduce_sum(current_net * act_one_hot, axis=1) target_q = tf.reduce_max(q_func(obs_tp1_float, num_actions, scope="target_q_func"), axis=1) # Now form the loss function and collect variables. target_val = rew_t_ph + (gamma * target_q) * (1 - done_mask_ph) total_error = tf.nn.l2_loss(target_val-current_q) / batch_size q_func_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='q_func') target_q_func_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='target_q_func') ###### # construct optimization op (with gradient clipping) learning_rate = tf.placeholder(tf.float32, (), name="learning_rate") optimizer = optimizer_spec.constructor(learning_rate=learning_rate, **optimizer_spec.kwargs) train_fn = minimize_and_clip(optimizer, total_error, var_list=q_func_vars, clip_val=grad_norm_clipping) # update_target_fn will be called periodically to copy Q network to target Q network update_target_fn = [] for var, var_target in zip(sorted(q_func_vars, key=lambda v: v.name), sorted(target_q_func_vars, key=lambda v: v.name)): update_target_fn.append(var_target.assign(var)) update_target_fn = tf.group(*update_target_fn) # construct the replay buffer replay_buffer = ReplayBuffer(replay_buffer_size, frame_history_len) ############### # RUN ENV # ############### scores_for_log = [] model_initialized = False num_param_updates = 0 mean_episode_reward = -float('nan') best_mean_episode_reward = -float('inf') last_obs = env.reset() # A numpy structure with shape (height, width, 1). LOG_EVERY_N_STEPS = 10000 t_start = time.time() for t in itertools.count(): ### 1. Check stopping criterion if stopping_criterion is not None and stopping_criterion(env, t): break ### 2. Step the env and store the transition # At this point, "last_obs" contains the latest observation that was # recorded from the simulator. Here, your code needs to store this # observation and its outcome (reward, next observation, etc.) into # the replay buffer while stepping the simulator forward one step. # At the end of this block of code, the simulator should have been # advanced one step, and the replay buffer should contain one more # transition. # Specifically, last_obs must point to the new latest observation. # Useful functions you'll need to call: # obs, reward, done, info = env.step(action) # this steps the environment forward one step # obs = env.reset() # this resets the environment if you reached an episode boundary. # Don't forget to call env.reset() to get a new observation if done # is true!! # Note that you cannot use "last_obs" directly as input # into your network, since it needs to be processed to include context # from previous frames. You should check out the replay buffer # implementation in dqn_utils.py to see what functionality the replay # buffer exposes. The replay buffer has a function called # encode_recent_observation that will take the latest observation # that you pushed into the buffer and compute the corresponding # input that should be given to a Q network by appending some # previous frames. # Don't forget to include epsilon greedy exploration! # And remember that the first time you enter this loop, the model # may not yet have been initialized (but of course, the first step # might as well be random, since you haven't trained your net...) ##### # This section is for one trial. Don't worry about batch sizes here. rb_index = replay_buffer.store_frame(last_obs) if (np.random.rand() < exploration.value(t) or not model_initialized): action = np.random.randint(num_actions) else: current_phi = replay_buffer.encode_recent_observation() current_phi = np.expand_dims(current_phi, axis=0) action = np.argmax(np.squeeze( session.run(current_net, feed_dict={obs_t_ph: current_phi}) )) obs, reward, done, info = env.step(action) replay_buffer.store_effect(rb_index, action, reward, done) if done: obs = env.reset() last_obs = obs ##### # at this point, the environment should have been advanced one step (and # reset if done was true), and last_obs should point to the new latest # observation ### 3. Perform experience replay and train the network. # note that this is only done if the replay buffer contains enough samples # for us to learn something useful -- until then, the model will not be # initialized and random actions should be taken if (t > learning_starts and t % learning_freq == 0 and replay_buffer.can_sample(batch_size)): # Here, you should perform training. Training consists of four steps: # 3.a: use the replay buffer to sample a batch of transitions (see the # replay buffer code for function definition, each batch that you sample # should consist of current observations, current actions, rewards, # next observations, and done indicator). # 3.b: initialize the model if it has not been initialized yet; to do # that, call # initialize_interdependent_variables(session, tf.global_variables(), { # obs_t_ph: obs_t_batch, # obs_tp1_ph: obs_tp1_batch, # }) # where obs_t_batch and obs_tp1_batch are the batches of observations at # the current and next time step. The boolean variable model_initialized # indicates whether or not the model has been initialized. # Remember that you have to update the target network too (see 3.d)! # 3.c: train the model. To do this, you'll need to use the train_fn and # total_error ops that were created earlier: total_error is what you # created to compute the total Bellman error in a batch, and train_fn # will actually perform a gradient step and update the network parameters # to reduce total_error. When calling session.run on these you'll need to # populate the following placeholders: # obs_t_ph # act_t_ph # rew_t_ph # obs_tp1_ph # done_mask_ph # (this is needed for computing total_error) # learning_rate -- you can get this from optimizer_spec.lr_schedule.value(t) # (this is needed by the optimizer to choose the learning rate) # 3.d: periodically update the target network by calling # session.run(update_target_fn) # you should update every target_update_freq steps, and you may find the # variable num_param_updates useful for this (it was initialized to 0) ##### obs_t_batch, act_batch, rew_batch, obs_tp1_batch, done_mask = \ replay_buffer.sample(batch_size) if (not model_initialized): initialize_interdependent_variables(session, tf.global_variables(), { obs_t_ph: obs_t_batch, obs_tp1_ph: obs_tp1_batch, }) model_initialized = True session.run(train_fn, feed_dict = { obs_t_ph: obs_t_batch, act_t_ph: act_batch, rew_t_ph: rew_batch, obs_tp1_ph: obs_tp1_batch, done_mask_ph: done_mask, learning_rate: optimizer_spec.lr_schedule.value(t) } ) # After some number of xp-replay updates, update the target network. if (num_param_updates % target_update_freq): session.run(update_target_fn) num_param_updates += 1 ##### ### 4. Log progress. episode_rewards = get_wrapper_by_name(env, "Monitor").get_episode_rewards() if len(episode_rewards) > 0: mean_episode_reward = np.mean(episode_rewards[-100:]) if len(episode_rewards) > 100: best_mean_episode_reward = max(best_mean_episode_reward, mean_episode_reward) # Report results, write to file. If case handles very last iteration. if ((t % LOG_EVERY_N_STEPS == 0 and model_initialized) or (stopping_criterion is not None and stopping_criterion(env,t+1))): current_episode_reward = episode_rewards[-1] print("\nTimestep: {}".format(t)) print("mean reward (100 episodes): {:.4f}".format(mean_episode_reward)) print("best mean reward: {:.4f}".format(best_mean_episode_reward)) print("current episode reward: {:.4f}".format(current_episode_reward)) print("episodes: {}".format(len(episode_rewards))) print("exploration: {:.5f}".format(exploration.value(t))) print("learning_rate: {:.5f}".format(optimizer_spec.lr_schedule.value(t))) seconds = (time.time()-t_start) hours = (time.time()-t_start) / (60*60) print("elapsed time: {:.1f} seconds ({:.2f} hours)".format(seconds,hours)) sys.stdout.flush() scores_for_log.append((t, mean_episode_reward, best_mean_episode_reward, current_episode_reward)) with open('./logs_pkls/'+log_file,'wb') as f: pickle.dump(scores_for_log, f) pickle.dump(episode_rewards, f) ================================================ FILE: dqn/dqn_utils.py ================================================ """This file includes a collection of utility functions that are useful for implementing DQN.""" import gym import tensorflow as tf import numpy as np import random def huber_loss(x, delta=1.0): # https://en.wikipedia.org/wiki/Huber_loss return tf.select( tf.abs(x) < delta, tf.square(x) * 0.5, delta * (tf.abs(x) - 0.5 * delta) ) def sample_n_unique(sampling_f, n): """Helper function. Given a function `sampling_f` that returns comparable objects, sample n such unique objects. """ res = [] while len(res) < n: candidate = sampling_f() if candidate not in res: res.append(candidate) return res class Schedule(object): def value(self, t): """Value of the schedule at time t""" raise NotImplementedError() class ConstantSchedule(object): def __init__(self, value): """Value remains constant over time. Parameters ---------- value: float Constant value of the schedule """ self._v = value def value(self, t): """See Schedule.value""" return self._v def linear_interpolation(l, r, alpha): return l + alpha * (r - l) class PiecewiseSchedule(object): def __init__(self, endpoints, interpolation=linear_interpolation, outside_value=None): """Piecewise schedule. endpoints: [(int, int)] list of pairs `(time, value)` meanining that schedule should output `value` when `t==time`. All the values for time must be sorted in an increasing order. When t is between two times, e.g. `(time_a, value_a)` and `(time_b, value_b)`, such that `time_a <= t < time_b` then value outputs `interpolation(value_a, value_b, alpha)` where alpha is a fraction of time passed between `time_a` and `time_b` for time `t`. interpolation: lambda float, float, float: float a function that takes value to the left and to the right of t according to the `endpoints`. Alpha is the fraction of distance from left endpoint to right endpoint that t has covered. See linear_interpolation for example. outside_value: float if the value is requested outside of all the intervals sepecified in `endpoints` this value is returned. If None then AssertionError is raised when outside value is requested. """ idxes = [e[0] for e in endpoints] assert idxes == sorted(idxes) self._interpolation = interpolation self._outside_value = outside_value self._endpoints = endpoints def value(self, t): """See Schedule.value""" for (l_t, l), (r_t, r) in zip(self._endpoints[:-1], self._endpoints[1:]): if l_t <= t and t < r_t: alpha = float(t - l_t) / (r_t - l_t) return self._interpolation(l, r, alpha) # t does not belong to any of the pieces, so doom. assert self._outside_value is not None return self._outside_value class LinearSchedule(object): def __init__(self, schedule_timesteps, final_p, initial_p=1.0): """Linear interpolation between initial_p and final_p over schedule_timesteps. After this many timesteps pass final_p is returned. Parameters ---------- schedule_timesteps: int Number of timesteps for which to linearly anneal initial_p to final_p initial_p: float initial output value final_p: float final output value """ self.schedule_timesteps = schedule_timesteps self.final_p = final_p self.initial_p = initial_p def value(self, t): """See Schedule.value""" fraction = min(float(t) / self.schedule_timesteps, 1.0) return self.initial_p + fraction * (self.final_p - self.initial_p) def compute_exponential_averages(variables, decay): """Given a list of tensorflow scalar variables create ops corresponding to their exponential averages Parameters ---------- variables: [tf.Tensor] List of scalar tensors. Returns ------- averages: [tf.Tensor] List of scalar tensors corresponding to averages of al the `variables` (in order) apply_op: tf.runnable Op to be run to update the averages with current value of variables. """ averager = tf.train.ExponentialMovingAverage(decay=decay) apply_op = averager.apply(variables) return [averager.average(v) for v in variables], apply_op def minimize_and_clip(optimizer, objective, var_list, clip_val=10): """Minimized `objective` using `optimizer` w.r.t. variables in `var_list` while ensure the norm of the gradients for each variable is clipped to `clip_val` """ gradients = optimizer.compute_gradients(objective, var_list=var_list) for i, (grad, var) in enumerate(gradients): if grad is not None: gradients[i] = (tf.clip_by_norm(grad, clip_val), var) return optimizer.apply_gradients(gradients) def initialize_interdependent_variables(session, vars_list, feed_dict): """Initialize a list of variables one at a time, which is useful if initialization of some variables depends on initialization of the others. """ vars_left = vars_list while len(vars_left) > 0: new_vars_left = [] for v in vars_left: try: # If using an older version of TensorFlow, uncomment the line # below and comment out the line after it. #session.run(tf.initialize_variables([v]), feed_dict) session.run(tf.variables_initializer([v]), feed_dict) except tf.errors.FailedPreconditionError: new_vars_left.append(v) if len(new_vars_left) >= len(vars_left): # This can happend if the variables all depend on each other, or more likely if there's # another variable outside of the list, that still needs to be initialized. This could be # detected here, but life's finite. raise Exception("Cycle in variable dependencies, or extenrnal precondition unsatisfied.") else: vars_left = new_vars_left def get_wrapper_by_name(env, classname): currentenv = env while True: if classname in currentenv.__class__.__name__: return currentenv elif isinstance(env, gym.Wrapper): currentenv = currentenv.env else: raise ValueError("Couldn't find wrapper named %s"%classname) class ReplayBuffer(object): def __init__(self, size, frame_history_len): """This is a memory efficient implementation of the replay buffer. The sepecific memory optimizations use here are: - only store each frame once rather than k times even if every observation normally consists of k last frames - store frames as np.uint8 (actually it is most time-performance to cast them back to float32 on GPU to minimize memory transfer time) - store frame_t and frame_(t+1) in the same buffer. For the typical use case in Atari Deep RL buffer with 1M frames the total memory footprint of this buffer is 10^6 * 84 * 84 bytes ~= 7 gigabytes Warning! Assumes that returning frame of zeros at the beginning of the episode, when there is less frames than `frame_history_len`, is acceptable. Parameters ---------- size: int Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped. frame_history_len: int Number of memories to be retried for each observation. """ self.size = size self.frame_history_len = frame_history_len self.next_idx = 0 self.num_in_buffer = 0 self.obs = None self.action = None self.reward = None self.done = None def can_sample(self, batch_size): """Returns true if `batch_size` different transitions can be sampled from the buffer.""" return batch_size + 1 <= self.num_in_buffer def _encode_sample(self, idxes): obs_batch = np.concatenate([self._encode_observation(idx)[None] for idx in idxes], 0) act_batch = self.action[idxes] rew_batch = self.reward[idxes] next_obs_batch = np.concatenate([self._encode_observation(idx + 1)[None] for idx in idxes], 0) done_mask = np.array([1.0 if self.done[idx] else 0.0 for idx in idxes], dtype=np.float32) return obs_batch, act_batch, rew_batch, next_obs_batch, done_mask def sample(self, batch_size): """Sample `batch_size` different transitions. i-th sample transition is the following: when observing `obs_batch[i]`, action `act_batch[i]` was taken, after which reward `rew_batch[i]` was received and subsequent observation next_obs_batch[i] was observed, unless the epsiode was done which is represented by `done_mask[i]` which is equal to 1 if episode has ended as a result of that action. Parameters ---------- batch_size: int How many transitions to sample. Returns ------- obs_batch: np.array Array of shape (batch_size, img_h, img_w, img_c * frame_history_len) and dtype np.uint8 act_batch: np.array Array of shape (batch_size,) and dtype np.int32 rew_batch: np.array Array of shape (batch_size,) and dtype np.float32 next_obs_batch: np.array Array of shape (batch_size, img_h, img_w, img_c * frame_history_len) and dtype np.uint8 done_mask: np.array Array of shape (batch_size,) and dtype np.float32 """ assert self.can_sample(batch_size) idxes = sample_n_unique(lambda: random.randint(0, self.num_in_buffer - 2), batch_size) return self._encode_sample(idxes) def encode_recent_observation(self): """Return the most recent `frame_history_len` frames. Returns ------- observation: np.array Array of shape (img_h, img_w, img_c * frame_history_len) and dtype np.uint8, where observation[:, :, i*img_c:(i+1)*img_c] encodes frame at time `t - frame_history_len + i` """ assert self.num_in_buffer > 0 return self._encode_observation((self.next_idx - 1) % self.size) def _encode_observation(self, idx): end_idx = idx + 1 # make noninclusive start_idx = end_idx - self.frame_history_len # this checks if we are using low-dimensional observations, such as RAM # state, in which case we just directly return the latest RAM. if len(self.obs.shape) == 2: return self.obs[end_idx-1] # if there weren't enough frames ever in the buffer for context if start_idx < 0 and self.num_in_buffer != self.size: start_idx = 0 for idx in range(start_idx, end_idx - 1): if self.done[idx % self.size]: start_idx = idx + 1 missing_context = self.frame_history_len - (end_idx - start_idx) # if zero padding is needed for missing context # or we are on the boundry of the buffer if start_idx < 0 or missing_context > 0: frames = [np.zeros_like(self.obs[0]) for _ in range(missing_context)] for idx in range(start_idx, end_idx): frames.append(self.obs[idx % self.size]) return np.concatenate(frames, 2) else: # this optimization has potential to saves about 30% compute time \o/ img_h, img_w = self.obs.shape[1], self.obs.shape[2] return self.obs[start_idx:end_idx].transpose(1, 2, 0, 3).reshape(img_h, img_w, -1) def store_frame(self, frame): """Store a single frame in the buffer at the next available index, overwriting old frames if necessary. Parameters ---------- frame: np.array Array of shape (img_h, img_w, img_c) and dtype np.uint8 the frame to be stored Returns ------- idx: int Index at which the frame is stored. To be used for `store_effect` later. """ if self.obs is None: self.obs = np.empty([self.size] + list(frame.shape), dtype=np.uint8) self.action = np.empty([self.size], dtype=np.int32) self.reward = np.empty([self.size], dtype=np.float32) self.done = np.empty([self.size], dtype=np.bool) self.obs[self.next_idx] = frame ret = self.next_idx self.next_idx = (self.next_idx + 1) % self.size self.num_in_buffer = min(self.size, self.num_in_buffer + 1) return ret def store_effect(self, idx, action, reward, done): """Store effects of action taken after observing frame stored at index idx. The reason `store_frame` and `store_effect` is broken up into two functions is so that once can call `encode_recent_observation` in between. Paramters --------- idx: int Index in buffer of recently observed frame (returned by `store_frame`). action: int Action that was performed upon observing this frame. reward: float Reward that was received when the actions was performed. done: bool True if episode was finished after performing that action. """ self.action[idx] = action self.reward[idx] = reward self.done[idx] = done ================================================ FILE: dqn/logs_pkls/BeamRider_s001.pkl ================================================ (lp0 (I60000 cnumpy.core.multiarray scalar p1 (cnumpy dtype p2 (S'f8' p3 I0 I1 tp4 Rp5 (I3 S'<' p6 NNNI-1 I-1 I0 tp7 bS'8\xbd\xe9Mo\x1au@' p8 tp9 Rp10 F-inf F352.0 tp11 a(I70000 g1 (g5 S'"5\xc1x+;u@' p12 tp13 Rp14 F-inf F396.0 tp15 a(I80000 g1 (g5 S'\xef\xee\xee\xee\xee.u@' p16 tp17 Rp18 F-inf F308.0 tp19 a(I90000 g1 (g5 S'\xb5\xb4\xb4\xb4\xb4\xa4u@' p20 tp21 Rp22 F-inf F352.0 tp23 a(I100000 g1 (g5 S'\xfdb\xc9/\x96\xfcu@' p24 tp25 Rp26 F-inf F900.0 tp27 a(I110000 g1 (g5 S'\xa9]\x89\xda\x95(v@' p28 tp29 Rp30 F-inf F308.0 tp31 a(I120000 g1 (g5 S'\xde\xdd\xdd\xdd\xdd\xfdu@' p32 tp33 Rp34 F-inf F264.0 tp35 a(I130000 g1 (g5 S'\x99\x86\xedfy\xd2u@' p36 tp37 Rp38 F-inf F308.0 tp39 a(I140000 g1 (g5 S'q=\n\xd7\xa3\xb0u@' p40 tp41 Rp42 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xccu@' p43 tp44 Rp45 F264.0 tp46 a(I150000 g1 (g5 S'\x8f\xc2\xf5(\\\x7fu@' p47 tp48 Rp49 g45 F352.0 tp50 a(I160000 g1 (g5 S'=\n\xd7\xa3p\xcdu@' p51 tp52 Rp53 g1 (g5 S'q=\n\xd7\xa3\xf0u@' p54 tp55 Rp56 F352.0 tp57 a(I170000 g1 (g5 S'\xb8\x1e\x85\xebQhv@' p58 tp59 Rp60 g1 (g5 S'\xb8\x1e\x85\xebQhv@' p61 tp62 Rp63 F352.0 tp64 a(I180000 g1 (g5 S'H\xe1z\x14\xae\xa7v@' p65 tp66 Rp67 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xbcv@' p68 tp69 Rp70 F440.0 tp71 a(I190000 g1 (g5 S'\xb8\x1e\x85\xebQhv@' p72 tp73 Rp74 g70 F396.0 tp75 a(I200000 g1 (g5 S'\n\xd7\xa3p=\x8av@' p76 tp77 Rp78 g1 (g5 S')\\\x8f\xc2\xf5\xd8v@' p79 tp80 Rp81 F264.0 tp82 a(I210000 g1 (g5 S'{\x14\xaeG\xe1Jv@' p83 tp84 Rp85 g81 F176.0 tp86 a(I220000 g1 (g5 S'q=\n\xd7\xa3 v@' p87 tp88 Rp89 g81 F176.0 tp90 a(I230000 g1 (g5 S'\xe1z\x14\xaeGav@' p91 tp92 Rp93 g81 F264.0 tp94 a(I240000 g1 (g5 S"\xd7\xa3p=\n'v@" p95 tp96 Rp97 g81 F440.0 tp98 a(I250000 g1 (g5 S'33333Cv@' p99 tp100 Rp101 g81 F308.0 tp102 a(I260000 g1 (g5 S'\xa4p=\n\xd7\xb3v@' p103 tp104 Rp105 g81 F440.0 tp106 a(I270000 g1 (g5 S'\xa4p=\n\xd7\xb3v@' p107 tp108 Rp109 g1 (g5 S'\\\x8f\xc2\xf5(\xecv@' p110 tp111 Rp112 F352.0 tp113 a(I280000 g1 (g5 S'=\n\xd7\xa3p\x1dw@' p114 tp115 Rp116 g1 (g5 S'=\n\xd7\xa3p\x1dw@' p117 tp118 Rp119 F176.0 tp120 a(I290000 g1 (g5 S'\x00\x00\x00\x00\x00\x90v@' p121 tp122 Rp123 g119 F440.0 tp124 a(I300000 g1 (g5 S')\\\x8f\xc2\xf5\xd8u@' p125 tp126 Rp127 g119 F132.0 tp128 a(I310000 g1 (g5 S'\xaeG\xe1z\x14\xeeu@' p129 tp130 Rp131 g119 F484.0 tp132 a(I320000 g1 (g5 S'\x1f\x85\xebQ\xb8^v@' p133 tp134 Rp135 g119 F176.0 tp136 a(I330000 g1 (g5 S'\x85\xebQ\xb8\x1e\xf5u@' p137 tp138 Rp139 g119 F176.0 tp140 a(I340000 g1 (g5 S'\x14\xaeG\xe1z4v@' p141 tp142 Rp143 g119 F484.0 tp144 a(I350000 g1 (g5 S'fffff\xd6v@' p145 tp146 Rp147 g119 F220.0 tp148 a(I360000 g1 (g5 S'\x8f\xc2\xf5(\\\xcfv@' p149 tp150 Rp151 g119 F484.0 tp152 a(I370000 g1 (g5 S'\xf6(\\\x8f\xc2\x95v@' p153 tp154 Rp155 g119 F264.0 tp156 a(I380000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x8cv@' p157 tp158 Rp159 g119 F396.0 tp160 a(I390000 g1 (g5 S'\xd7\xa3p=\n\xb7v@' p161 tp162 Rp163 g119 F352.0 tp164 a(I400000 g1 (g5 S'q=\n\xd7\xa3pv@' p165 tp166 Rp167 g119 F440.0 tp168 a(I410000 g1 (g5 S'\xc3\xf5(\\\x8fbv@' p169 tp170 Rp171 g119 F572.0 tp172 a(I420000 g1 (g5 S'\xb8\x1e\x85\xebQ\xe8v@' p173 tp174 Rp175 g119 F352.0 tp176 a(I430000 g1 (g5 S'\x85\xebQ\xb8\x1euw@' p177 tp178 Rp179 g1 (g5 S'\x85\xebQ\xb8\x1euw@' p180 tp181 Rp182 F440.0 tp183 a(I440000 g1 (g5 S'\xa4p=\n\xd7\xc3w@' p184 tp185 Rp186 g1 (g5 S'\xa4p=\n\xd7\xc3w@' p187 tp188 Rp189 F756.0 tp190 a(I450000 g1 (g5 S'\x9a\x99\x99\x99\x99Ix@' p191 tp192 Rp193 g1 (g5 S'\xf6(\\\x8f\xc2ex@' p194 tp195 Rp196 F264.0 tp197 a(I460000 g1 (g5 S'\xaeG\xe1z\x14Nw@' p198 tp199 Rp200 g196 F88.0 tp201 a(I470000 g1 (g5 S'\xecQ\xb8\x1e\x85\x9bw@' p202 tp203 Rp204 g196 F660.0 tp205 a(I480000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xccw@' p206 tp207 Rp208 g196 F352.0 tp209 a(I490000 g1 (g5 S'\xb8\x1e\x85\xebQxw@' p210 tp211 Rp212 g196 F528.0 tp213 a(I500000 g1 (g5 S'33333cw@' p214 tp215 Rp216 g196 F308.0 tp217 a(I510000 g1 (g5 S'\xb8\x1e\x85\xebQxw@' p218 tp219 Rp220 g196 F220.0 tp221 a(I520000 g1 (g5 S'\xb8\x1e\x85\xebQxw@' p222 tp223 Rp224 g196 F352.0 tp225 a(I530000 g1 (g5 S'\x9a\x99\x99\x99\x99\xa9w@' p226 tp227 Rp228 g196 F396.0 tp229 a(I540000 g1 (g5 S'\xecQ\xb8\x1e\x85\x1bx@' p230 tp231 Rp232 g196 F308.0 tp233 a(I550000 g1 (g5 S'R\xb8\x1e\x85\xeb\xb1w@' p234 tp235 Rp236 g196 F176.0 tp237 a(I560000 g1 (g5 S'\xf6(\\\x8f\xc2\x95w@' p238 tp239 Rp240 g196 F484.0 tp241 a(I570000 g1 (g5 S'\x85\xebQ\xb8\x1e%w@' p242 tp243 Rp244 g196 F264.0 tp245 a(I580000 g1 (g5 S'\x14\xaeG\xe1z4v@' p246 tp247 Rp248 g196 F220.0 tp249 a(I590000 g1 (g5 S'=\n\xd7\xa3p=v@' p250 tp251 Rp252 g196 F528.0 tp253 a(I600000 g1 (g5 S'q=\n\xd7\xa3Pv@' p254 tp255 Rp256 g196 F88.0 tp257 a(I610000 g1 (g5 S'\x1f\x85\xebQ\xb8^v@' p258 tp259 Rp260 g196 F264.0 tp261 a(I620000 g1 (g5 S'\x1f\x85\xebQ\xb8^v@' p262 tp263 Rp264 g196 F308.0 tp265 a(I630000 g1 (g5 S'\xf6(\\\x8f\xc2ev@' p266 tp267 Rp268 g196 F396.0 tp269 a(I640000 g1 (g5 S'\xd7\xa3p=\n\xe7u@' p270 tp271 Rp272 g196 F308.0 tp273 a(I650000 g1 (g5 S'\x14\xaeG\xe1z\x84u@' p274 tp275 Rp276 g196 F264.0 tp277 a(I660000 g1 (g5 S'\x9a\x99\x99\x99\x99iu@' p278 tp279 Rp280 g196 F440.0 tp281 a(I670000 g1 (g5 S'=\n\xd7\xa3p\xcdt@' p282 tp283 Rp284 g196 F132.0 tp285 a(I680000 g1 (g5 S'\xc3\xf5(\\\x8f\xe2t@' p286 tp287 Rp288 g196 F44.0 tp289 a(I690000 g1 (g5 S'H\xe1z\x14\xae\xf7t@' p290 tp291 Rp292 g196 F352.0 tp293 a(I700000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x0cu@' p294 tp295 Rp296 g196 F220.0 tp297 a(I710000 g1 (g5 S'R\xb8\x1e\x85\xeb!u@' p298 tp299 Rp300 g196 F88.0 tp301 a(I720000 g1 (g5 S'H\xe1z\x14\xaewu@' p302 tp303 Rp304 g196 F220.0 tp305 a(I730000 g1 (g5 S'\x1f\x85\xebQ\xb8nu@' p306 tp307 Rp308 g196 F308.0 tp309 a(I740000 g1 (g5 S'{\x14\xaeG\xe1\x8au@' p310 tp311 Rp312 g196 F352.0 tp313 a(I750000 g1 (g5 S'\xc3\xf5(\\\x8fRu@' p314 tp315 Rp316 g196 F484.0 tp317 a(I760000 g1 (g5 S'\x9a\x99\x99\x99\x99Yu@' p318 tp319 Rp320 g196 F264.0 tp321 a(I770000 g1 (g5 S'\x00\x00\x00\x00\x00\xa0u@' p322 tp323 Rp324 g196 F440.0 tp325 a(I780000 g1 (g5 S'\xa4p=\n\xd73v@' p326 tp327 Rp328 g196 F264.0 tp329 a(I790000 g1 (g5 S'\x85\xebQ\xb8\x1eev@' p330 tp331 Rp332 g196 F660.0 tp333 a(I800000 g1 (g5 S'\xf6(\\\x8f\xc2Uv@' p334 tp335 Rp336 g196 F264.0 tp337 a(I810000 g1 (g5 S'\x1f\x85\xebQ\xb8Nv@' p338 tp339 Rp340 g196 F220.0 tp341 a(I820000 g1 (g5 S'\xd7\xa3p=\n\xd7u@' p342 tp343 Rp344 g196 F220.0 tp345 a(I830000 g1 (g5 S'=\n\xd7\xa3p]v@' p346 tp347 Rp348 g196 F308.0 tp349 a(I840000 g1 (g5 S')\\\x8f\xc2\xf5\xb8v@' p350 tp351 Rp352 g196 F616.0 tp353 a(I850000 g1 (g5 S'\\\x8f\xc2\xf5(\xdcv@' p354 tp355 Rp356 g196 F220.0 tp357 a(I860000 g1 (g5 S'q=\n\xd7\xa3\xb0v@' p358 tp359 Rp360 g196 F176.0 tp361 a(I870000 g1 (g5 S'\x8f\xc2\xf5(\\\x7fv@' p362 tp363 Rp364 g196 F220.0 tp365 a(I880000 g1 (g5 S'\x1f\x85\xebQ\xb8\x0ev@' p366 tp367 Rp368 g196 F352.0 tp369 a(I890000 g1 (g5 S'\xd7\xa3p=\n\x97u@' p370 tp371 Rp372 g196 F264.0 tp373 a(I900000 g1 (g5 S'\xecQ\xb8\x1e\x85Ku@' p374 tp375 Rp376 g196 F804.0 tp377 a(I910000 g1 (g5 S'\xb8\x1e\x85\xebQ\xd8u@' p378 tp379 Rp380 g196 F660.0 tp381 a(I920000 g1 (g5 S'\xe1z\x14\xaeG\xd1u@' p382 tp383 Rp384 g196 F88.0 tp385 a(I930000 g1 (g5 S'\xc3\xf5(\\\x8fRu@' p386 tp387 Rp388 g196 F176.0 tp389 a(I940000 g1 (g5 S'\xe1z\x14\xaeG\xd1u@' p390 tp391 Rp392 g196 F616.0 tp393 a(I950000 g1 (g5 S'\xc3\xf5(\\\x8fRu@' p394 tp395 Rp396 g196 F484.0 tp397 a(I960000 g1 (g5 S'\n\xd7\xa3p=\x1au@' p398 tp399 Rp400 g196 F484.0 tp401 a(I970000 g1 (g5 S'\\\x8f\xc2\xf5(\xbcu@' p402 tp403 Rp404 g196 F660.0 tp405 a(I980000 g1 (g5 S'\xecQ\xb8\x1e\x85\xabv@' p406 tp407 Rp408 g196 F572.0 tp409 a(I990000 g1 (g5 S'\x8f\xc2\xf5(\\Ov@' p410 tp411 Rp412 g196 F220.0 tp413 a(I1000000 g1 (g5 S'\xf6(\\\x8f\xc2\xe5u@' p414 tp415 Rp416 g196 F440.0 tp417 a(I1010000 g1 (g5 S'R\xb8\x1e\x85\xebQu@' p418 tp419 Rp420 g196 F264.0 tp421 a(I1020000 g1 (g5 S'R\xb8\x1e\x85\xebQu@' p422 tp423 Rp424 g196 F440.0 tp425 a(I1030000 g1 (g5 S'\xa4p=\n\xd7Cu@' p426 tp427 Rp428 g196 F616.0 tp429 a(I1040000 g1 (g5 S'33333\x03v@' p430 tp431 Rp432 g196 F220.0 tp433 a(I1050000 g1 (g5 S'\xecQ\xb8\x1e\x85;v@' p434 tp435 Rp436 g196 F528.0 tp437 a(I1060000 g1 (g5 S'\x1f\x85\xebQ\xb8^v@' p438 tp439 Rp440 g196 F44.0 tp441 a(I1070000 g1 (g5 S'\xaeG\xe1z\x14.u@' p442 tp443 Rp444 g196 F176.0 tp445 a(I1080000 g1 (g5 S'\xf6(\\\x8f\xc2\xf5t@' p446 tp447 Rp448 g196 F572.0 tp449 a(I1090000 g1 (g5 S'\x1f\x85\xebQ\xb8\xeet@' p450 tp451 Rp452 g196 F440.0 tp453 a(I1100000 g1 (g5 S'\x8f\xc2\xf5(\\\xaft@' p454 tp455 Rp456 g196 F352.0 tp457 a(I1110000 g1 (g5 S"\xd7\xa3p=\n'u@" p458 tp459 Rp460 g196 F88.0 tp461 a(I1120000 g1 (g5 S'\xd7\xa3p=\nwt@' p462 tp463 Rp464 g196 F352.0 tp465 a(I1130000 g1 (g5 S'\xcd\xcc\xcc\xcc\xccLt@' p466 tp467 Rp468 g196 F440.0 tp469 a(I1140000 g1 (g5 S'\x8f\xc2\xf5(\\\xaft@' p470 tp471 Rp472 g196 F308.0 tp473 a(I1150000 g1 (g5 S'\xb8\x1e\x85\xebQXu@' p474 tp475 Rp476 g196 F528.0 tp477 a(I1160000 g1 (g5 S'=\n\xd7\xa3pmu@' p478 tp479 Rp480 g196 F352.0 tp481 a(I1170000 g1 (g5 S'H\xe1z\x14\xae\x97u@' p482 tp483 Rp484 g196 F220.0 tp485 a(I1180000 g1 (g5 S'\\\x8f\xc2\xf5(\xecu@' p486 tp487 Rp488 g196 F176.0 tp489 a(I1190000 g1 (g5 S'\x1f\x85\xebQ\xb8\x1eu@' p490 tp491 Rp492 g196 F220.0 tp493 a(I1200000 g1 (g5 S'\x14\xaeG\xe1z\xf4t@' p494 tp495 Rp496 g196 F484.0 tp497 a(I1210000 g1 (g5 S'\x00\x00\x00\x00\x00\xa0t@' p498 tp499 Rp500 g196 F220.0 tp501 a(I1220000 g1 (g5 S'\x85\xebQ\xb8\x1e\xb5t@' p502 tp503 Rp504 g196 F440.0 tp505 a(I1230000 g1 (g5 S'\x00\x00\x00\x00\x00Pu@' p506 tp507 Rp508 g196 F264.0 tp509 a(I1240000 g1 (g5 S'\xaeG\xe1z\x14^u@' p510 tp511 Rp512 g196 F660.0 tp513 a(I1250000 g1 (g5 S'\x1f\x85\xebQ\xb8\x1eu@' p514 tp515 Rp516 g196 F660.0 tp517 a(I1260000 g1 (g5 S')\\\x8f\xc2\xf5Hu@' p518 tp519 Rp520 g196 F308.0 tp521 a(I1270000 g1 (g5 S'\\\x8f\xc2\xf5(lu@' p522 tp523 Rp524 g196 F396.0 tp525 a(I1280000 g1 (g5 S')\\\x8f\xc2\xf5\x98t@' p526 tp527 Rp528 g196 F132.0 tp529 a(I1290000 g1 (g5 S'\xecQ\xb8\x1e\x85\x8bt@' p530 tp531 Rp532 g196 F708.0 tp533 a(I1300000 g1 (g5 S'\x00\x00\x00\x00\x000t@' p534 tp535 Rp536 g196 F88.0 tp537 a(I1310000 g1 (g5 S'\xe1z\x14\xaeG\xb1s@' p538 tp539 Rp540 g196 F352.0 tp541 a(I1320000 g1 (g5 S'{\x14\xaeG\xe1\x1at@' p542 tp543 Rp544 g196 F176.0 tp545 a(I1330000 g1 (g5 S'\x85\xebQ\xb8\x1eEt@' p546 tp547 Rp548 g196 F396.0 tp549 a(I1340000 g1 (g5 S'{\x14\xaeG\xe1\xcat@' p550 tp551 Rp552 g196 F484.0 tp553 a(I1350000 g1 (g5 S'\\\x8f\xc2\xf5(\xfct@' p554 tp555 Rp556 g196 F352.0 tp557 a(I1360000 g1 (g5 S'\x1f\x85\xebQ\xb8^u@' p558 tp559 Rp560 g196 F440.0 tp561 a(I1370000 g1 (g5 S'fffff&u@' p562 tp563 Rp564 g196 F440.0 tp565 a(I1380000 g1 (g5 S'{\x14\xaeG\xe1\xcat@' p566 tp567 Rp568 g196 F132.0 tp569 a(I1390000 g1 (g5 S'\n\xd7\xa3p=Zt@' p570 tp571 Rp572 g196 F308.0 tp573 a(I1400000 g1 (g5 S'\xe1z\x14\xaeGat@' p574 tp575 Rp576 g196 F396.0 tp577 a(I1410000 g1 (g5 S'\x14\xaeG\xe1z\x84t@' p578 tp579 Rp580 g196 F132.0 tp581 a(I1420000 g1 (g5 S'33333St@' p582 tp583 Rp584 g196 F308.0 tp585 a(I1430000 g1 (g5 S'\x8f\xc2\xf5(\\ot@' p586 tp587 Rp588 g196 F308.0 tp589 a(I1440000 g1 (g5 S'33333\xc3t@' p590 tp591 Rp592 g196 F616.0 tp593 a(I1450000 g1 (g5 S'\x14\xaeG\xe1z\xa4u@' p594 tp595 Rp596 g196 F660.0 tp597 a(I1460000 g1 (g5 S'\x1f\x85\xebQ\xb8\xceu@' p598 tp599 Rp600 g196 F132.0 tp601 a(I1470000 g1 (g5 S'{\x14\xaeG\xe1:u@' p602 tp603 Rp604 g196 F132.0 tp605 a(I1480000 g1 (g5 S'33333su@' p606 tp607 Rp608 g196 F528.0 tp609 a(I1490000 g1 (g5 S'\xf6(\\\x8f\xc2%u@' p610 tp611 Rp612 g196 F352.0 tp613 a(I1500000 g1 (g5 S'\\\x8f\xc2\xf5(lu@' p614 tp615 Rp616 g196 F220.0 tp617 a(I1510000 g1 (g5 S'\xb8\x1e\x85\xebQ\x88u@' p618 tp619 Rp620 g196 F176.0 tp621 a(I1520000 g1 (g5 S'H\xe1z\x14\xae\xc7u@' p622 tp623 Rp624 g196 F352.0 tp625 a(I1530000 g1 (g5 S'{\x14\xaeG\xe1:u@' p626 tp627 Rp628 g196 F220.0 tp629 a(I1540000 g1 (g5 S'\x1f\x85\xebQ\xb8\xceu@' p630 tp631 Rp632 g196 F176.0 tp633 a(I1550000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xdcu@' p634 tp635 Rp636 g196 F264.0 tp637 a(I1560000 g1 (g5 S'\xd7\xa3p=\nWu@' p638 tp639 Rp640 g196 F308.0 tp641 a(I1570000 g1 (g5 S'fffff\x96u@' p642 tp643 Rp644 g196 F308.0 tp645 a(I1580000 g1 (g5 S'33333su@' p646 tp647 Rp648 g196 F528.0 tp649 a(I1590000 g1 (g5 S'\x14\xaeG\xe1z\xf4t@' p650 tp651 Rp652 g196 F264.0 tp653 a(I1600000 g1 (g5 S'\x1f\x85\xebQ\xb8\xbes@' p654 tp655 Rp656 g196 F264.0 tp657 a(I1610000 g1 (g5 S'\x00\x00\x00\x00\x00\xf0s@' p658 tp659 Rp660 g196 F440.0 tp661 a(I1620000 g1 (g5 S'\xb8\x1e\x85\xebQ(t@' p662 tp663 Rp664 g196 F396.0 tp665 a(I1630000 g1 (g5 S'\x9a\x99\x99\x99\x99Yt@' p666 tp667 Rp668 g196 F396.0 tp669 a(I1640000 g1 (g5 S'\n\xd7\xa3p=\x1at@' p670 tp671 Rp672 g196 F220.0 tp673 a(I1650000 g1 (g5 S'H\xe1z\x14\xae\xb7s@' p674 tp675 Rp676 g196 F220.0 tp677 a(I1660000 g1 (g5 S'\x8f\xc2\xf5(\\\x7fs@' p678 tp679 Rp680 g196 F308.0 tp681 a(I1670000 g1 (g5 S'R\xb8\x1e\x85\xeb1s@' p682 tp683 Rp684 g196 F176.0 tp685 a(I1680000 g1 (g5 S'\xb8\x1e\x85\xebQxs@' p686 tp687 Rp688 g196 F176.0 tp689 a(I1690000 g1 (g5 S'fffff\x86s@' p690 tp691 Rp692 g196 F352.0 tp693 a(I1700000 g1 (g5 S'=\n\xd7\xa3p\x8ds@' p694 tp695 Rp696 g196 F352.0 tp697 a(I1710000 g1 (g5 S'\xb8\x1e\x85\xebQxs@' p698 tp699 Rp700 g196 F220.0 tp701 a(I1720000 g1 (g5 S'H\xe1z\x14\xae\xb7s@' p702 tp703 Rp704 g196 F176.0 tp705 a(I1730000 g1 (g5 S'\xe1z\x14\xaeGqs@' p706 tp707 Rp708 g196 F220.0 tp709 a(I1740000 g1 (g5 S'\x9a\x99\x99\x99\x99\xa9s@' p710 tp711 Rp712 g196 F616.0 tp713 a(I1750000 g1 (g5 S'\xa4p=\n\xd7#s@' p714 tp715 Rp716 g196 F440.0 tp717 a(I1760000 g1 (g5 S'q=\n\xd7\xa3\x00s@' p718 tp719 Rp720 g196 F484.0 tp721 a(I1770000 g1 (g5 S'\xe1z\x14\xaeG\xc1r@' p722 tp723 Rp724 g196 F220.0 tp725 a(I1780000 g1 (g5 S'\x9a\x99\x99\x99\x99\xf9r@' p726 tp727 Rp728 g196 F88.0 tp729 a(I1790000 g1 (g5 S'=\n\xd7\xa3p\xddr@' p730 tp731 Rp732 g196 F132.0 tp733 a(I1800000 g1 (g5 S'\xaeG\xe1z\x14Ns@' p734 tp735 Rp736 g196 F572.0 tp737 a(I1810000 g1 (g5 S'\n\xd7\xa3p=\xbar@' p738 tp739 Rp740 g196 F220.0 tp741 a(I1820000 g1 (g5 S'\x1f\x85\xebQ\xb8^r@' p742 tp743 Rp744 g196 F176.0 tp745 a(I1830000 g1 (g5 S'\x9a\x99\x99\x99\x99Ir@' p746 tp747 Rp748 g196 F572.0 tp749 a(I1840000 g1 (g5 S'\\\x8f\xc2\xf5(\xfcq@' p750 tp751 Rp752 g196 F484.0 tp753 a(I1850000 g1 (g5 S'\n\xd7\xa3p=\nr@' p754 tp755 Rp756 g196 F396.0 tp757 a(I1860000 g1 (g5 S'\xa4p=\n\xd7\xc3q@' p758 tp759 Rp760 g196 F88.0 tp761 a(I1870000 g1 (g5 S'\xb8\x1e\x85\xebQ\x18r@' p762 tp763 Rp764 g196 F308.0 tp765 a(I1880000 g1 (g5 S'\xe1z\x14\xaeG\x11r@' p766 tp767 Rp768 g196 F352.0 tp769 a(I1890000 g1 (g5 S'\xc3\xf5(\\\x8fBr@' p770 tp771 Rp772 g196 F132.0 tp773 a(I1900000 g1 (g5 S'\xd7\xa3p=\n\x97r@' p774 tp775 Rp776 g196 F660.0 tp777 a(I1910000 g1 (g5 S'\xf6(\\\x8f\xc2er@' p778 tp779 Rp780 g196 F176.0 tp781 a(I1920000 g1 (g5 S'\xecQ\xb8\x1e\x85;r@' p782 tp783 Rp784 g196 F308.0 tp785 a(I1930000 g1 (g5 S'\x14\xaeG\xe1z4r@' p786 tp787 Rp788 g196 F484.0 tp789 a(I1940000 g1 (g5 S'\\\x8f\xc2\xf5(\xacr@' p790 tp791 Rp792 g196 F396.0 tp793 a(I1950000 g1 (g5 S'\xecQ\xb8\x1e\x85;r@' p794 tp795 Rp796 g196 F264.0 tp797 a(I1960000 g1 (g5 S'R\xb8\x1e\x85\xeb1s@' p798 tp799 Rp800 g196 F484.0 tp801 a(I1970000 g1 (g5 S'\xc3\xf5(\\\x8f\xa2s@' p802 tp803 Rp804 g196 F528.0 tp805 a(I1980000 g1 (g5 S'\n\xd7\xa3p=js@' p806 tp807 Rp808 g196 F396.0 tp809 a(I1990000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x1cs@' p810 tp811 Rp812 g196 F176.0 tp813 a(I2000000 g1 (g5 S'\x85\xebQ\xb8\x1e\xa5r@' p814 tp815 Rp816 g196 F308.0 tp817 a(I2010000 g1 (g5 S'\xa4p=\n\xd7sr@' p818 tp819 Rp820 g196 F308.0 tp821 a(I2020000 g1 (g5 S'\x9a\x99\x99\x99\x99Ir@' p822 tp823 Rp824 g196 F176.0 tp825 a(I2030000 g1 (g5 S'\x00\x00\x00\x00\x00\xe0q@' p826 tp827 Rp828 g196 F440.0 tp829 a(I2040000 g1 (g5 S'q=\n\xd7\xa3\xa0q@' p830 tp831 Rp832 g196 F352.0 tp833 a(I2050000 g1 (g5 S'{\x14\xaeG\xe1\xcaq@' p834 tp835 Rp836 g196 F352.0 tp837 a(I2060000 g1 (g5 S'=\n\xd7\xa3p-r@' p838 tp839 Rp840 g196 F264.0 tp841 a(I2070000 g1 (g5 S'=\n\xd7\xa3p-r@' p842 tp843 Rp844 g196 F220.0 tp845 a(I2080000 g1 (g5 S'fffff&r@' p846 tp847 Rp848 g196 F308.0 tp849 a(I2090000 g1 (g5 S'\x14\xaeG\xe1z4r@' p850 tp851 Rp852 g196 F220.0 tp853 a(I2100000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99q@' p854 tp855 Rp856 g196 F132.0 tp857 a(I2110000 g1 (g5 S'\x1f\x85\xebQ\xb8\xfep@' p858 tp859 Rp860 g196 F352.0 tp861 a(I2120000 g1 (g5 S'\x85\xebQ\xb8\x1eEq@' p862 tp863 Rp864 g196 F528.0 tp865 a(I2130000 g1 (g5 S'\xb8\x1e\x85\xebQ\x18r@' p866 tp867 Rp868 g196 F484.0 tp869 a(I2140000 g1 (g5 S'\x00\x00\x00\x00\x00\xe0q@' p870 tp871 Rp872 g196 F88.0 tp873 a(I2150000 g1 (g5 S'\n\xd7\xa3p=\nr@' p874 tp875 Rp876 g196 F176.0 tp877 a(I2160000 g1 (g5 S'\x8f\xc2\xf5(\\\x1fr@' p878 tp879 Rp880 g196 F176.0 tp881 a(I2170000 g1 (g5 S'fffff&r@' p882 tp883 Rp884 g196 F308.0 tp885 a(I2180000 g1 (g5 S')\\\x8f\xc2\xf5\xd8q@' p886 tp887 Rp888 g196 F88.0 tp889 a(I2190000 g1 (g5 S')\\\x8f\xc2\xf5\xd8q@' p890 tp891 Rp892 g196 F264.0 tp893 a(I2200000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x0cq@' p894 tp895 Rp896 g196 F88.0 tp897 a(I2210000 g1 (g5 S'=\n\xd7\xa3p\xcdp@' p898 tp899 Rp900 g196 F220.0 tp901 a(I2220000 g1 (g5 S'\x14\xaeG\xe1z\xd4p@' p902 tp903 Rp904 g196 F88.0 tp905 a(I2230000 g1 (g5 S'\x85\xebQ\xb8\x1e\x95p@' p906 tp907 Rp908 g196 F396.0 tp909 a(I2240000 g1 (g5 S'\xc3\xf5(\\\x8f\xe2p@' p910 tp911 Rp912 g196 F176.0 tp913 a(I2250000 g1 (g5 S'\\\x8f\xc2\xf5(Lq@' p914 tp915 Rp916 g196 F396.0 tp917 a(I2260000 g1 (g5 S'R\xb8\x1e\x85\xeb!q@' p918 tp919 Rp920 g196 F132.0 tp921 a(I2270000 g1 (g5 S'\n\xd7\xa3p=\xaap@' p922 tp923 Rp924 g196 F132.0 tp925 a(I2280000 g1 (g5 S'\x00\x00\x00\x00\x000q@' p926 tp927 Rp928 g196 F308.0 tp929 a(I2290000 g1 (g5 S'\xaeG\xe1z\x14>q@' p930 tp931 Rp932 g196 F308.0 tp933 a(I2300000 g1 (g5 S'\x14\xaeG\xe1z\x84q@' p934 tp935 Rp936 g196 F132.0 tp937 a(I2310000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99q@' p938 tp939 Rp940 g196 F264.0 tp941 a(I2320000 g1 (g5 S'\x8f\xc2\xf5(\\oq@' p942 tp943 Rp944 g196 F44.0 tp945 a(I2330000 g1 (g5 S'\xa4p=\n\xd7\x13q@' p946 tp947 Rp948 g196 F0.0 tp949 a(I2340000 g1 (g5 S'\xa4p=\n\xd7cp@' p950 tp951 Rp952 g196 F220.0 tp953 a(I2350000 g1 (g5 S'\x1f\x85\xebQ\xb8Np@' p954 tp955 Rp956 g196 F88.0 tp957 a(I2360000 g1 (g5 S'\xd7\xa3p=\n\x87p@' p958 tp959 Rp960 g196 F132.0 tp961 a(I2370000 g1 (g5 S'\xaeG\xe1z\x14>q@' p962 tp963 Rp964 g196 F308.0 tp965 a(I2380000 g1 (g5 S'\\\x8f\xc2\xf5(Lq@' p966 tp967 Rp968 g196 F484.0 tp969 a(I2390000 g1 (g5 S'\x9a\x99\x99\x99\x99\xe9p@' p970 tp971 Rp972 g196 F264.0 tp973 a(I2400000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x0cq@' p974 tp975 Rp976 g196 F484.0 tp977 a(I2410000 g1 (g5 S'R\xb8\x1e\x85\xeb!q@' p978 tp979 Rp980 g196 F264.0 tp981 a(I2420000 g1 (g5 S'\x9a\x99\x99\x99\x99\xe9p@' p982 tp983 Rp984 g196 F88.0 tp985 a(I2430000 g1 (g5 S'\x14\xaeG\xe1z\xd4p@' p986 tp987 Rp988 g196 F132.0 tp989 a(I2440000 g1 (g5 S')\\\x8f\xc2\xf5xp@' p990 tp991 Rp992 g196 F220.0 tp993 a(I2450000 g1 (g5 S'\\\x8f\xc2\xf5(\x9cp@' p994 tp995 Rp996 g196 F220.0 tp997 a(I2460000 g1 (g5 S'\x00\x00\x00\x00\x000q@' p998 tp999 Rp1000 g196 F528.0 tp1001 a(I2470000 g1 (g5 S'fffffvq@' p1002 tp1003 Rp1004 g196 F220.0 tp1005 a(I2480000 g1 (g5 S'\xecQ\xb8\x1e\x85;r@' p1006 tp1007 Rp1008 g196 F176.0 tp1009 a(I2490000 g1 (g5 S'=\n\xd7\xa3p\xddr@' p1010 tp1011 Rp1012 g196 F308.0 tp1013 a(I2500000 g1 (g5 S'\n\xd7\xa3p=js@' p1014 tp1015 Rp1016 g196 F660.0 tp1017 a(I2510000 g1 (g5 S'\x9a\x99\x99\x99\x99\xf9r@' p1018 tp1019 Rp1020 g196 F572.0 tp1021 a(I2520000 g1 (g5 S'\xe1z\x14\xaeG\xc1r@' p1022 tp1023 Rp1024 g196 F220.0 tp1025 a(I2530000 g1 (g5 S'\\\x8f\xc2\xf5(\\s@' p1026 tp1027 Rp1028 g196 F616.0 tp1029 a(I2540000 g1 (g5 S'\xc3\xf5(\\\x8f\xa2s@' p1030 tp1031 Rp1032 g196 F396.0 tp1033 a(I2550000 g1 (g5 S'fffff6t@' p1034 tp1035 Rp1036 g196 F528.0 tp1037 a(I2560000 g1 (g5 S'R\xb8\x1e\x85\xeb\xe1t@' p1038 tp1039 Rp1040 g196 F352.0 tp1041 a(I2570000 g1 (g5 S'\x9a\x99\x99\x99\x99Yu@' p1042 tp1043 Rp1044 g196 F308.0 tp1045 a(I2580000 g1 (g5 S'\xb8\x1e\x85\xebQ\xd8u@' p1046 tp1047 Rp1048 g196 F308.0 tp1049 a(I2590000 g1 (g5 S'\n\xd7\xa3p=\xcau@' p1050 tp1051 Rp1052 g196 F264.0 tp1053 a(I2600000 g1 (g5 S'\x9a\x99\x99\x99\x99\tv@' p1054 tp1055 Rp1056 g196 F396.0 tp1057 a(I2610000 g1 (g5 S'\xe1z\x14\xaeG\x81v@' p1058 tp1059 Rp1060 g196 F308.0 tp1061 a(I2620000 g1 (g5 S'{\x14\xaeG\xe1:v@' p1062 tp1063 Rp1064 g196 F264.0 tp1065 a(I2630000 g1 (g5 S'\x85\xebQ\xb8\x1e\x15w@' p1066 tp1067 Rp1068 g196 F616.0 tp1069 a(I2640000 g1 (g5 S')\\\x8f\xc2\xf58w@' p1070 tp1071 Rp1072 g196 F440.0 tp1073 a(I2650000 g1 (g5 S'{\x14\xaeG\xe1:x@' p1074 tp1075 Rp1076 g196 F572.0 tp1077 a(I2660000 g1 (g5 S'\x9a\x99\x99\x99\x99Ix@' p1078 tp1079 Rp1080 g196 F132.0 tp1081 a(I2670000 g1 (g5 S'\x1f\x85\xebQ\xb8\x9ex@' p1082 tp1083 Rp1084 g1 (g5 S'\x85\xebQ\xb8\x1e\xe5x@' p1085 tp1086 Rp1087 F616.0 tp1088 a(I2680000 g1 (g5 S'\xecQ\xb8\x1e\x85+y@' p1089 tp1090 Rp1091 g1 (g5 S'H\xe1z\x14\xaeGy@' p1092 tp1093 Rp1094 F308.0 tp1095 a(I2690000 g1 (g5 S'R\xb8\x1e\x85\xeb\xc1x@' p1096 tp1097 Rp1098 g1094 F132.0 tp1099 a(I2700000 g1 (g5 S'\xe1z\x14\xaeG\x11y@' p1100 tp1101 Rp1102 g1094 F308.0 tp1103 a(I2710000 g1 (g5 S'\xa4p=\n\xd7\xc3x@' p1104 tp1105 Rp1106 g1094 F352.0 tp1107 a(I2720000 g1 (g5 S'\x14\xaeG\xe1z\xe4x@' p1108 tp1109 Rp1110 g1094 F660.0 tp1111 a(I2730000 g1 (g5 S'fffff\xd6x@' p1112 tp1113 Rp1114 g1094 F396.0 tp1115 a(I2740000 g1 (g5 S'\xc3\xf5(\\\x8f\xf2x@' p1116 tp1117 Rp1118 g1094 F572.0 tp1119 a(I2750000 g1 (g5 S'R\xb8\x1e\x85\xeb1y@' p1120 tp1121 Rp1122 g1 (g5 S'\\\x8f\xc2\xf5(\\y@' p1123 tp1124 Rp1125 F484.0 tp1126 a(I2760000 g1 (g5 S'\xd7\xa3p=\n\xd7y@' p1127 tp1128 Rp1129 g1 (g5 S'\x85\xebQ\xb8\x1e\xe5y@' p1130 tp1131 Rp1132 F308.0 tp1133 a(I2770000 g1 (g5 S'\x85\xebQ\xb8\x1e\xe5y@' p1134 tp1135 Rp1136 g1 (g5 S'\x8f\xc2\xf5(\\\x0fz@' p1137 tp1138 Rp1139 F220.0 tp1140 a(I2780000 g1 (g5 S'\x1f\x85\xebQ\xb8Nz@' p1141 tp1142 Rp1143 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\\z@' p1144 tp1145 Rp1146 F264.0 tp1147 a(I2790000 g1 (g5 S'\xe1z\x14\xaeG\xb1z@' p1148 tp1149 Rp1150 g1 (g5 S'\xecQ\xb8\x1e\x85\xdbz@' p1151 tp1152 Rp1153 F396.0 tp1154 a(I2800000 g1 (g5 S'\xb8\x1e\x85\xebQxz@' p1155 tp1156 Rp1157 g1153 F484.0 tp1158 a(I2810000 g1 (g5 S'\xa4p=\n\xd7cz@' p1159 tp1160 Rp1161 g1153 F572.0 tp1162 a(I2820000 g1 (g5 S')\\\x8f\xc2\xf5\x18z@' p1163 tp1164 Rp1165 g1153 F396.0 tp1166 a(I2830000 g1 (g5 S'=\n\xd7\xa3p-z@' p1167 tp1168 Rp1169 g1153 F660.0 tp1170 a(I2840000 g1 (g5 S'H\xe1z\x14\xaeWz@' p1171 tp1172 Rp1173 g1153 F660.0 tp1174 a(I2850000 g1 (g5 S'\\\x8f\xc2\xf5(|z@' p1175 tp1176 Rp1177 g1153 F88.0 tp1178 a(I2860000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc|y@' p1179 tp1180 Rp1181 g1153 F132.0 tp1182 a(I2870000 g1 (g5 S'\x9a\x99\x99\x99\x99\xa9x@' p1183 tp1184 Rp1185 g1153 F88.0 tp1186 a(I2880000 g1 (g5 S'\xa4p=\n\xd7#x@' p1187 tp1188 Rp1189 g1153 F264.0 tp1190 a(I2890000 g1 (g5 S'\xd7\xa3p=\n\x97w@' p1191 tp1192 Rp1193 g1153 F308.0 tp1194 a(I2900000 g1 (g5 S')\\\x8f\xc2\xf5\x88w@' p1195 tp1196 Rp1197 g1153 F264.0 tp1198 a(I2910000 g1 (g5 S'\xd7\xa3p=\nGx@' p1199 tp1200 Rp1201 g1153 F308.0 tp1202 a(I2920000 g1 (g5 S'33333\xd3w@' p1203 tp1204 Rp1205 g1153 F352.0 tp1206 a(I2930000 g1 (g5 S'R\xb8\x1e\x85\xeb\xa1w@' p1207 tp1208 Rp1209 g1153 F660.0 tp1210 a(I2940000 g1 (g5 S'33333#w@' p1211 tp1212 Rp1213 g1153 F352.0 tp1214 a(I2950000 g1 (g5 S'\x85\xebQ\xb8\x1e\x15w@' p1215 tp1216 Rp1217 g1153 F352.0 tp1218 a(I2960000 g1 (g5 S'{\x14\xaeG\xe1\x9aw@' p1219 tp1220 Rp1221 g1153 F308.0 tp1222 a(I2970000 g1 (g5 S'\xcd\xcc\xcc\xcc\xccLw@' p1223 tp1224 Rp1225 g1153 F528.0 tp1226 a(I2980000 g1 (g5 S'\\\x8f\xc2\xf5(\xdcv@' p1227 tp1228 Rp1229 g1153 F440.0 tp1230 a(I2990000 g1 (g5 S'\x1f\x85\xebQ\xb8\xcev@' p1231 tp1232 Rp1233 g1153 F572.0 tp1234 a(I3000000 g1 (g5 S'\xf6(\\\x8f\xc2Uv@' p1235 tp1236 Rp1237 g1153 F264.0 tp1238 a(I3010000 g1 (g5 S'fffffvw@' p1239 tp1240 Rp1241 g1153 F440.0 tp1242 a(I3020000 g1 (g5 S'\x8f\xc2\xf5(\\\x1fx@' p1243 tp1244 Rp1245 g1153 F264.0 tp1246 a(I3030000 g1 (g5 S'q=\n\xd7\xa3\x00y@' p1247 tp1248 Rp1249 g1153 F528.0 tp1250 a(I3040000 g1 (g5 S')\\\x8f\xc2\xf5xy@' p1251 tp1252 Rp1253 g1153 F352.0 tp1254 a(I3050000 g1 (g5 S'\xe1z\x14\xaeGaz@' p1255 tp1256 Rp1257 g1153 F660.0 tp1258 a(I3060000 g1 (g5 S'\xaeG\xe1z\x14\xeez@' p1259 tp1260 Rp1261 g1 (g5 S'33333\x03{@' p1262 tp1263 Rp1264 F264.0 tp1265 a(I3070000 g1 (g5 S'\xb8\x1e\x85\xebQ\x18{@' p1266 tp1267 Rp1268 g1 (g5 S'\x9a\x99\x99\x99\x99I{@' p1269 tp1270 Rp1271 F484.0 tp1272 a(I3080000 g1 (g5 S'=\n\xd7\xa3p}z@' p1273 tp1274 Rp1275 g1271 F396.0 tp1276 a(I3090000 g1 (g5 S'q=\n\xd7\xa3P{@' p1277 tp1278 Rp1279 g1 (g5 S'q=\n\xd7\xa3P{@' p1280 tp1281 Rp1282 F660.0 tp1283 a(I3100000 g1 (g5 S'\xecQ\xb8\x1e\x85\xfb{@' p1284 tp1285 Rp1286 g1 (g5 S'H\xe1z\x14\xae\x17|@' p1287 tp1288 Rp1289 F396.0 tp1290 a(I3110000 g1 (g5 S'\\\x8f\xc2\xf5(\xac|@' p1291 tp1292 Rp1293 g1 (g5 S'\\\x8f\xc2\xf5(\xac|@' p1294 tp1295 Rp1296 F352.0 tp1297 a(I3120000 g1 (g5 S'33333c}@' p1298 tp1299 Rp1300 g1 (g5 S'33333c}@' p1301 tp1302 Rp1303 F616.0 tp1304 a(I3130000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xec|@' p1305 tp1306 Rp1307 g1303 F756.0 tp1308 a(I3140000 g1 (g5 S'H\xe1z\x14\xae\x97}@' p1309 tp1310 Rp1311 g1 (g5 S'H\xe1z\x14\xae\x97}@' p1312 tp1313 Rp1314 F804.0 tp1315 a(I3150000 g1 (g5 S'\x85\xebQ\xb8\x1eE~@' p1316 tp1317 Rp1318 g1 (g5 S'\x85\xebQ\xb8\x1eE~@' p1319 tp1320 Rp1321 F616.0 tp1322 a(I3160000 g1 (g5 S'\\\x8f\xc2\xf5(\xcc~@' p1323 tp1324 Rp1325 g1 (g5 S'\\\x8f\xc2\xf5(\xcc~@' p1326 tp1327 Rp1328 F528.0 tp1329 a(I3170000 g1 (g5 S'\xf6(\\\x8f\xc2\xf5~@' p1330 tp1331 Rp1332 g1 (g5 S'=\n\xd7\xa3p\xfd~@' p1333 tp1334 Rp1335 F708.0 tp1336 a(I3180000 g1 (g5 S'\x1f\x85\xebQ\xb8\xee~@' p1337 tp1338 Rp1339 g1 (g5 S'\x85\xebQ\xb8\x1e5\x7f@' p1340 tp1341 Rp1342 F308.0 tp1343 a(I3190000 g1 (g5 S'\xaeG\xe1z\x14.\x7f@' p1344 tp1345 Rp1346 g1 (g5 S'\xe1z\x14\xaeGQ\x7f@' p1347 tp1348 Rp1349 F308.0 tp1350 a(I3200000 g1 (g5 S'\x00\x00\x00\x00\x00 \x7f@' p1351 tp1352 Rp1353 g1349 F484.0 tp1354 a(I3210000 g1 (g5 S'\xaeG\xe1z\x14.\x7f@' p1355 tp1356 Rp1357 g1349 F616.0 tp1358 a(I3220000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xac\x7f@' p1359 tp1360 Rp1361 g1 (g5 S')\\\x8f\xc2\xf5\xc8\x7f@' p1362 tp1363 Rp1364 F440.0 tp1365 a(I3230000 g1 (g5 S'{\x14\xaeG\xe1Z~@' p1366 tp1367 Rp1368 g1 (g5 S'\xd7\xa3p=\n\xd7\x7f@' p1369 tp1370 Rp1371 F176.0 tp1372 a(I3240000 g1 (g5 S'\xe1z\x14\xaeG\xf1}@' p1373 tp1374 Rp1375 g1371 F352.0 tp1376 a(I3250000 g1 (g5 S'\xd7\xa3p=\n\xc7}@' p1377 tp1378 Rp1379 g1371 F264.0 tp1380 a(I3260000 g1 (g5 S'\x9a\x99\x99\x99\x99y}@' p1381 tp1382 Rp1383 g1371 F528.0 tp1384 a(I3270000 g1 (g5 S'33333#}@' p1385 tp1386 Rp1387 g1371 F396.0 tp1388 a(I3280000 g1 (g5 S'\x00\x00\x00\x00\x00\x10|@' p1389 tp1390 Rp1391 g1371 F264.0 tp1392 a(I3290000 g1 (g5 S'\x1f\x85\xebQ\xb8\xde{@' p1393 tp1394 Rp1395 g1371 F484.0 tp1396 a(I3300000 g1 (g5 S'\\\x8f\xc2\xf5(\xac{@' p1397 tp1398 Rp1399 g1371 F220.0 tp1400 a(I3310000 g1 (g5 S'\xe1z\x14\xaeG\xa1z@' p1401 tp1402 Rp1403 g1371 F352.0 tp1404 a(I3320000 g1 (g5 S'\xc3\xf5(\\\x8fRz@' p1405 tp1406 Rp1407 g1371 F264.0 tp1408 a(I3330000 g1 (g5 S'\\\x8f\xc2\xf5(Lz@' p1409 tp1410 Rp1411 g1371 F440.0 tp1412 a(I3340000 g1 (g5 S'R\xb8\x1e\x85\xeb\x91z@' p1413 tp1414 Rp1415 g1371 F572.0 tp1416 a(I3350000 g1 (g5 S'\x8f\xc2\xf5(\\\x1f{@' p1417 tp1418 Rp1419 g1371 F528.0 tp1420 a(I3360000 g1 (g5 S'\xa4p=\n\xd7s{@' p1421 tp1422 Rp1423 g1371 F660.0 tp1424 a(I3370000 g1 (g5 S'\x8f\xc2\xf5(\\\x7f{@' p1425 tp1426 Rp1427 g1371 F264.0 tp1428 a(I3380000 g1 (g5 S'\xd7\xa3p=\n\x87{@' p1429 tp1430 Rp1431 g1371 F396.0 tp1432 a(I3390000 g1 (g5 S'\xa4p=\n\xd7\xb3{@' p1433 tp1434 Rp1435 g1371 F484.0 tp1436 a(I3400000 g1 (g5 S'=\n\xd7\xa3p\xad|@' p1437 tp1438 Rp1439 g1371 F176.0 tp1440 a(I3410000 g1 (g5 S'\xf6(\\\x8f\xc2\xe5|@' p1441 tp1442 Rp1443 g1371 F528.0 tp1444 a(I3420000 g1 (g5 S'\xf6(\\\x8f\xc2\x95}@' p1445 tp1446 Rp1447 g1371 F308.0 tp1448 a(I3430000 g1 (g5 S'\x85\xebQ\xb8\x1e\xe5}@' p1449 tp1450 Rp1451 g1371 F264.0 tp1452 a(I3440000 g1 (g5 S'q=\n\xd7\xa3@~@' p1453 tp1454 Rp1455 g1371 F264.0 tp1456 a(I3450000 g1 (g5 S'fffff\xd6~@' p1457 tp1458 Rp1459 g1371 F660.0 tp1460 a(I3460000 g1 (g5 S'H\xe1z\x14\xae\xb7\x7f@' p1461 tp1462 Rp1463 g1371 F660.0 tp1464 a(I3470000 g1 (g5 S'\xaeG\xe1z\x14\xce\x7f@' p1465 tp1466 Rp1467 g1 (g5 S'\x8f\xc2\xf5(\\\xff\x7f@' p1468 tp1469 Rp1470 F264.0 tp1471 a(I3480000 g1 (g5 S'=\n\xd7\xa3p\xad\x7f@' p1472 tp1473 Rp1474 g1470 F852.0 tp1475 a(I3490000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc|\x80@' p1476 tp1477 Rp1478 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc|\x80@' p1479 tp1480 Rp1481 F660.0 tp1482 a(I3500000 g1 (g5 S'\x85\xebQ\xb8\x1e\xcd\x80@' p1483 tp1484 Rp1485 g1 (g5 S'\x85\xebQ\xb8\x1e\xcd\x80@' p1486 tp1487 Rp1488 F396.0 tp1489 a(I3510000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xf4\x80@' p1490 tp1491 Rp1492 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xf4\x80@' p1493 tp1494 Rp1495 F660.0 tp1496 a(I3520000 g1 (g5 S'\\\x8f\xc2\xf5(\x1c\x81@' p1497 tp1498 Rp1499 g1 (g5 S'\\\x8f\xc2\xf5(\x1c\x81@' p1500 tp1501 Rp1502 F756.0 tp1503 a(I3530000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x14\x81@' p1504 tp1505 Rp1506 g1502 F660.0 tp1507 a(I3540000 g1 (g5 S'\x14\xaeG\xe1zt\x81@' p1508 tp1509 Rp1510 g1 (g5 S'\x14\xaeG\xe1zt\x81@' p1511 tp1512 Rp1513 F616.0 tp1514 a(I3550000 g1 (g5 S')\\\x8f\xc2\xf5\xa0\x81@' p1515 tp1516 Rp1517 g1 (g5 S'\xb8\x1e\x85\xebQ\xb0\x81@' p1518 tp1519 Rp1520 F804.0 tp1521 a(I3560000 g1 (g5 S'\xaeG\xe1z\x14\xb6\x81@' p1522 tp1523 Rp1524 g1 (g5 S'\x9a\x99\x99\x99\x99\xb9\x81@' p1525 tp1526 Rp1527 F756.0 tp1528 a(I3570000 g1 (g5 S'\xecQ\xb8\x1e\x85\xa3\x81@' p1529 tp1530 Rp1531 g1527 F616.0 tp1532 a(I3580000 g1 (g5 S'\xc3\xf5(\\\x8f\xc2\x81@' p1533 tp1534 Rp1535 g1 (g5 S'\xc3\xf5(\\\x8f\xc2\x81@' p1536 tp1537 Rp1538 F756.0 tp1539 a(I3590000 g1 (g5 S'\xaeG\xe1z\x14F\x82@' p1540 tp1541 Rp1542 g1 (g5 S'H\xe1z\x14\xaeW\x82@' p1543 tp1544 Rp1545 F440.0 tp1546 a(I3600000 g1 (g5 S'\x00\x00\x00\x00\x008\x82@' p1547 tp1548 Rp1549 g1 (g5 S'\xf6(\\\x8f\xc2e\x82@' p1550 tp1551 Rp1552 F528.0 tp1553 a(I3610000 g1 (g5 S'\xe1z\x14\xaeGY\x82@' p1554 tp1555 Rp1556 g1 (g5 S'R\xb8\x1e\x85\xebq\x82@' p1557 tp1558 Rp1559 F264.0 tp1560 a(I3620000 g1 (g5 S'\x85\xebQ\xb8\x1e]\x82@' p1561 tp1562 Rp1563 g1559 F660.0 tp1564 a(I3630000 g1 (g5 S'\\\x8f\xc2\xf5(\xac\x82@' p1565 tp1566 Rp1567 g1 (g5 S'\\\x8f\xc2\xf5(\xb4\x82@' p1568 tp1569 Rp1570 F660.0 tp1571 a(I3640000 g1 (g5 S'33333\xc3\x82@' p1572 tp1573 Rp1574 g1 (g5 S'\x1f\x85\xebQ\xb8\xc6\x82@' p1575 tp1576 Rp1577 F616.0 tp1578 a(I3650000 g1 (g5 S'H\xe1z\x14\xae\x07\x83@' p1579 tp1580 Rp1581 g1 (g5 S'H\xe1z\x14\xae\x07\x83@' p1582 tp1583 Rp1584 F616.0 tp1585 a(I3660000 g1 (g5 S'=\n\xd7\xa3p5\x83@' p1586 tp1587 Rp1588 g1 (g5 S'=\n\xd7\xa3p5\x83@' p1589 tp1590 Rp1591 F572.0 tp1592 a(I3670000 g1 (g5 S'\xa4p=\n\xd7S\x83@' p1593 tp1594 Rp1595 g1 (g5 S'\xf6(\\\x8f\xc2m\x83@' p1596 tp1597 Rp1598 F1284.0 tp1599 a(I3680000 g1 (g5 S'\x1f\x85\xebQ\xb8>\x83@' p1600 tp1601 Rp1602 g1598 F396.0 tp1603 a(I3690000 g1 (g5 S'\xc3\xf5(\\\x8fR\x83@' p1604 tp1605 Rp1606 g1598 F528.0 tp1607 a(I3700000 g1 (g5 S'33333c\x83@' p1608 tp1609 Rp1610 g1598 F1140.0 tp1611 a(I3710000 g1 (g5 S'\x1f\x85\xebQ\xb8v\x83@' p1612 tp1613 Rp1614 g1 (g5 S'\x8f\xc2\xf5(\\\x7f\x83@' p1615 tp1616 Rp1617 F948.0 tp1618 a(I3720000 g1 (g5 S'\xb8\x1e\x85\xebQX\x83@' p1619 tp1620 Rp1621 g1 (g5 S'=\n\xd7\xa3p\x8d\x83@' p1622 tp1623 Rp1624 F528.0 tp1625 a(I3730000 g1 (g5 S'\xecQ\xb8\x1e\x85;\x83@' p1626 tp1627 Rp1628 g1624 F484.0 tp1629 a(I3740000 g1 (g5 S'\xd7\xa3p=\nw\x83@' p1630 tp1631 Rp1632 g1624 F708.0 tp1633 a(I3750000 g1 (g5 S'\n\xd7\xa3p=J\x83@' p1634 tp1635 Rp1636 g1624 F660.0 tp1637 a(I3760000 g1 (g5 S'H\xe1z\x14\xae\x7f\x83@' p1638 tp1639 Rp1640 g1624 F528.0 tp1641 a(I3770000 g1 (g5 S'\\\x8f\xc2\xf5(\xb4\x83@' p1642 tp1643 Rp1644 g1 (g5 S'\x9a\x99\x99\x99\x99\xc1\x83@' p1645 tp1646 Rp1647 F756.0 tp1648 a(I3780000 g1 (g5 S'{\x14\xaeG\xe1\xc2\x83@' p1649 tp1650 Rp1651 g1 (g5 S'\xf6(\\\x8f\xc2\xd5\x83@' p1652 tp1653 Rp1654 F616.0 tp1655 a(I3790000 g1 (g5 S'R\xb8\x1e\x85\xebI\x84@' p1656 tp1657 Rp1658 g1 (g5 S'R\xb8\x1e\x85\xebI\x84@' p1659 tp1660 Rp1661 F660.0 tp1662 a(I3800000 g1 (g5 S'\xf6(\\\x8f\xc2m\x84@' p1663 tp1664 Rp1665 g1 (g5 S'\xf6(\\\x8f\xc2m\x84@' p1666 tp1667 Rp1668 F572.0 tp1669 a(I3810000 g1 (g5 S'R\xb8\x1e\x85\xeb\xd1\x84@' p1670 tp1671 Rp1672 g1 (g5 S'R\xb8\x1e\x85\xeb\xd1\x84@' p1673 tp1674 Rp1675 F616.0 tp1676 a(I3820000 g1 (g5 S'\xb8\x1e\x85\xebQ\xa0\x84@' p1677 tp1678 Rp1679 g1675 F708.0 tp1680 a(I3830000 g1 (g5 S')\\\x8f\xc2\xf5\xe0\x84@' p1681 tp1682 Rp1683 g1 (g5 S')\\\x8f\xc2\xf5\xe0\x84@' p1684 tp1685 Rp1686 F1092.0 tp1687 a(I3840000 g1 (g5 S'q=\n\xd7\xa3\xd0\x84@' p1688 tp1689 Rp1690 g1 (g5 S'\x1f\x85\xebQ\xb8\xfe\x84@' p1691 tp1692 Rp1693 F708.0 tp1694 a(I3850000 g1 (g5 S'\x1f\x85\xebQ\xb8\x0e\x85@' p1695 tp1696 Rp1697 g1 (g5 S'\x1f\x85\xebQ\xb8\x0e\x85@' p1698 tp1699 Rp1700 F756.0 tp1701 a(I3860000 g1 (g5 S'\xd7\xa3p=\nO\x85@' p1702 tp1703 Rp1704 g1 (g5 S'\x00\x00\x00\x00\x00P\x85@' p1705 tp1706 Rp1707 F948.0 tp1708 a(I3870000 g1 (g5 S'\x00\x00\x00\x00\x00\x88\x85@' p1709 tp1710 Rp1711 g1 (g5 S'\x8f\xc2\xf5(\\\x97\x85@' p1712 tp1713 Rp1714 F948.0 tp1715 a(I3880000 g1 (g5 S'\x00\x00\x00\x00\x00\xd8\x85@' p1716 tp1717 Rp1718 g1 (g5 S'\x00\x00\x00\x00\x00\xd8\x85@' p1719 tp1720 Rp1721 F852.0 tp1722 a(I3890000 g1 (g5 S'\xc3\xf5(\\\x8f\xda\x85@' p1723 tp1724 Rp1725 g1 (g5 S'\xc3\xf5(\\\x8f\xda\x85@' p1726 tp1727 Rp1728 F1432.0 tp1729 a(I3900000 g1 (g5 S'\x85\xebQ\xb8\x1e\xfd\x85@' p1730 tp1731 Rp1732 g1 (g5 S'\xc3\xf5(\\\x8f\x12\x86@' p1733 tp1734 Rp1735 F660.0 tp1736 a(I3910000 g1 (g5 S'\xe1z\x14\xaeGA\x86@' p1737 tp1738 Rp1739 g1 (g5 S'\xe1z\x14\xaeGA\x86@' p1740 tp1741 Rp1742 F804.0 tp1743 a(I3920000 g1 (g5 S'\xcd\xcc\xcc\xcc\xccT\x86@' p1744 tp1745 Rp1746 g1 (g5 S'\\\x8f\xc2\xf5(\\\x86@' p1747 tp1748 Rp1749 F708.0 tp1750 a(I3930000 g1 (g5 S'33333\x0b\x86@' p1751 tp1752 Rp1753 g1 (g5 S'R\xb8\x1e\x85\xebi\x86@' p1754 tp1755 Rp1756 F616.0 tp1757 a(I3940000 g1 (g5 S')\\\x8f\xc2\xf5h\x86@' p1758 tp1759 Rp1760 g1 (g5 S'\xaeG\xe1z\x14n\x86@' p1761 tp1762 Rp1763 F660.0 tp1764 a(I3950000 g1 (g5 S'fffff\xae\x86@' p1765 tp1766 Rp1767 g1 (g5 S'R\xb8\x1e\x85\xeb\xb1\x86@' p1768 tp1769 Rp1770 F616.0 tp1771 a(I3960000 g1 (g5 S'\xe1z\x14\xaeG\x81\x86@' p1772 tp1773 Rp1774 g1770 F660.0 tp1775 a(I3970000 g1 (g5 S')\\\x8f\xc2\xf5\xa0\x86@' p1776 tp1777 Rp1778 g1770 F1044.0 tp1779 a(I3980000 g1 (g5 S'\x14\xaeG\xe1z\xf4\x86@' p1780 tp1781 Rp1782 g1 (g5 S'\\\x8f\xc2\xf5(\xfc\x86@' p1783 tp1784 Rp1785 F660.0 tp1786 a(I3990000 g1 (g5 S')\\\x8f\xc2\xf5\xe8\x86@' p1787 tp1788 Rp1789 g1785 F660.0 tp1790 a(I4000000 g1 (g5 S'\xe1z\x14\xaeG\x11\x87@' p1791 tp1792 Rp1793 g1 (g5 S'\xa4p=\n\xd7\x1b\x87@' p1794 tp1795 Rp1796 F484.0 tp1797 a(I4010000 g1 (g5 S'fffff\xf6\x86@' p1798 tp1799 Rp1800 g1796 F852.0 tp1801 a(I4020000 g1 (g5 S'fffff\xf6\x86@' p1802 tp1803 Rp1804 g1796 F396.0 tp1805 a(I4030000 g1 (g5 S'\n\xd7\xa3p=\xba\x86@' p1806 tp1807 Rp1808 g1796 F660.0 tp1809 a(I4040000 g1 (g5 S'\x85\xebQ\xb8\x1e\r\x87@' p1810 tp1811 Rp1812 g1796 F1140.0 tp1813 a(I4050000 g1 (g5 S'\x1f\x85\xebQ\xb8\x06\x87@' p1814 tp1815 Rp1816 g1796 F948.0 tp1817 a(I4060000 g1 (g5 S'R\xb8\x1e\x85\xebq\x87@' p1818 tp1819 Rp1820 g1 (g5 S'R\xb8\x1e\x85\xebq\x87@' p1821 tp1822 Rp1823 F900.0 tp1824 a(I4070000 g1 (g5 S'\x8f\xc2\xf5(\\G\x87@' p1825 tp1826 Rp1827 g1 (g5 S'\x14\xaeG\xe1z\x94\x87@' p1828 tp1829 Rp1830 F660.0 tp1831 a(I4080000 g1 (g5 S'{\x14\xaeG\xe1z\x87@' p1832 tp1833 Rp1834 g1830 F804.0 tp1835 a(I4090000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc|\x88@' p1836 tp1837 Rp1838 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc|\x88@' p1839 tp1840 Rp1841 F1284.0 tp1842 a(I4100000 g1 (g5 S'fffff\x16\x89@' p1843 tp1844 Rp1845 g1 (g5 S'fffff\x16\x89@' p1846 tp1847 Rp1848 F1692.0 tp1849 a(I4110000 g1 (g5 S'\xc3\xf5(\\\x8f\xb2\x89@' p1850 tp1851 Rp1852 g1 (g5 S'\xc3\xf5(\\\x8f\xb2\x89@' p1853 tp1854 Rp1855 F948.0 tp1856 a(I4120000 g1 (g5 S'\xa4p=\n\xd7;\x8a@' p1857 tp1858 Rp1859 g1 (g5 S'\xa4p=\n\xd7;\x8a@' p1860 tp1861 Rp1862 F2160.0 tp1863 a(I4130000 g1 (g5 S'\x00\x00\x00\x00\x00\xc8\x8a@' p1864 tp1865 Rp1866 g1 (g5 S'\x00\x00\x00\x00\x00\xc8\x8a@' p1867 tp1868 Rp1869 F660.0 tp1870 a(I4140000 g1 (g5 S')\\\x8f\xc2\xf5p\x8b@' p1871 tp1872 Rp1873 g1 (g5 S')\\\x8f\xc2\xf5p\x8b@' p1874 tp1875 Rp1876 F1140.0 tp1877 a(I4150000 g1 (g5 S'q=\n\xd7\xa38\x8c@' p1878 tp1879 Rp1880 g1 (g5 S'q=\n\xd7\xa38\x8c@' p1881 tp1882 Rp1883 F1380.0 tp1884 a(I4160000 g1 (g5 S'\xc3\xf5(\\\x8f\x12\x8d@' p1885 tp1886 Rp1887 g1 (g5 S'\xc3\xf5(\\\x8f\x12\x8d@' p1888 tp1889 Rp1890 F2004.0 tp1891 a(I4170000 g1 (g5 S'\\\x8f\xc2\xf5(T\x8e@' p1892 tp1893 Rp1894 g1 (g5 S'\\\x8f\xc2\xf5(T\x8e@' p1895 tp1896 Rp1897 F1284.0 tp1898 a(I4180000 g1 (g5 S'\xf6(\\\x8f\xc2\x8d\x8e@' p1899 tp1900 Rp1901 g1 (g5 S'\xf6(\\\x8f\xc2\x8d\x8e@' p1902 tp1903 Rp1904 F2004.0 tp1905 a(I4190000 g1 (g5 S')\\\x8f\xc2\xf5\x00\x8f@' p1906 tp1907 Rp1908 g1 (g5 S')\\\x8f\xc2\xf5\x00\x8f@' p1909 tp1910 Rp1911 F708.0 tp1912 a(I4200000 g1 (g5 S'\x14\xaeG\xe1zl\x8f@' p1913 tp1914 Rp1915 g1 (g5 S'\x14\xaeG\xe1zl\x8f@' p1916 tp1917 Rp1918 F1484.0 tp1919 a(I4210000 g1 (g5 S'=\n\xd7\xa3p-\x90@' p1920 tp1921 Rp1922 g1 (g5 S'=\n\xd7\xa3p-\x90@' p1923 tp1924 Rp1925 F1332.0 tp1926 a(I4220000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xd8\x90@' p1927 tp1928 Rp1929 g1 (g5 S'q=\n\xd7\xa3\xdc\x90@' p1930 tp1931 Rp1932 F708.0 tp1933 a(I4230000 g1 (g5 S'\x9a\x99\x99\x99\x99I\x91@' p1934 tp1935 Rp1936 g1 (g5 S'\x9a\x99\x99\x99\x99I\x91@' p1937 tp1938 Rp1939 F1236.0 tp1940 a(I4240000 g1 (g5 S'\x8f\xc2\xf5(\\\xc3\x91@' p1941 tp1942 Rp1943 g1 (g5 S'\x8f\xc2\xf5(\\\xc3\x91@' p1944 tp1945 Rp1946 F2004.0 tp1947 a(I4250000 g1 (g5 S')\\\x8f\xc2\xf5\x08\x92@' p1948 tp1949 Rp1950 g1 (g5 S')\\\x8f\xc2\xf5\x08\x92@' p1951 tp1952 Rp1953 F660.0 tp1954 a(I4260000 g1 (g5 S'\x14\xaeG\xe1zp\x92@' p1955 tp1956 Rp1957 g1 (g5 S'\x9a\x99\x99\x99\x99\x85\x92@' p1958 tp1959 Rp1960 F660.0 tp1961 a(I4270000 g1 (g5 S'\x8f\xc2\xf5(\\\xaf\x92@' p1962 tp1963 Rp1964 g1 (g5 S'\x8f\xc2\xf5(\\\xaf\x92@' p1965 tp1966 Rp1967 F1380.0 tp1968 a(I4280000 g1 (g5 S'\xf6(\\\x8f\xc2\r\x93@' p1969 tp1970 Rp1971 g1 (g5 S'\xf6(\\\x8f\xc2\r\x93@' p1972 tp1973 Rp1974 F1432.0 tp1975 a(I4290000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc@\x93@' p1976 tp1977 Rp1978 g1 (g5 S'\x14\xaeG\xe1zH\x93@' p1979 tp1980 Rp1981 F804.0 tp1982 a(I4300000 g1 (g5 S'\x14\xaeG\xe1z\xc0\x93@' p1983 tp1984 Rp1985 g1 (g5 S'\x14\xaeG\xe1z\xc0\x93@' p1986 tp1987 Rp1988 F1332.0 tp1989 a(I4310000 g1 (g5 S'\x14\xaeG\xe1z<\x94@' p1990 tp1991 Rp1992 g1 (g5 S'\x14\xaeG\xe1z<\x94@' p1993 tp1994 Rp1995 F2636.0 tp1996 a(I4320000 g1 (g5 S'H\xe1z\x14\xae\xdb\x94@' p1997 tp1998 Rp1999 g1 (g5 S'H\xe1z\x14\xae\xdb\x94@' p2000 tp2001 Rp2002 F1900.0 tp2003 a(I4330000 g1 (g5 S'=\n\xd7\xa3p=\x95@' p2004 tp2005 Rp2006 g1 (g5 S'=\n\xd7\xa3p=\x95@' p2007 tp2008 Rp2009 F2384.0 tp2010 a(I4340000 g1 (g5 S'\xa4p=\n\xd7\x7f\x95@' p2011 tp2012 Rp2013 g1 (g5 S'\xa4p=\n\xd7\x7f\x95@' p2014 tp2015 Rp2016 F2328.0 tp2017 a(I4350000 g1 (g5 S'\xecQ\xb8\x1e\x85\xdb\x95@' p2018 tp2019 Rp2020 g1 (g5 S'\xecQ\xb8\x1e\x85\xdb\x95@' p2021 tp2022 Rp2023 F2004.0 tp2024 a(I4360000 g1 (g5 S'=\n\xd7\xa3p5\x96@' p2025 tp2026 Rp2027 g1 (g5 S'=\n\xd7\xa3p5\x96@' p2028 tp2029 Rp2030 F1380.0 tp2031 a(I4370000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xa4\x96@' p2032 tp2033 Rp2034 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xa4\x96@' p2035 tp2036 Rp2037 F1140.0 tp2038 a(I4380000 g1 (g5 S'\xf6(\\\x8f\xc2\xbd\x96@' p2039 tp2040 Rp2041 g1 (g5 S'\xf6(\\\x8f\xc2\xbd\x96@' p2042 tp2043 Rp2044 F948.0 tp2045 a(I4390000 g1 (g5 S'\n\xd7\xa3p=\x06\x97@' p2046 tp2047 Rp2048 g1 (g5 S'\n\xd7\xa3p=\x06\x97@' p2049 tp2050 Rp2051 F3000.0 tp2052 a(I4400000 g1 (g5 S'\x14\xaeG\xe1z\x84\x97@' p2053 tp2054 Rp2055 g1 (g5 S'\x14\xaeG\xe1z\x84\x97@' p2056 tp2057 Rp2058 F3084.0 tp2059 a(I4410000 g1 (g5 S'\x14\xaeG\xe1z\x04\x98@' p2060 tp2061 Rp2062 g1 (g5 S'\x14\xaeG\xe1z\x04\x98@' p2063 tp2064 Rp2065 F1380.0 tp2066 a(I4420000 g1 (g5 S'\\\x8f\xc2\xf5(P\x98@' p2067 tp2068 Rp2069 g1 (g5 S'\\\x8f\xc2\xf5(P\x98@' p2070 tp2071 Rp2072 F1332.0 tp2073 a(I4430000 g1 (g5 S'\x00\x00\x00\x00\x00\xc8\x98@' p2074 tp2075 Rp2076 g1 (g5 S'\x00\x00\x00\x00\x00\xc8\x98@' p2077 tp2078 Rp2079 F4156.0 tp2080 a(I4440000 g1 (g5 S'\x1f\x85\xebQ\xb82\x99@' p2081 tp2082 Rp2083 g1 (g5 S'\x1f\x85\xebQ\xb82\x99@' p2084 tp2085 Rp2086 F1432.0 tp2087 a(I4450000 g1 (g5 S'33333\x9b\x99@' p2088 tp2089 Rp2090 g1 (g5 S'33333\x9b\x99@' p2091 tp2092 Rp2093 F2832.0 tp2094 a(I4460000 g1 (g5 S'\xa4p=\n\xd7\xdf\x99@' p2095 tp2096 Rp2097 g1 (g5 S'\xa4p=\n\xd7\xdf\x99@' p2098 tp2099 Rp2100 F4028.0 tp2101 a(I4470000 g1 (g5 S'\xe1z\x14\xaeG\xf9\x99@' p2102 tp2103 Rp2104 g1 (g5 S'\xc3\xf5(\\\x8f\n\x9a@' p2105 tp2106 Rp2107 F852.0 tp2108 a(I4480000 g1 (g5 S'\x14\xaeG\xe1z\x84\x9a@' p2109 tp2110 Rp2111 g1 (g5 S'\x14\xaeG\xe1z\x84\x9a@' p2112 tp2113 Rp2114 F3840.0 tp2115 a(I4490000 g1 (g5 S'\x8f\xc2\xf5(\\\xdb\x9a@' p2116 tp2117 Rp2118 g1 (g5 S'\x8f\xc2\xf5(\\\xdb\x9a@' p2119 tp2120 Rp2121 F1332.0 tp2122 a(I4500000 g1 (g5 S'\x9a\x99\x99\x99\x99y\x9b@' p2123 tp2124 Rp2125 g1 (g5 S'\x9a\x99\x99\x99\x99y\x9b@' p2126 tp2127 Rp2128 F3360.0 tp2129 a(I4510000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc(\x9c@' p2130 tp2131 Rp2132 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc(\x9c@' p2133 tp2134 Rp2135 F1744.0 tp2136 a(I4520000 g1 (g5 S'33333\xab\x9c@' p2137 tp2138 Rp2139 g1 (g5 S'33333\xab\x9c@' p2140 tp2141 Rp2142 F4992.0 tp2143 a(I4530000 g1 (g5 S'\xb8\x1e\x85\xebQH\x9d@' p2144 tp2145 Rp2146 g1 (g5 S'\xb8\x1e\x85\xebQH\x9d@' p2147 tp2148 Rp2149 F2832.0 tp2150 a(I4540000 g1 (g5 S'\x8f\xc2\xf5(\\\xbf\x9d@' p2151 tp2152 Rp2153 g1 (g5 S'\x8f\xc2\xf5(\\\xbf\x9d@' p2154 tp2155 Rp2156 F3780.0 tp2157 a(I4550000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xf4\x9d@' p2158 tp2159 Rp2160 g1 (g5 S'fffff\n\x9e@' p2161 tp2162 Rp2163 F1900.0 tp2164 a(I4560000 g1 (g5 S'\xe1z\x14\xaeG\xc9\x9e@' p2165 tp2166 Rp2167 g1 (g5 S'\xe1z\x14\xaeG\xc9\x9e@' p2168 tp2169 Rp2170 F3600.0 tp2171 a(I4570000 g1 (g5 S'q=\n\xd7\xa3\\\x9f@' p2172 tp2173 Rp2174 g1 (g5 S'q=\n\xd7\xa3\\\x9f@' p2175 tp2176 Rp2177 F1588.0 tp2178 a(I4580000 g1 (g5 S'\xaeG\xe1z\x14\xb2\x9f@' p2179 tp2180 Rp2181 g1 (g5 S'\xaeG\xe1z\x14\xb2\x9f@' p2182 tp2183 Rp2184 F2440.0 tp2185 a(I4590000 g1 (g5 S'\x14\xaeG\xe1z\x0c\xa0@' p2186 tp2187 Rp2188 g1 (g5 S'\x14\xaeG\xe1z\x0c\xa0@' p2189 tp2190 Rp2191 F3084.0 tp2192 a(I4600000 g1 (g5 S'R\xb8\x1e\x85\xeb^\xa0@' p2193 tp2194 Rp2195 g1 (g5 S'R\xb8\x1e\x85\xeb^\xa0@' p2196 tp2197 Rp2198 F4470.0 tp2199 a(I4610000 g1 (g5 S'\x8f\xc2\xf5(\\\x8b\xa0@' p2200 tp2201 Rp2202 g1 (g5 S'\x8f\xc2\xf5(\\\x8b\xa0@' p2203 tp2204 Rp2205 F3810.0 tp2206 a(I4620000 g1 (g5 S'33333)\xa1@' p2207 tp2208 Rp2209 g1 (g5 S'33333)\xa1@' p2210 tp2211 Rp2212 F3840.0 tp2213 a(I4630000 g1 (g5 S')\\\x8f\xc2\xf5\x88\xa1@' p2214 tp2215 Rp2216 g1 (g5 S')\\\x8f\xc2\xf5\x88\xa1@' p2217 tp2218 Rp2219 F3600.0 tp2220 a(I4640000 g1 (g5 S'\x8f\xc2\xf5(\\\xdd\xa1@' p2221 tp2222 Rp2223 g1 (g5 S'\x8f\xc2\xf5(\\\xdd\xa1@' p2224 tp2225 Rp2226 F3784.0 tp2227 a(I4650000 g1 (g5 S'\xaeG\xe1z\x14\x0e\xa2@' p2228 tp2229 Rp2230 g1 (g5 S'\xaeG\xe1z\x14\x0e\xa2@' p2231 tp2232 Rp2233 F3480.0 tp2234 a(I4660000 g1 (g5 S'q=\n\xd7\xa3\xe3\xa2@' p2235 tp2236 Rp2237 g1 (g5 S'q=\n\xd7\xa3\xe3\xa2@' p2238 tp2239 Rp2240 F5246.0 tp2241 a(I4670000 g1 (g5 S'\xa4p=\n\xd7(\xa3@' p2242 tp2243 Rp2244 g1 (g5 S'\xa4p=\n\xd7(\xa3@' p2245 tp2246 Rp2247 F4892.0 tp2248 a(I4680000 g1 (g5 S'\x14\xaeG\xe1z\xa5\xa3@' p2249 tp2250 Rp2251 g1 (g5 S'\x14\xaeG\xe1z\xa5\xa3@' p2252 tp2253 Rp2254 F5070.0 tp2255 a(I4690000 g1 (g5 S'fffff!\xa4@' p2256 tp2257 Rp2258 g1 (g5 S'fffff!\xa4@' p2259 tp2260 Rp2261 F4560.0 tp2262 a(I4700000 g1 (g5 S')\\\x8f\xc2\xf5_\xa4@' p2263 tp2264 Rp2265 g1 (g5 S')\\\x8f\xc2\xf5_\xa4@' p2266 tp2267 Rp2268 F4412.0 tp2269 a(I4710000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xc4\xa4@' p2270 tp2271 Rp2272 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xc4\xa4@' p2273 tp2274 Rp2275 F3600.0 tp2276 a(I4720000 g1 (g5 S'\\\x8f\xc2\xf5(\xec\xa4@' p2277 tp2278 Rp2279 g1 (g5 S'\\\x8f\xc2\xf5(\xec\xa4@' p2280 tp2281 Rp2282 F2944.0 tp2283 a(I4730000 g1 (g5 S')\\\x8f\xc2\xf5t\xa5@' p2284 tp2285 Rp2286 g1 (g5 S')\\\x8f\xc2\xf5t\xa5@' p2287 tp2288 Rp2289 F2944.0 tp2290 a(I4740000 g1 (g5 S')\\\x8f\xc2\xf5\xb4\xa5@' p2291 tp2292 Rp2293 g1 (g5 S')\\\x8f\xc2\xf5\xb4\xa5@' p2294 tp2295 Rp2296 F4892.0 tp2297 a(I4750000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xf2\xa5@' p2298 tp2299 Rp2300 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xf2\xa5@' p2301 tp2302 Rp2303 F1640.0 tp2304 a(I4760000 g1 (g5 S'\xcd\xcc\xcc\xcc\xccB\xa6@' p2305 tp2306 Rp2307 g1 (g5 S'\xcd\xcc\xcc\xcc\xccB\xa6@' p2308 tp2309 Rp2310 F3840.0 tp2311 a(I4770000 g1 (g5 S')\\\x8f\xc2\xf5\xe7\xa6@' p2312 tp2313 Rp2314 g1 (g5 S')\\\x8f\xc2\xf5\xe7\xa6@' p2315 tp2316 Rp2317 F5340.0 tp2318 a(I4780000 g1 (g5 S'=\n\xd7\xa3p\x08\xa7@' p2319 tp2320 Rp2321 g1 (g5 S'=\n\xd7\xa3p\x08\xa7@' p2322 tp2323 Rp2324 F5212.0 tp2325 a(I4790000 g1 (g5 S'\xd7\xa3p=\n?\xa7@' p2326 tp2327 Rp2328 g1 (g5 S'\xd7\xa3p=\n?\xa7@' p2329 tp2330 Rp2331 F4734.0 tp2332 a(I4800000 g1 (g5 S'\xaeG\xe1z\x14\x88\xa7@' p2333 tp2334 Rp2335 g1 (g5 S'\xaeG\xe1z\x14\x88\xa7@' p2336 tp2337 Rp2338 F2160.0 tp2339 a(I4810000 g1 (g5 S'fffff\xf4\xa7@' p2340 tp2341 Rp2342 g1 (g5 S'fffff\xf4\xa7@' p2343 tp2344 Rp2345 F3252.0 tp2346 a(I4820000 g1 (g5 S'\\\x8f\xc2\xf5(\xe4\xa7@' p2347 tp2348 Rp2349 g2345 F1744.0 tp2350 a(I4830000 g1 (g5 S'q=\n\xd7\xa3z\xa8@' p2351 tp2352 Rp2353 g1 (g5 S'q=\n\xd7\xa3z\xa8@' p2354 tp2355 Rp2356 F1848.0 tp2357 a(I4840000 g1 (g5 S'{\x14\xaeG\xe1\xaa\xa8@' p2358 tp2359 Rp2360 g1 (g5 S'{\x14\xaeG\xe1\xaa\xa8@' p2361 tp2362 Rp2363 F1952.0 tp2364 a(I4850000 g1 (g5 S'\xd7\xa3p=\n3\xa9@' p2365 tp2366 Rp2367 g1 (g5 S'\xd7\xa3p=\n3\xa9@' p2368 tp2369 Rp2370 F4606.0 tp2371 a(I4860000 g1 (g5 S'R\xb8\x1e\x85\xeb1\xa9@' p2372 tp2373 Rp2374 g2370 F3028.0 tp2375 a(I4870000 g1 (g5 S'\xaeG\xe1z\x14R\xa9@' p2376 tp2377 Rp2378 g1 (g5 S'\xaeG\xe1z\x14R\xa9@' p2379 tp2380 Rp2381 F4384.0 tp2382 a(I4880000 g1 (g5 S'\\\x8f\xc2\xf5(\xe0\xa9@' p2383 tp2384 Rp2385 g1 (g5 S'\\\x8f\xc2\xf5(\xe0\xa9@' p2386 tp2387 Rp2388 F4412.0 tp2389 a(I4890000 g1 (g5 S'\\\x8f\xc2\xf5(-\xaa@' p2390 tp2391 Rp2392 g1 (g5 S'\\\x8f\xc2\xf5(-\xaa@' p2393 tp2394 Rp2395 F4734.0 tp2396 a(I4900000 g1 (g5 S'\n\xd7\xa3p=\x8e\xaa@' p2397 tp2398 Rp2399 g1 (g5 S'\n\xd7\xa3p=\x8e\xaa@' p2400 tp2401 Rp2402 F4290.0 tp2403 a(I4910000 g1 (g5 S'R\xb8\x1e\x85\xeb\x8d\xaa@' p2404 tp2405 Rp2406 g2402 F2160.0 tp2407 a(I4920000 g1 (g5 S'{\x14\xaeG\xe1\xde\xaa@' p2408 tp2409 Rp2410 g1 (g5 S'{\x14\xaeG\xe1\xde\xaa@' p2411 tp2412 Rp2413 F4892.0 tp2414 a(I4930000 g1 (g5 S'R\xb8\x1e\x85\xeb1\xab@' p2415 tp2416 Rp2417 g1 (g5 S'R\xb8\x1e\x85\xeb1\xab@' p2418 tp2419 Rp2420 F6312.0 tp2421 a(I4940000 g1 (g5 S'\n\xd7\xa3p=R\xab@' p2422 tp2423 Rp2424 g1 (g5 S'H\xe1z\x14\xaeU\xab@' p2425 tp2426 Rp2427 F4512.0 tp2428 a(I4950000 g1 (g5 S'\n\xd7\xa3p=\x9c\xab@' p2429 tp2430 Rp2431 g1 (g5 S'\n\xd7\xa3p=\x9c\xab@' p2432 tp2433 Rp2434 F5340.0 tp2435 a(I4960000 g1 (g5 S')\\\x8f\xc2\xf5E\xac@' p2436 tp2437 Rp2438 g1 (g5 S')\\\x8f\xc2\xf5E\xac@' p2439 tp2440 Rp2441 F5632.0 tp2442 a(I4970000 g1 (g5 S'\x00\x00\x00\x00\x00\x7f\xac@' p2443 tp2444 Rp2445 g1 (g5 S'\x00\x00\x00\x00\x00\x7f\xac@' p2446 tp2447 Rp2448 F5340.0 tp2449 a(I4980000 g1 (g5 S'=\n\xd7\xa3p\xe1\xac@' p2450 tp2451 Rp2452 g1 (g5 S'=\n\xd7\xa3p\xe1\xac@' p2453 tp2454 Rp2455 F4500.0 tp2456 a(I4990000 g1 (g5 S'\x00\x00\x00\x00\x00(\xad@' p2457 tp2458 Rp2459 g1 (g5 S'\x00\x00\x00\x00\x00(\xad@' p2460 tp2461 Rp2462 F5276.0 tp2463 a(I5000000 g1 (g5 S'\x85\xebQ\xb8\x1ey\xad@' p2464 tp2465 Rp2466 g1 (g5 S'\x85\xebQ\xb8\x1ey\xad@' p2467 tp2468 Rp2469 F7416.0 tp2470 a(I5010000 g1 (g5 S'\x14\xaeG\xe1z\xbc\xad@' p2471 tp2472 Rp2473 g1 (g5 S'\x14\xaeG\xe1z\xbc\xad@' p2474 tp2475 Rp2476 F2160.0 tp2477 a(I5020000 g1 (g5 S'\x1f\x85\xebQ\xb8 \xae@' p2478 tp2479 Rp2480 g1 (g5 S'\x1f\x85\xebQ\xb8 \xae@' p2481 tp2482 Rp2483 F3964.0 tp2484 a(I5030000 g1 (g5 S'\x85\xebQ\xb8\x1e\x1f\xae@' p2485 tp2486 Rp2487 g1 (g5 S'\xa4p=\n\xd7/\xae@' p2488 tp2489 Rp2490 F2108.0 tp2491 a(I5040000 g1 (g5 S'R\xb8\x1e\x85\xebc\xae@' p2492 tp2493 Rp2494 g1 (g5 S'R\xb8\x1e\x85\xebc\xae@' p2495 tp2496 Rp2497 F3840.0 tp2498 a(I5050000 g1 (g5 S'\xb8\x1e\x85\xebQ\xc8\xae@' p2499 tp2500 Rp2501 g1 (g5 S'\xb8\x1e\x85\xebQ\xc8\xae@' p2502 tp2503 Rp2504 F5504.0 tp2505 a(I5060000 g1 (g5 S'\x14\xaeG\xe1z\xe4\xae@' p2506 tp2507 Rp2508 g1 (g5 S'\x1f\x85\xebQ\xb8\x06\xaf@' p2509 tp2510 Rp2511 F2608.0 tp2512 a(I5070000 g1 (g5 S'\xe1z\x14\xaeG\xe3\xae@' p2513 tp2514 Rp2515 g2511 F3000.0 tp2516 a(I5080000 g1 (g5 S'\xa4p=\n\xd7$\xaf@' p2517 tp2518 Rp2519 g1 (g5 S'\xa4p=\n\xd7$\xaf@' p2520 tp2521 Rp2522 F2160.0 tp2523 a(I5090000 g1 (g5 S'=\n\xd7\xa3p^\xaf@' p2524 tp2525 Rp2526 g1 (g5 S'=\n\xd7\xa3p^\xaf@' p2527 tp2528 Rp2529 F3300.0 tp2530 a(I5100000 g1 (g5 S'\x14\xaeG\xe1z\xf2\xaf@' p2531 tp2532 Rp2533 g1 (g5 S'\x14\xaeG\xe1z\xf2\xaf@' p2534 tp2535 Rp2536 F6262.0 tp2537 a(I5110000 g1 (g5 S'{\x14\xaeGa\x14\xb0@' p2538 tp2539 Rp2540 g1 (g5 S'{\x14\xaeGa\x14\xb0@' p2541 tp2542 Rp2543 F5798.0 tp2544 a(I5120000 g1 (g5 S'33333%\xb0@' p2545 tp2546 Rp2547 g1 (g5 S'ffff\xe6*\xb0@' p2548 tp2549 Rp2550 F3900.0 tp2551 a(I5130000 g1 (g5 S'\x00\x00\x00\x00\x00c\xb0@' p2552 tp2553 Rp2554 g1 (g5 S'\x00\x00\x00\x00\x00c\xb0@' p2555 tp2556 Rp2557 F8132.0 tp2558 a(I5140000 g1 (g5 S'H\xe1z\x14.N\xb0@' p2559 tp2560 Rp2561 g1 (g5 S'\x8f\xc2\xf5(\xdcy\xb0@' p2562 tp2563 Rp2564 F2384.0 tp2565 a(I5150000 g1 (g5 S'\xaeG\xe1z\x94Y\xb0@' p2566 tp2567 Rp2568 g2564 F3420.0 tp2569 a(I5160000 g1 (g5 S'\n\xd7\xa3p\xbdc\xb0@' p2570 tp2571 Rp2572 g2564 F3420.0 tp2573 a(I5170000 g1 (g5 S'H\xe1z\x14.2\xb0@' p2574 tp2575 Rp2576 g2564 F2608.0 tp2577 a(I5180000 g1 (g5 S'=\n\xd7\xa3p(\xb0@' p2578 tp2579 Rp2580 g2564 F5504.0 tp2581 a(I5190000 g1 (g5 S'\xa4p=\n\xd7D\xb0@' p2582 tp2583 Rp2584 g2564 F4860.0 tp2585 a(I5200000 g1 (g5 S'33333I\xb0@' p2586 tp2587 Rp2588 g2564 F2944.0 tp2589 a(I5210000 g1 (g5 S'\xcd\xcc\xcc\xccLd\xb0@' p2590 tp2591 Rp2592 g2564 F3660.0 tp2593 a(I5220000 g1 (g5 S'\x85\xebQ\xb8\x9el\xb0@' p2594 tp2595 Rp2596 g2564 F5246.0 tp2597 a(I5230000 g1 (g5 S'\x9a\x99\x99\x99\x19\x98\xb0@' p2598 tp2599 Rp2600 g1 (g5 S'\x9a\x99\x99\x99\x19\x98\xb0@' p2601 tp2602 Rp2603 F5964.0 tp2604 a(I5240000 g1 (g5 S'\x1f\x85\xebQ\xb8\x8c\xb0@' p2605 tp2606 Rp2607 g1 (g5 S'\xc3\xf5(\\\x0f\xa8\xb0@' p2608 tp2609 Rp2610 F3990.0 tp2611 a(I5250000 g1 (g5 S'=\n\xd7\xa3p\x88\xb0@' p2612 tp2613 Rp2614 g1 (g5 S'\xc3\xf5(\\\x8f\xab\xb0@' p2615 tp2616 Rp2617 F1380.0 tp2618 a(I5260000 g1 (g5 S'\x00\x00\x00\x00\x00z\xb0@' p2619 tp2620 Rp2621 g2617 F1744.0 tp2622 a(I5270000 g1 (g5 S'\xa4p=\n\xd7\x85\xb0@' p2623 tp2624 Rp2625 g2617 F5024.0 tp2626 a(I5280000 g1 (g5 S'\n\xd7\xa3p=p\xb0@' p2627 tp2628 Rp2629 g2617 F1952.0 tp2630 a(I5290000 g1 (g5 S')\\\x8f\xc2\xf5k\xb0@' p2631 tp2632 Rp2633 g2617 F3720.0 tp2634 a(I5300000 g1 (g5 S'\xecQ\xb8\x1e\x05\x82\xb0@' p2635 tp2636 Rp2637 g2617 F3196.0 tp2638 a(I5310000 g1 (g5 S'\n\xd7\xa3p\xbd\x83\xb0@' p2639 tp2640 Rp2641 g2617 F3000.0 tp2642 a(I5320000 g1 (g5 S'\\\x8f\xc2\xf5(\xa7\xb0@' p2643 tp2644 Rp2645 g2617 F4050.0 tp2646 a(I5330000 g1 (g5 S'\x8f\xc2\xf5(\\\x9e\xb0@' p2647 tp2648 Rp2649 g2617 F3964.0 tp2650 a(I5340000 g1 (g5 S'{\x14\xaeG\xe1\xa2\xb0@' p2651 tp2652 Rp2653 g1 (g5 S'R\xb8\x1e\x85\xeb\xb9\xb0@' p2654 tp2655 Rp2656 F1536.0 tp2657 a(I5350000 g1 (g5 S'\x85\xebQ\xb8\x9e\xc1\xb0@' p2658 tp2659 Rp2660 g1 (g5 S'\x85\xebQ\xb8\x9e\xc1\xb0@' p2661 tp2662 Rp2663 F6508.0 tp2664 a(I5360000 g1 (g5 S'\xb8\x1e\x85\xebQ\xbb\xb0@' p2665 tp2666 Rp2667 g2663 F2748.0 tp2668 a(I5370000 g1 (g5 S'fffff\xf4\xb0@' p2669 tp2670 Rp2671 g1 (g5 S'fffff\xf4\xb0@' p2672 tp2673 Rp2674 F6096.0 tp2675 a(I5380000 g1 (g5 S'R\xb8\x1e\x85\xeb\xed\xb0@' p2676 tp2677 Rp2678 g1 (g5 S'\n\xd7\xa3p=\xf7\xb0@' p2679 tp2680 Rp2681 F5412.0 tp2682 a(I5390000 g1 (g5 S'q=\n\xd7#\xde\xb0@' p2683 tp2684 Rp2685 g2681 F3900.0 tp2686 a(I5400000 g1 (g5 S'{\x14\xaeGa\x06\xb1@' p2687 tp2688 Rp2689 g1 (g5 S'{\x14\xaeGa\x06\xb1@' p2690 tp2691 Rp2692 F6146.0 tp2693 a(I5410000 g1 (g5 S'ffff\xe6&\xb1@' p2694 tp2695 Rp2696 g1 (g5 S'ffff\xe6&\xb1@' p2697 tp2698 Rp2699 F5952.0 tp2700 a(I5420000 g1 (g5 S'\xb8\x1e\x85\xeb\xd1A\xb1@' p2701 tp2702 Rp2703 g1 (g5 S'\xb8\x1e\x85\xeb\xd1A\xb1@' p2704 tp2705 Rp2706 F6804.0 tp2707 a(I5430000 g1 (g5 S'{\x14\xaeGa \xb1@' p2708 tp2709 Rp2710 g2706 F3240.0 tp2711 a(I5440000 g1 (g5 S'\xcd\xcc\xcc\xccLJ\xb1@' p2712 tp2713 Rp2714 g1 (g5 S'\xcd\xcc\xcc\xccLJ\xb1@' p2715 tp2716 Rp2717 F5602.0 tp2718 a(I5450000 g1 (g5 S'\xb8\x1e\x85\xebQA\xb1@' p2719 tp2720 Rp2721 g1 (g5 S'{\x14\xaeG\xe1L\xb1@' p2722 tp2723 Rp2724 F4606.0 tp2725 a(I5460000 g1 (g5 S'\x8f\xc2\xf5(\xdcV\xb1@' p2726 tp2727 Rp2728 g1 (g5 S'\x8f\xc2\xf5(\xdcV\xb1@' p2729 tp2730 Rp2731 F5812.0 tp2732 a(I5470000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcck\xb1@' p2733 tp2734 Rp2735 g1 (g5 S'\\\x8f\xc2\xf5\xa8m\xb1@' p2736 tp2737 Rp2738 F5154.0 tp2739 a(I5480000 g1 (g5 S'\xb8\x1e\x85\xeb\xd1G\xb1@' p2740 tp2741 Rp2742 g2738 F4156.0 tp2743 a(I5490000 g1 (g5 S'=\n\xd7\xa3\xf0s\xb1@' p2744 tp2745 Rp2746 g1 (g5 S'=\n\xd7\xa3\xf0s\xb1@' p2747 tp2748 Rp2749 F5748.0 tp2750 a(I5500000 g1 (g5 S'\\\x8f\xc2\xf5(Z\xb1@' p2751 tp2752 Rp2753 g2749 F7242.0 tp2754 a(I5510000 g1 (g5 S'\x1f\x85\xebQ\xb8\x82\xb1@' p2755 tp2756 Rp2757 g1 (g5 S'\x1f\x85\xebQ\xb8\x82\xb1@' p2758 tp2759 Rp2760 F4704.0 tp2761 a(I5520000 g1 (g5 S'q=\n\xd7#\x99\xb1@' p2762 tp2763 Rp2764 g1 (g5 S'q=\n\xd7#\x9d\xb1@' p2765 tp2766 Rp2767 F5348.0 tp2768 a(I5530000 g1 (g5 S'R\xb8\x1e\x85k\xe7\xb1@' p2769 tp2770 Rp2771 g1 (g5 S'R\xb8\x1e\x85k\xe7\xb1@' p2772 tp2773 Rp2774 F9680.0 tp2775 a(I5540000 g1 (g5 S'\xaeG\xe1z\x94\x13\xb2@' p2776 tp2777 Rp2778 g1 (g5 S'\xaeG\xe1z\x94\x13\xb2@' p2779 tp2780 Rp2781 F8256.0 tp2782 a(I5550000 g1 (g5 S'\xaeG\xe1z\x14\xca\xb1@' p2783 tp2784 Rp2785 g2781 F3990.0 tp2786 a(I5560000 g1 (g5 S'=\n\xd7\xa3p\xcd\xb1@' p2787 tp2788 Rp2789 g2781 F2944.0 tp2790 a(I5570000 g1 (g5 S'q=\n\xd7\xa3\x1f\xb2@' p2791 tp2792 Rp2793 g1 (g5 S'q=\n\xd7\xa3\x1f\xb2@' p2794 tp2795 Rp2796 F7182.0 tp2797 a(I5580000 g1 (g5 S'\xf6(\\\x8fB-\xb2@' p2798 tp2799 Rp2800 g1 (g5 S'\xf6(\\\x8fB-\xb2@' p2801 tp2802 Rp2803 F6876.0 tp2804 a(I5590000 g1 (g5 S'R\xb8\x1e\x85\xeb,\xb2@' p2805 tp2806 Rp2807 g1 (g5 S'\xa4p=\n\xd7G\xb2@' p2808 tp2809 Rp2810 F2944.0 tp2811 a(I5600000 g1 (g5 S'R\xb8\x1e\x85\xeb\x02\xb2@' p2812 tp2813 Rp2814 g2810 F3000.0 tp2815 a(I5610000 g1 (g5 S'\x9a\x99\x99\x99\x19\xfd\xb1@' p2816 tp2817 Rp2818 g2810 F2832.0 tp2819 a(I5620000 g1 (g5 S'\xcd\xcc\xcc\xccL\xf0\xb1@' p2820 tp2821 Rp2822 g2810 F3672.0 tp2823 a(I5630000 g1 (g5 S'\xd7\xa3p=\n\x18\xb2@' p2824 tp2825 Rp2826 g2810 F3000.0 tp2827 a(I5640000 g1 (g5 S'\x8f\xc2\xf5(\\,\xb2@' p2828 tp2829 Rp2830 g2810 F4028.0 tp2831 a(I5650000 g1 (g5 S'3333\xb39\xb2@' p2832 tp2833 Rp2834 g2810 F3540.0 tp2835 a(I5660000 g1 (g5 S'\\\x8f\xc2\xf5\xa8[\xb2@' p2836 tp2837 Rp2838 g1 (g5 S'\\\x8f\xc2\xf5\xa8[\xb2@' p2839 tp2840 Rp2841 F4284.0 tp2842 a(I5670000 g1 (g5 S'\xcd\xcc\xcc\xccL\x1a\xb2@' p2843 tp2844 Rp2845 g2841 F3000.0 tp2846 a(I5680000 g1 (g5 S'\xecQ\xb8\x1e\x05\x17\xb2@' p2847 tp2848 Rp2849 g2841 F5880.0 tp2850 a(I5690000 g1 (g5 S'\x8f\xc2\xf5(\\\x1e\xb2@' p2851 tp2852 Rp2853 g2841 F6240.0 tp2854 a(I5700000 g1 (g5 S'\n\xd7\xa3p\xbd+\xb2@' p2855 tp2856 Rp2857 g2841 F4348.0 tp2858 a(I5710000 g1 (g5 S'R\xb8\x1e\x85k4\xb2@' p2859 tp2860 Rp2861 g2841 F7920.0 tp2862 a(I5720000 g1 (g5 S'\xd7\xa3p=\nd\xb2@' p2863 tp2864 Rp2865 g1 (g5 S'\xd7\xa3p=\nd\xb2@' p2866 tp2867 Rp2868 F7494.0 tp2869 a(I5730000 g1 (g5 S')\\\x8f\xc2u\x81\xb2@' p2870 tp2871 Rp2872 g1 (g5 S')\\\x8f\xc2u\x81\xb2@' p2873 tp2874 Rp2875 F8970.0 tp2876 a(I5740000 g1 (g5 S'33333\xb9\xb2@' p2877 tp2878 Rp2879 g1 (g5 S'33333\xb9\xb2@' p2880 tp2881 Rp2882 F5216.0 tp2883 a(I5750000 g1 (g5 S'{\x14\xaeG\xe1\xb5\xb2@' p2884 tp2885 Rp2886 g1 (g5 S'\\\x8f\xc2\xf5(\xd6\xb2@' p2887 tp2888 Rp2889 F1796.0 tp2890 a(I5760000 g1 (g5 S'\x85\xebQ\xb8\x9e\xc7\xb2@' p2891 tp2892 Rp2893 g2889 F3840.0 tp2894 a(I5770000 g1 (g5 S'\xb8\x1e\x85\xebQ\x9f\xb2@' p2895 tp2896 Rp2897 g2889 F1588.0 tp2898 a(I5780000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xd5\xb2@' p2899 tp2900 Rp2901 g2889 F4414.0 tp2902 a(I5790000 g1 (g5 S'\x1f\x85\xebQ\xb8\xfb\xb2@' p2903 tp2904 Rp2905 g1 (g5 S'\x1f\x85\xebQ\xb8\xfb\xb2@' p2906 tp2907 Rp2908 F7260.0 tp2909 a(I5800000 g1 (g5 S'fffff\x11\xb3@' p2910 tp2911 Rp2912 g1 (g5 S'\xb8\x1e\x85\xebQ\x17\xb3@' p2913 tp2914 Rp2915 F3308.0 tp2916 a(I5810000 g1 (g5 S'H\xe1z\x14\xae/\xb3@' p2917 tp2918 Rp2919 g1 (g5 S'H\xe1z\x14\xae/\xb3@' p2920 tp2921 Rp2922 F5282.0 tp2923 a(I5820000 g1 (g5 S'\xf6(\\\x8f\xc2B\xb3@' p2924 tp2925 Rp2926 g1 (g5 S'\xf6(\\\x8f\xc2B\xb3@' p2927 tp2928 Rp2929 F6384.0 tp2930 a(I5830000 g1 (g5 S'\x1f\x85\xebQ8h\xb3@' p2931 tp2932 Rp2933 g1 (g5 S'\x1f\x85\xebQ8h\xb3@' p2934 tp2935 Rp2936 F4796.0 tp2937 a(I5840000 g1 (g5 S'\xaeG\xe1z\x94Z\xb3@' p2938 tp2939 Rp2940 g2936 F5088.0 tp2941 a(I5850000 g1 (g5 S'\x00\x00\x00\x00\x00{\xb3@' p2942 tp2943 Rp2944 g1 (g5 S'\x00\x00\x00\x00\x00{\xb3@' p2945 tp2946 Rp2947 F5182.0 tp2948 a(I5860000 g1 (g5 S'H\xe1z\x14.\x98\xb3@' p2949 tp2950 Rp2951 g1 (g5 S'H\xe1z\x14.\x98\xb3@' p2952 tp2953 Rp2954 F9014.0 tp2955 a(I5870000 g1 (g5 S'\xe1z\x14\xaeG|\xb3@' p2956 tp2957 Rp2958 g2954 F3000.0 tp2959 a(I5880000 g1 (g5 S'\x9a\x99\x99\x99\x19\xb8\xb3@' p2960 tp2961 Rp2962 g1 (g5 S'\x9a\x99\x99\x99\x19\xb8\xb3@' p2963 tp2964 Rp2965 F4640.0 tp2966 a(I5890000 g1 (g5 S'\xd7\xa3p=\n\xe3\xb3@' p2967 tp2968 Rp2969 g1 (g5 S'\xd7\xa3p=\n\xe3\xb3@' p2970 tp2971 Rp2972 F9000.0 tp2973 a(I5900000 g1 (g5 S'\xd7\xa3p=\x8a\xe2\xb3@' p2974 tp2975 Rp2976 g2972 F6096.0 tp2977 a(I5910000 g1 (g5 S'\\\x8f\xc2\xf5\xa8\x12\xb4@' p2978 tp2979 Rp2980 g1 (g5 S'\\\x8f\xc2\xf5\xa8\x12\xb4@' p2981 tp2982 Rp2983 F7140.0 tp2984 a(I5920000 g1 (g5 S'\x8f\xc2\xf5(\xdc\r\xb4@' p2985 tp2986 Rp2987 g1 (g5 S'\xf6(\\\x8fB"\xb4@' p2988 tp2989 Rp2990 F4764.0 tp2991 a(I5930000 g1 (g5 S'33333%\xb4@' p2992 tp2993 Rp2994 g1 (g5 S'33333%\xb4@' p2995 tp2996 Rp2997 F5602.0 tp2998 a(I5940000 g1 (g5 S'\xe1z\x14\xaeG3\xb4@' p2999 tp3000 Rp3001 g1 (g5 S'\xe1z\x14\xaeG3\xb4@' p3002 tp3003 Rp3004 F6462.0 tp3005 a(I5950000 g1 (g5 S'R\xb8\x1e\x85\xeb4\xb4@' p3006 tp3007 Rp3008 g1 (g5 S'=\n\xd7\xa3\xf0<\xb4@' p3009 tp3010 Rp3011 F4796.0 tp3012 a(I5960000 g1 (g5 S'\x00\x00\x00\x00\x002\xb4@' p3013 tp3014 Rp3015 g1 (g5 S'\x14\xaeG\xe1\xfaN\xb4@' p3016 tp3017 Rp3018 F4412.0 tp3019 a(I5970000 g1 (g5 S'\xe1z\x14\xae\xc7\x0e\xb4@' p3020 tp3021 Rp3022 g3018 F2496.0 tp3023 a(I5980000 g1 (g5 S'\x85\xebQ\xb8\x9eH\xb4@' p3024 tp3025 Rp3026 g3018 F7684.0 tp3027 a(I5990000 g1 (g5 S'=\n\xd7\xa3\xf0i\xb4@' p3028 tp3029 Rp3030 g1 (g5 S'\\\x8f\xc2\xf5\xa8q\xb4@' p3031 tp3032 Rp3033 F4860.0 tp3034 a(I6000000 g1 (g5 S'fffffb\xb4@' p3035 tp3036 Rp3037 g3033 F6734.0 tp3038 a(I6010000 g1 (g5 S'\x9a\x99\x99\x99\x99A\xb4@' p3039 tp3040 Rp3041 g3033 F5182.0 tp3042 a(I6020000 g1 (g5 S'\x1f\x85\xebQ\xb8,\xb4@' p3043 tp3044 Rp3045 g3033 F4542.0 tp3046 a(I6030000 g1 (g5 S'\xaeG\xe1z\x94\x15\xb4@' p3047 tp3048 Rp3049 g3033 F3900.0 tp3050 a(I6040000 g1 (g5 S'R\xb8\x1e\x85\xeb\xa9\xb3@' p3051 tp3052 Rp3053 g3033 F1380.0 tp3054 a(I6050000 g1 (g5 S'\xd7\xa3p=\x8a\xb7\xb3@' p3055 tp3056 Rp3057 g3033 F6600.0 tp3058 a(I6060000 g1 (g5 S')\\\x8f\xc2\xf5\xb6\xb3@' p3059 tp3060 Rp3061 g3033 F5130.0 tp3062 a(I6070000 g1 (g5 S'\\\x8f\xc2\xf5\xa8\xbd\xb3@' p3063 tp3064 Rp3065 g3033 F5310.0 tp3066 a(I6080000 g1 (g5 S'\xaeG\xe1z\x14\xab\xb3@' p3067 tp3068 Rp3069 g3033 F5310.0 tp3070 a(I6090000 g1 (g5 S'\xa4p=\n\xd7\xef\xb3@' p3071 tp3072 Rp3073 g3033 F9820.0 tp3074 a(I6100000 g1 (g5 S'\x85\xebQ\xb8\x9e-\xb4@' p3075 tp3076 Rp3077 g3033 F7950.0 tp3078 a(I6110000 g1 (g5 S'\xc3\xf5(\\\x8fI\xb4@' p3079 tp3080 Rp3081 g3033 F7692.0 tp3082 a(I6120000 g1 (g5 S'\xc3\xf5(\\\x0f@\xb4@' p3083 tp3084 Rp3085 g3033 F6682.0 tp3086 a(I6130000 g1 (g5 S'\x9a\x99\x99\x99\x19J\xb4@' p3087 tp3088 Rp3089 g3033 F4350.0 tp3090 a(I6140000 g1 (g5 S'\n\xd7\xa3p\xbd_\xb4@' p3091 tp3092 Rp3093 g3033 F8552.0 tp3094 a(I6150000 g1 (g5 S'\xecQ\xb8\x1e\x05\x86\xb4@' p3095 tp3096 Rp3097 g1 (g5 S'\xecQ\xb8\x1e\x05\x86\xb4@' p3098 tp3099 Rp3100 F8064.0 tp3101 a(I6160000 g1 (g5 S'\xa4p=\nW\x8d\xb4@' p3102 tp3103 Rp3104 g1 (g5 S'\\\x8f\xc2\xf5\xa8\x91\xb4@' p3105 tp3106 Rp3107 F5268.0 tp3108 a(I6170000 g1 (g5 S')\\\x8f\xc2u\x8b\xb4@' p3109 tp3110 Rp3111 g3107 F4260.0 tp3112 a(I6180000 g1 (g5 S'\xb8\x1e\x85\xeb\xd1\xbb\xb4@' p3113 tp3114 Rp3115 g1 (g5 S'\xb8\x1e\x85\xeb\xd1\xbb\xb4@' p3116 tp3117 Rp3118 F6504.0 tp3119 a(I6190000 g1 (g5 S'\\\x8f\xc2\xf5\xa8\xbf\xb4@' p3120 tp3121 Rp3122 g1 (g5 S'\\\x8f\xc2\xf5\xa8\xbf\xb4@' p3123 tp3124 Rp3125 F4284.0 tp3126 a(I6200000 g1 (g5 S'\xa4p=\n\xd7\xd4\xb4@' p3127 tp3128 Rp3129 g1 (g5 S'\xa4p=\n\xd7\xd4\xb4@' p3130 tp3131 Rp3132 F4512.0 tp3133 a(I6210000 g1 (g5 S'{\x14\xaeG\xe1\xbd\xb4@' p3134 tp3135 Rp3136 g3132 F4220.0 tp3137 a(I6220000 g1 (g5 S'=\n\xd7\xa3\xf0\xf2\xb4@' p3138 tp3139 Rp3140 g1 (g5 S'=\n\xd7\xa3\xf0\xf2\xb4@' p3141 tp3142 Rp3143 F7020.0 tp3144 a(I6230000 g1 (g5 S'3333\xb3\xd7\xb4@' p3145 tp3146 Rp3147 g3143 F6198.0 tp3148 a(I6240000 g1 (g5 S'\xe1z\x14\xaeG\xae\xb4@' p3149 tp3150 Rp3151 g3143 F5632.0 tp3152 a(I6250000 g1 (g5 S'\n\xd7\xa3p\xbd\xca\xb4@' p3153 tp3154 Rp3155 g3143 F5536.0 tp3156 a(I6260000 g1 (g5 S'=\n\xd7\xa3p\xb3\xb4@' p3157 tp3158 Rp3159 g3143 F6146.0 tp3160 a(I6270000 g1 (g5 S'\xd7\xa3p=\n\x0f\xb5@' p3161 tp3162 Rp3163 g1 (g5 S'\xd7\xa3p=\n\x0f\xb5@' p3164 tp3165 Rp3166 F5926.0 tp3167 a(I6280000 g1 (g5 S'\xb8\x1e\x85\xebQ)\xb5@' p3168 tp3169 Rp3170 g1 (g5 S'\xb8\x1e\x85\xebQ)\xb5@' p3171 tp3172 Rp3173 F5376.0 tp3174 a(I6290000 g1 (g5 S'\n\xd7\xa3p=L\xb5@' p3175 tp3176 Rp3177 g1 (g5 S'\n\xd7\xa3p=L\xb5@' p3178 tp3179 Rp3180 F7392.0 tp3181 a(I6300000 g1 (g5 S'\xecQ\xb8\x1e\x05[\xb5@' p3182 tp3183 Rp3184 g1 (g5 S'\n\xd7\xa3p\xbd}\xb5@' p3185 tp3186 Rp3187 F3900.0 tp3188 a(I6310000 g1 (g5 S'fffff\x86\xb5@' p3189 tp3190 Rp3191 g1 (g5 S'fffff\x86\xb5@' p3192 tp3193 Rp3194 F9674.0 tp3195 a(I6320000 g1 (g5 S'33333\x89\xb5@' p3196 tp3197 Rp3198 g1 (g5 S'33333\x89\xb5@' p3199 tp3200 Rp3201 F7760.0 tp3202 a(I6330000 g1 (g5 S'\xb8\x1e\x85\xebQ\xa3\xb5@' p3203 tp3204 Rp3205 g1 (g5 S'\xb8\x1e\x85\xebQ\xa3\xb5@' p3206 tp3207 Rp3208 F6562.0 tp3209 a(I6340000 g1 (g5 S'{\x14\xaeGa\xb3\xb5@' p3210 tp3211 Rp3212 g1 (g5 S'{\x14\xaeGa\xb3\xb5@' p3213 tp3214 Rp3215 F6024.0 tp3216 a(I6350000 g1 (g5 S'{\x14\xaeG\xe1\xc9\xb5@' p3217 tp3218 Rp3219 g1 (g5 S'{\x14\xaeG\xe1\xc9\xb5@' p3220 tp3221 Rp3222 F7046.0 tp3223 a(I6360000 g1 (g5 S'fffff\xe7\xb5@' p3224 tp3225 Rp3226 g1 (g5 S'fffff\xe7\xb5@' p3227 tp3228 Rp3229 F5132.0 tp3230 a(I6370000 g1 (g5 S'\xc3\xf5(\\\x8f\n\xb6@' p3231 tp3232 Rp3233 g1 (g5 S'\xc3\xf5(\\\x8f\n\xb6@' p3234 tp3235 Rp3236 F8604.0 tp3237 a(I6380000 g1 (g5 S'\x14\xaeG\xe1\xfa\x03\xb6@' p3238 tp3239 Rp3240 g1 (g5 S'\x9a\x99\x99\x99\x99\x0c\xb6@' p3241 tp3242 Rp3243 F4320.0 tp3244 a(I6390000 g1 (g5 S'\n\xd7\xa3p\xbd\xf0\xb5@' p3245 tp3246 Rp3247 g3243 F4734.0 tp3248 a(I6400000 g1 (g5 S'q=\n\xd7\xa3\xd9\xb5@' p3249 tp3250 Rp3251 g3243 F5700.0 tp3252 a(I6410000 g1 (g5 S'\xf6(\\\x8f\xc2\xec\xb5@' p3253 tp3254 Rp3255 g3243 F4832.0 tp3256 a(I6420000 g1 (g5 S'=\n\xd7\xa3\xf0\xd6\xb5@' p3257 tp3258 Rp3259 g3243 F6182.0 tp3260 a(I6430000 g1 (g5 S'\xd7\xa3p=\n\xc5\xb5@' p3261 tp3262 Rp3263 g3243 F5608.0 tp3264 a(I6440000 g1 (g5 S'\xb8\x1e\x85\xeb\xd1\xba\xb5@' p3265 tp3266 Rp3267 g3243 F4770.0 tp3268 a(I6450000 g1 (g5 S'q=\n\xd7#\xe9\xb5@' p3269 tp3270 Rp3271 g3243 F6802.0 tp3272 a(I6460000 g1 (g5 S'{\x14\xaeG\xe1\n\xb6@' p3273 tp3274 Rp3275 g3243 F9760.0 tp3276 a(I6470000 g1 (g5 S'\xcd\xcc\xcc\xccL\x18\xb6@' p3277 tp3278 Rp3279 g1 (g5 S'\xcd\xcc\xcc\xccL\x18\xb6@' p3280 tp3281 Rp3282 F6138.0 tp3283 a(I6480000 g1 (g5 S'\xc3\xf5(\\\x8fT\xb6@' p3284 tp3285 Rp3286 g1 (g5 S'\xc3\xf5(\\\x8fT\xb6@' p3287 tp3288 Rp3289 F10290.0 tp3290 a(I6490000 g1 (g5 S'\\\x8f\xc2\xf5(h\xb6@' p3291 tp3292 Rp3293 g1 (g5 S'\\\x8f\xc2\xf5(h\xb6@' p3294 tp3295 Rp3296 F4932.0 tp3297 a(I6500000 g1 (g5 S'\xe1z\x14\xae\xc7s\xb6@' p3298 tp3299 Rp3300 g1 (g5 S'\\\x8f\xc2\xf5(\x8c\xb6@' p3301 tp3302 Rp3303 F5246.0 tp3304 a(I6510000 g1 (g5 S'\x14\xaeG\xe1\xfa[\xb6@' p3305 tp3306 Rp3307 g3303 F5880.0 tp3308 a(I6520000 g1 (g5 S'=\n\xd7\xa3p\x8b\xb6@' p3309 tp3310 Rp3311 g3303 F4542.0 tp3312 a(I6530000 g1 (g5 S'H\xe1z\x14\xae\x86\xb6@' p3313 tp3314 Rp3315 g3303 F5070.0 tp3316 a(I6540000 g1 (g5 S'\xd7\xa3p=\n\xa4\xb6@' p3317 tp3318 Rp3319 g1 (g5 S'\xd7\xa3p=\n\xa4\xb6@' p3320 tp3321 Rp3322 F5892.0 tp3323 a(I6550000 g1 (g5 S'\x14\xaeG\xe1\xfa\xa3\xb6@' p3324 tp3325 Rp3326 g1 (g5 S'\xb8\x1e\x85\xebQ\xb7\xb6@' p3327 tp3328 Rp3329 F2608.0 tp3330 a(I6560000 g1 (g5 S'=\n\xd7\xa3p\xcf\xb6@' p3331 tp3332 Rp3333 g1 (g5 S'=\n\xd7\xa3p\xcf\xb6@' p3334 tp3335 Rp3336 F9080.0 tp3337 a(I6570000 g1 (g5 S'\\\x8f\xc2\xf5(\xd4\xb6@' p3338 tp3339 Rp3340 g1 (g5 S'\\\x8f\xc2\xf5(\xd4\xb6@' p3341 tp3342 Rp3343 F3196.0 tp3344 a(I6580000 g1 (g5 S'\xd7\xa3p=\n\xda\xb6@' p3345 tp3346 Rp3347 g1 (g5 S'\xa4p=\n\xd7\xf6\xb6@' p3348 tp3349 Rp3350 F3720.0 tp3351 a(I6590000 g1 (g5 S'H\xe1z\x14\xae\xea\xb6@' p3352 tp3353 Rp3354 g1 (g5 S'\xaeG\xe1z\x94\xf7\xb6@' p3355 tp3356 Rp3357 F3840.0 tp3358 a(I6600000 g1 (g5 S'\x1f\x85\xebQ8#\xb7@' p3359 tp3360 Rp3361 g1 (g5 S'\x1f\x85\xebQ8#\xb7@' p3362 tp3363 Rp3364 F7314.0 tp3365 a(I6610000 g1 (g5 S'\x8f\xc2\xf5(\xdc\n\xb7@' p3366 tp3367 Rp3368 g1 (g5 S'\xecQ\xb8\x1e\x85%\xb7@' p3369 tp3370 Rp3371 F3000.0 tp3372 a(I6620000 g1 (g5 S'R\xb8\x1e\x85k-\xb7@' p3373 tp3374 Rp3375 g1 (g5 S'R\xb8\x1e\x85k-\xb7@' p3376 tp3377 Rp3378 F8766.0 tp3379 a(I6630000 g1 (g5 S'\xaeG\xe1z\x94\t\xb7@' p3380 tp3381 Rp3382 g3378 F5676.0 tp3383 a(I6640000 g1 (g5 S'R\xb8\x1e\x85k\xcf\xb6@' p3384 tp3385 Rp3386 g3378 F5926.0 tp3387 a(I6650000 g1 (g5 S'\\\x8f\xc2\xf5(\xef\xb6@' p3388 tp3389 Rp3390 g3378 F6130.0 tp3391 a(I6660000 g1 (g5 S'H\xe1z\x14\xae\x13\xb7@' p3392 tp3393 Rp3394 g3378 F6232.0 tp3395 a(I6670000 g1 (g5 S'\xc3\xf5(\\\x8f\x05\xb7@' p3396 tp3397 Rp3398 g3378 F5088.0 tp3399 a(I6680000 g1 (g5 S'H\xe1z\x14.\xec\xb6@' p3400 tp3401 Rp3402 g3378 F4670.0 tp3403 a(I6690000 g1 (g5 S'\x1f\x85\xebQ8\x14\xb7@' p3404 tp3405 Rp3406 g3378 F6742.0 tp3407 a(I6700000 g1 (g5 S'H\xe1z\x14.&\xb7@' p3408 tp3409 Rp3410 g3378 F5952.0 tp3411 a(I6710000 g1 (g5 S'\xecQ\xb8\x1e\x05m\xb7@' p3412 tp3413 Rp3414 g1 (g5 S'\xecQ\xb8\x1e\x05m\xb7@' p3415 tp3416 Rp3417 F5934.0 tp3418 a(I6720000 g1 (g5 S'\xb8\x1e\x85\xebQk\xb7@' p3419 tp3420 Rp3421 g3417 F6334.0 tp3422 a(I6730000 g1 (g5 S'\x00\x00\x00\x00\x80\xa1\xb7@' p3423 tp3424 Rp3425 g1 (g5 S'{\x14\xaeG\xe1\xa3\xb7@' p3426 tp3427 Rp3428 F7568.0 tp3429 a(I6740000 g1 (g5 S'\x85\xebQ\xb8\x1e\xb9\xb7@' p3430 tp3431 Rp3432 g1 (g5 S'\x85\xebQ\xb8\x1e\xb9\xb7@' p3433 tp3434 Rp3435 F7566.0 tp3436 a(I6750000 g1 (g5 S'\xc3\xf5(\\\x8f\xca\xb7@' p3437 tp3438 Rp3439 g1 (g5 S'\xc3\xf5(\\\x8f\xca\xb7@' p3440 tp3441 Rp3442 F5964.0 tp3443 a(I6760000 g1 (g5 S'\xb8\x1e\x85\xebQ\xbe\xb7@' p3444 tp3445 Rp3446 g3442 F6742.0 tp3447 a(I6770000 g1 (g5 S'\xa4p=\nW\xd1\xb7@' p3448 tp3449 Rp3450 g1 (g5 S'\xa4p=\nW\xd1\xb7@' p3451 tp3452 Rp3453 F8100.0 tp3454 a(I6780000 g1 (g5 S'\x9a\x99\x99\x99\x99\xda\xb7@' p3455 tp3456 Rp3457 g1 (g5 S'\x9a\x99\x99\x99\x99\xda\xb7@' p3458 tp3459 Rp3460 F5820.0 tp3461 a(I6790000 g1 (g5 S'{\x14\xaeGa\xed\xb7@' p3462 tp3463 Rp3464 g1 (g5 S'{\x14\xaeGa\xed\xb7@' p3465 tp3466 Rp3467 F7086.0 tp3468 a(I6800000 g1 (g5 S'\xaeG\xe1z\x94F\xb8@' p3469 tp3470 Rp3471 g1 (g5 S'\xaeG\xe1z\x94F\xb8@' p3472 tp3473 Rp3474 F10300.0 tp3475 a(I6810000 g1 (g5 S'\xe1z\x14\xae\xc77\xb8@' p3476 tp3477 Rp3478 g1 (g5 S')\\\x8f\xc2\xf5N\xb8@' p3479 tp3480 Rp3481 F6372.0 tp3482 a(I6820000 g1 (g5 S'\x9a\x99\x99\x99\x994\xb8@' p3483 tp3484 Rp3485 g3481 F5608.0 tp3486 a(I6830000 g1 (g5 S'\xa4p=\n\xd7d\xb8@' p3487 tp3488 Rp3489 g1 (g5 S'\xa4p=\n\xd7d\xb8@' p3490 tp3491 Rp3492 F7486.0 tp3493 a(I6840000 g1 (g5 S'\x85\xebQ\xb8\x9eI\xb8@' p3494 tp3495 Rp3496 g3492 F4670.0 tp3497 a(I6850000 g1 (g5 S'{\x14\xaeG\xe1s\xb8@' p3498 tp3499 Rp3500 g1 (g5 S'{\x14\xaeG\xe1s\xb8@' p3501 tp3502 Rp3503 F6312.0 tp3504 a(I6860000 g1 (g5 S'H\xe1z\x14.z\xb8@' p3505 tp3506 Rp3507 g1 (g5 S'\xa4p=\n\xd7\x89\xb8@' p3508 tp3509 Rp3510 F8108.0 tp3511 a(I6870000 g1 (g5 S'{\x14\xaeG\xe1\x82\xb8@' p3512 tp3513 Rp3514 g3510 F7446.0 tp3515 a(I6880000 g1 (g5 S'\xd7\xa3p=\n}\xb8@' p3516 tp3517 Rp3518 g3510 F6876.0 tp3519 a(I6890000 g1 (g5 S'ffffft\xb8@' p3520 tp3521 Rp3522 g3510 F4092.0 tp3523 a(I6900000 g1 (g5 S'\xd7\xa3p=\n\x97\xb8@' p3524 tp3525 Rp3526 g1 (g5 S'\x8f\xc2\xf5(\xdc\xa9\xb8@' p3527 tp3528 Rp3529 F4860.0 tp3530 a(I6910000 g1 (g5 S'\x1f\x85\xebQ\xb8\x9c\xb8@' p3531 tp3532 Rp3533 g3529 F6462.0 tp3534 a(I6920000 g1 (g5 S'{\x14\xaeGa\x95\xb8@' p3535 tp3536 Rp3537 g3529 F7488.0 tp3538 a(I6930000 g1 (g5 S'\xaeG\xe1z\x14\x8d\xb8@' p3539 tp3540 Rp3541 g3529 F4170.0 tp3542 a(I6940000 g1 (g5 S'\x85\xebQ\xb8\x9e\xa5\xb8@' p3543 tp3544 Rp3545 g3529 F5308.0 tp3546 a(I6950000 g1 (g5 S'\xaeG\xe1z\x14\xc7\xb8@' p3547 tp3548 Rp3549 g1 (g5 S'\xaeG\xe1z\x14\xc7\xb8@' p3550 tp3551 Rp3552 F6176.0 tp3553 a(I6960000 g1 (g5 S'\xecQ\xb8\x1e\x85\xb7\xb8@' p3554 tp3555 Rp3556 g3552 F4092.0 tp3557 a(I6970000 g1 (g5 S'\x85\xebQ\xb8\x1e\xbf\xb8@' p3558 tp3559 Rp3560 g3552 F7492.0 tp3561 a(I6980000 g1 (g5 S'\\\x8f\xc2\xf5\xa8\xa0\xb8@' p3562 tp3563 Rp3564 g3552 F6262.0 tp3565 a(I6990000 g1 (g5 S'\xcd\xcc\xcc\xccL\xa2\xb8@' p3566 tp3567 Rp3568 g3552 F5248.0 tp3569 a(I7000000 g1 (g5 S'R\xb8\x1e\x85kz\xb8@' p3570 tp3571 Rp3572 g3552 F4220.0 tp3573 a(I7010000 g1 (g5 S'q=\n\xd7\xa3L\xb8@' p3574 tp3575 Rp3576 g3552 F5608.0 tp3577 a(I7020000 g1 (g5 S'\x1f\x85\xebQ8H\xb8@' p3578 tp3579 Rp3580 g3552 F5696.0 tp3581 a(I7030000 g1 (g5 S'33333\x16\xb8@' p3582 tp3583 Rp3584 g3552 F3000.0 tp3585 a(I7040000 g1 (g5 S'\x85\xebQ\xb8\x9e\xe0\xb7@' p3586 tp3587 Rp3588 g3552 F1380.0 tp3589 a(I7050000 g1 (g5 S'\x9a\x99\x99\x99\x19\x06\xb8@' p3590 tp3591 Rp3592 g3552 F7314.0 tp3593 a(I7060000 g1 (g5 S'\x14\xaeG\xe1\xfa\xe9\xb7@' p3594 tp3595 Rp3596 g3552 F4092.0 tp3597 a(I7070000 g1 (g5 S'\x9a\x99\x99\x99\x99\xf1\xb7@' p3598 tp3599 Rp3600 g3552 F8340.0 tp3601 a(I7080000 g1 (g5 S'\\\x8f\xc2\xf5(\xdb\xb7@' p3602 tp3603 Rp3604 g3552 F6894.0 tp3605 a(I7090000 g1 (g5 S')\\\x8f\xc2\xf5\xe0\xb7@' p3606 tp3607 Rp3608 g3552 F7050.0 tp3609 a(I7100000 g1 (g5 S'\xaeG\xe1z\x94\x12\xb8@' p3610 tp3611 Rp3612 g3552 F5410.0 tp3613 a(I7110000 g1 (g5 S'\xe1z\x14\xaeG\x0c\xb8@' p3614 tp3615 Rp3616 g3552 F4290.0 tp3617 a(I7120000 g1 (g5 S'\xf6(\\\x8f\xc2\x12\xb8@' p3618 tp3619 Rp3620 g3552 F6948.0 tp3621 a(I7130000 g1 (g5 S'{\x14\xaeG\xe1\x01\xb8@' p3622 tp3623 Rp3624 g3552 F6240.0 tp3625 a(I7140000 g1 (g5 S'R\xb8\x1e\x85k\x8a\xb7@' p3626 tp3627 Rp3628 g3552 F4110.0 tp3629 a(I7150000 g1 (g5 S'\\\x8f\xc2\xf5\xa8\xaa\xb7@' p3630 tp3631 Rp3632 g3552 F6224.0 tp3633 a(I7160000 g1 (g5 S'H\xe1z\x14.\x9a\xb7@' p3634 tp3635 Rp3636 g3552 F7776.0 tp3637 a(I7170000 g1 (g5 S'\xd7\xa3p=\x8a\xa2\xb7@' p3638 tp3639 Rp3640 g3552 F5280.0 tp3641 a(I7180000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xa1\xb7@' p3642 tp3643 Rp3644 g3552 F6892.0 tp3645 a(I7190000 g1 (g5 S'3333\xb3\xc0\xb7@' p3646 tp3647 Rp3648 g3552 F9220.0 tp3649 a(I7200000 g1 (g5 S'\xaeG\xe1z\x14\xa0\xb7@' p3650 tp3651 Rp3652 g3552 F4860.0 tp3653 a(I7210000 g1 (g5 S'R\xb8\x1e\x85k\x83\xb7@' p3654 tp3655 Rp3656 g3552 F3570.0 tp3657 a(I7220000 g1 (g5 S'\x85\xebQ\xb8\x9e\xb4\xb7@' p3658 tp3659 Rp3660 g3552 F11358.0 tp3661 a(I7230000 g1 (g5 S'\\\x8f\xc2\xf5\xa8\xd5\xb7@' p3662 tp3663 Rp3664 g3552 F7068.0 tp3665 a(I7240000 g1 (g5 S'\xecQ\xb8\x1e\x05\xd3\xb7@' p3666 tp3667 Rp3668 g3552 F6478.0 tp3669 a(I7250000 g1 (g5 S'\xb8\x1e\x85\xebQ\xc8\xb7@' p3670 tp3671 Rp3672 g3552 F7476.0 tp3673 a(I7260000 g1 (g5 S'R\xb8\x1e\x85k\xe3\xb7@' p3674 tp3675 Rp3676 g3552 F6168.0 tp3677 a(I7270000 g1 (g5 S'{\x14\xaeG\xe1\xcf\xb7@' p3678 tp3679 Rp3680 g3552 F7986.0 tp3681 a(I7280000 g1 (g5 S'\xb8\x1e\x85\xebQ\xf1\xb7@' p3682 tp3683 Rp3684 g3552 F7818.0 tp3685 a(I7290000 g1 (g5 S'\xd7\xa3p=\x8a\xf3\xb7@' p3686 tp3687 Rp3688 g3552 F7224.0 tp3689 a(I7300000 g1 (g5 S'\xd7\xa3p=\n\xf4\xb7@' p3690 tp3691 Rp3692 g3552 F4898.0 tp3693 a(I7310000 g1 (g5 S'\x85\xebQ\xb8\x1e\xf7\xb7@' p3694 tp3695 Rp3696 g3552 F7050.0 tp3697 a(I7320000 g1 (g5 S'H\xe1z\x14\xae\xec\xb7@' p3698 tp3699 Rp3700 g3552 F6894.0 tp3701 a(I7330000 g1 (g5 S'R\xb8\x1e\x85\xeb\x0c\xb8@' p3702 tp3703 Rp3704 g3552 F9044.0 tp3705 a(I7340000 g1 (g5 S'H\xe1z\x14\xae(\xb8@' p3706 tp3707 Rp3708 g3552 F10040.0 tp3709 a(I7350000 g1 (g5 S'\x14\xaeG\xe1\xfa\x13\xb8@' p3710 tp3711 Rp3712 g3552 F6100.0 tp3713 a(I7360000 g1 (g5 S'\xecQ\xb8\x1e\x85\xea\xb7@' p3714 tp3715 Rp3716 g3552 F5182.0 tp3717 a(I7370000 g1 (g5 S'33333\xdc\xb7@' p3718 tp3719 Rp3720 g3552 F4414.0 tp3721 a(I7380000 g1 (g5 S'\x85\xebQ\xb8\x1e\xd7\xb7@' p3722 tp3723 Rp3724 g3552 F6978.0 tp3725 a(I7390000 g1 (g5 S'\xd7\xa3p=\x8a\xd5\xb7@' p3726 tp3727 Rp3728 g3552 F6262.0 tp3729 a(I7400000 g1 (g5 S'3333\xb3\xdf\xb7@' p3730 tp3731 Rp3732 g3552 F8820.0 tp3733 a(I7410000 g1 (g5 S'\xd7\xa3p=\x8a\xb7\xb7@' p3734 tp3735 Rp3736 g3552 F4092.0 tp3737 a(I7420000 g1 (g5 S'\xecQ\xb8\x1e\x85\xe3\xb7@' p3738 tp3739 Rp3740 g3552 F9288.0 tp3741 a(I7430000 g1 (g5 S'\xe1z\x14\xaeG\xcb\xb7@' p3742 tp3743 Rp3744 g3552 F6704.0 tp3745 a(I7440000 g1 (g5 S'\xecQ\xb8\x1e\x05\xd9\xb7@' p3746 tp3747 Rp3748 g3552 F8346.0 tp3749 a(I7450000 g1 (g5 S'\xecQ\xb8\x1e\x85\xd4\xb7@' p3750 tp3751 Rp3752 g3552 F4962.0 tp3753 a(I7460000 g1 (g5 S'\x14\xaeG\xe1\xfa\xbd\xb7@' p3754 tp3755 Rp3756 g3552 F5310.0 tp3757 a(I7470000 g1 (g5 S'\xaeG\xe1z\x94\xc5\xb7@' p3758 tp3759 Rp3760 g3552 F6330.0 tp3761 a(I7480000 g1 (g5 S'3333\xb3\xcf\xb7@' p3762 tp3763 Rp3764 g3552 F5182.0 tp3765 a(I7490000 g1 (g5 S'H\xe1z\x14.\xa8\xb7@' p3766 tp3767 Rp3768 g3552 F2496.0 tp3769 a(I7500000 g1 (g5 S'\xf6(\\\x8f\xc2\xab\xb7@' p3770 tp3771 Rp3772 g3552 F6534.0 tp3773 a(I7510000 g1 (g5 S'H\xe1z\x14\xae\xed\xb7@' p3774 tp3775 Rp3776 g3552 F7200.0 tp3777 a(I7520000 g1 (g5 S'\x9a\x99\x99\x99\x19\xd2\xb7@' p3778 tp3779 Rp3780 g3552 F4734.0 tp3781 a(I7530000 g1 (g5 S'\x1f\x85\xebQ\xb8\xe7\xb7@' p3782 tp3783 Rp3784 g3552 F1380.0 tp3785 a(I7540000 g1 (g5 S'\x8f\xc2\xf5(\\7\xb8@' p3786 tp3787 Rp3788 g3552 F10610.0 tp3789 a(I7550000 g1 (g5 S'\xd7\xa3p=\x8aT\xb8@' p3790 tp3791 Rp3792 g3552 F8166.0 tp3793 a(I7560000 g1 (g5 S'\x14\xaeG\xe1zn\xb8@' p3794 tp3795 Rp3796 g3552 F8940.0 tp3797 a(I7570000 g1 (g5 S'\n\xd7\xa3p=\xa6\xb8@' p3798 tp3799 Rp3800 g3552 F5054.0 tp3801 a(I7580000 g1 (g5 S'\x1f\x85\xebQ\xb8\xc4\xb8@' p3802 tp3803 Rp3804 g1 (g5 S'\xd7\xa3p=\n\xce\xb8@' p3805 tp3806 Rp3807 F4764.0 tp3808 a(I7590000 g1 (g5 S'\xe1z\x14\xae\xc7\xb3\xb8@' p3809 tp3810 Rp3811 g3807 F7416.0 tp3812 a(I7600000 g1 (g5 S'\xe1z\x14\xaeG\xfd\xb8@' p3813 tp3814 Rp3815 g1 (g5 S'\xe1z\x14\xaeG\xfd\xb8@' p3816 tp3817 Rp3818 F10350.0 tp3819 a(I7610000 g1 (g5 S'\xc3\xf5(\\\x8f;\xb9@' p3820 tp3821 Rp3822 g1 (g5 S'\xc3\xf5(\\\x8f;\xb9@' p3823 tp3824 Rp3825 F6618.0 tp3826 a(I7620000 g1 (g5 S'=\n\xd7\xa3\xf0?\xb9@' p3827 tp3828 Rp3829 g1 (g5 S'=\n\xd7\xa3\xf0?\xb9@' p3830 tp3831 Rp3832 F8022.0 tp3833 a(I7630000 g1 (g5 S'\xa4p=\n\xd7[\xb9@' p3834 tp3835 Rp3836 g1 (g5 S'\xa4p=\n\xd7[\xb9@' p3837 tp3838 Rp3839 F7652.0 tp3840 a(I7640000 g1 (g5 S'\x1f\x85\xebQ\xb8\x8e\xb9@' p3841 tp3842 Rp3843 g1 (g5 S'\x1f\x85\xebQ\xb8\x8e\xb9@' p3844 tp3845 Rp3846 F5880.0 tp3847 a(I7650000 g1 (g5 S'R\xb8\x1e\x85\xeb\x9e\xb9@' p3848 tp3849 Rp3850 g1 (g5 S'R\xb8\x1e\x85\xeb\x9e\xb9@' p3851 tp3852 Rp3853 F9960.0 tp3854 a(I7660000 g1 (g5 S'\xe1z\x14\xae\xc7\x81\xb9@' p3855 tp3856 Rp3857 g3853 F4220.0 tp3858 a(I7670000 g1 (g5 S'\x85\xebQ\xb8\x9e~\xb9@' p3859 tp3860 Rp3861 g3853 F7320.0 tp3862 a(I7680000 g1 (g5 S'\\\x8f\xc2\xf5\xa8k\xb9@' p3863 tp3864 Rp3865 g3853 F5340.0 tp3866 a(I7690000 g1 (g5 S'\xc3\xf5(\\\x8f\xbc\xb9@' p3867 tp3868 Rp3869 g1 (g5 S'\xc3\xf5(\\\x8f\xbc\xb9@' p3870 tp3871 Rp3872 F12696.0 tp3873 a(I7700000 g1 (g5 S'\xa4p=\n\xd7\xc0\xb9@' p3874 tp3875 Rp3876 g1 (g5 S'\xecQ\xb8\x1e\x85\xcb\xb9@' p3877 tp3878 Rp3879 F5880.0 tp3880 a(I7710000 g1 (g5 S'\x00\x00\x00\x00\x00\x05\xba@' p3881 tp3882 Rp3883 g1 (g5 S'\x00\x00\x00\x00\x00\x05\xba@' p3884 tp3885 Rp3886 F8736.0 tp3887 a(I7720000 g1 (g5 S'R\xb8\x1e\x85k#\xba@' p3888 tp3889 Rp3890 g1 (g5 S'R\xb8\x1e\x85k#\xba@' p3891 tp3892 Rp3893 F9282.0 tp3894 a(I7730000 g1 (g5 S'\x14\xaeG\xe1z\xc1\xba@' p3895 tp3896 Rp3897 g1 (g5 S'\x14\xaeG\xe1z\xc1\xba@' p3898 tp3899 Rp3900 F5280.0 tp3901 a(I7740000 g1 (g5 S'\xb8\x1e\x85\xebQ*\xbb@' p3902 tp3903 Rp3904 g1 (g5 S'\xb8\x1e\x85\xebQ*\xbb@' p3905 tp3906 Rp3907 F14594.0 tp3908 a(I7750000 g1 (g5 S'H\xe1z\x14\xae>\xbb@' p3909 tp3910 Rp3911 g1 (g5 S'H\xe1z\x14\xae>\xbb@' p3912 tp3913 Rp3914 F8260.0 tp3915 a(I7760000 g1 (g5 S"\xcd\xcc\xcc\xcc\xcc'\xbb@" p3916 tp3917 Rp3918 g1 (g5 S'\x8f\xc2\xf5(\\S\xbb@' p3919 tp3920 Rp3921 F3420.0 tp3922 a(I7770000 g1 (g5 S'\x1f\x85\xebQ8>\xbb@' p3923 tp3924 Rp3925 g3921 F5676.0 tp3926 a(I7780000 g1 (g5 S'\xe1z\x14\xaeGo\xbb@' p3927 tp3928 Rp3929 g1 (g5 S'\xe1z\x14\xaeGo\xbb@' p3930 tp3931 Rp3932 F12068.0 tp3933 a(I7790000 g1 (g5 S'\x85\xebQ\xb8\x1e^\xbb@' p3934 tp3935 Rp3936 g1 (g5 S'\x14\xaeG\xe1zq\xbb@' p3937 tp3938 Rp3939 F7284.0 tp3940 a(I7800000 g1 (g5 S'\xd7\xa3p=\n{\xbb@' p3941 tp3942 Rp3943 g1 (g5 S'\xd7\xa3p=\n{\xbb@' p3944 tp3945 Rp3946 F9636.0 tp3947 a(I7810000 g1 (g5 S'\n\xd7\xa3p=\xa3\xbb@' p3948 tp3949 Rp3950 g1 (g5 S'\n\xd7\xa3p=\xa3\xbb@' p3951 tp3952 Rp3953 F4860.0 tp3954 a(I7820000 g1 (g5 S'=\n\xd7\xa3p\xbf\xbb@' p3955 tp3956 Rp3957 g1 (g5 S'=\n\xd7\xa3p\xbf\xbb@' p3958 tp3959 Rp3960 F6390.0 tp3961 a(I7830000 g1 (g5 S'q=\n\xd7\xa3v\xbb@' p3962 tp3963 Rp3964 g3960 F6028.0 tp3965 a(I7840000 g1 (g5 S'\n\xd7\xa3p\xbdZ\xbb@' p3966 tp3967 Rp3968 g3960 F4156.0 tp3969 a(I7850000 g1 (g5 S'\xb8\x1e\x85\xebQW\xbb@' p3970 tp3971 Rp3972 g3960 F6888.0 tp3973 a(I7860000 g1 (g5 S'H\xe1z\x14./\xbb@' p3974 tp3975 Rp3976 g3960 F4796.0 tp3977 a(I7870000 g1 (g5 S'\x1f\x85\xebQ8n\xbb@' p3978 tp3979 Rp3980 g3960 F12472.0 tp3981 a(I7880000 g1 (g5 S'\x00\x00\x00\x00\x00m\xbb@' p3982 tp3983 Rp3984 g3960 F8164.0 tp3985 a(I7890000 g1 (g5 S'\x9a\x99\x99\x99\x196\xbb@' p3986 tp3987 Rp3988 g3960 F4956.0 tp3989 a(I7900000 g1 (g5 S'\xa4p=\nW.\xbb@' p3990 tp3991 Rp3992 g3960 F5182.0 tp3993 a(I7910000 g1 (g5 S'\xb8\x1e\x85\xebQ\x19\xbb@' p3994 tp3995 Rp3996 g3960 F2664.0 tp3997 a(I7920000 g1 (g5 S'\x85\xebQ\xb8\x9e.\xbb@' p3998 tp3999 Rp4000 g3960 F9720.0 tp4001 a(I7930000 g1 (g5 S'\n\xd7\xa3p=0\xbb@' p4002 tp4003 Rp4004 g3960 F6390.0 tp4005 a(I7940000 g1 (g5 S'{\x14\xaeGa\xfc\xba@' p4006 tp4007 Rp4008 g3960 F4860.0 tp4009 a(I7950000 g1 (g5 S'R\xb8\x1e\x85k\xf5\xba@' p4010 tp4011 Rp4012 g3960 F5404.0 tp4013 a(I7960000 g1 (g5 S'\xf6(\\\x8fB\x14\xbb@' p4014 tp4015 Rp4016 g3960 F7112.0 tp4017 a(I7970000 g1 (g5 S'ffff\xe6:\xbb@' p4018 tp4019 Rp4020 g3960 F6644.0 tp4021 a(I7980000 g1 (g5 S'\xf6(\\\x8fB7\xbb@' p4022 tp4023 Rp4024 g3960 F5148.0 tp4025 a(I7990000 g1 (g5 S'q=\n\xd7#\x1e\xbb@' p4026 tp4027 Rp4028 g3960 F6292.0 tp4029 a(I8000000 g1 (g5 S'\xc3\xf5(\\\x8f\x15\xbb@' p4030 tp4031 Rp4032 g3960 F5024.0 tp4033 a(I8010000 g1 (g5 S'\xf6(\\\x8f\xc24\xbb@' p4034 tp4035 Rp4036 g3960 F7212.0 tp4037 a(I8020000 g1 (g5 S'{\x14\xaeGa\x03\xbb@' p4038 tp4039 Rp4040 g3960 F5200.0 tp4041 a(I8030000 g1 (g5 S'R\xb8\x1e\x85kT\xbb@' p4042 tp4043 Rp4044 g3960 F9030.0 tp4045 a(I8040000 g1 (g5 S'\xf6(\\\x8fB\x03\xbb@' p4046 tp4047 Rp4048 g3960 F6840.0 tp4049 a(I8050000 g1 (g5 S'\x8f\xc2\xf5(\\>\xbb@' p4050 tp4051 Rp4052 g3960 F10872.0 tp4053 a(I8060000 g1 (g5 S'fffffk\xbb@' p4054 tp4055 Rp4056 g3960 F8916.0 tp4057 a(I8070000 g1 (g5 S'\xe1z\x14\xae\xc7\x8e\xbb@' p4058 tp4059 Rp4060 g3960 F6508.0 tp4061 a(I8080000 g1 (g5 S'H\xe1z\x14\xae\xc1\xbb@' p4062 tp4063 Rp4064 g1 (g5 S'H\xe1z\x14\xae\xc1\xbb@' p4065 tp4066 Rp4067 F6732.0 tp4068 a(I8090000 g1 (g5 S'\x00\x00\x00\x00\x80\xab\xbb@' p4069 tp4070 Rp4071 g4067 F5312.0 tp4072 a(I8100000 g1 (g5 S'\n\xd7\xa3p=\xfb\xbb@' p4073 tp4074 Rp4075 g1 (g5 S'\n\xd7\xa3p=\xfb\xbb@' p4076 tp4077 Rp4078 F5216.0 tp4079 a(I8110000 g1 (g5 S'\xa4p=\n\xd7\xb8\xbb@' p4080 tp4081 Rp4082 g4078 F4348.0 tp4083 a(I8120000 g1 (g5 S'33333\xb6\xbb@' p4084 tp4085 Rp4086 g4078 F6936.0 tp4087 a(I8130000 g1 (g5 S'\x1f\x85\xebQ8\xb6\xbb@' p4088 tp4089 Rp4090 g4078 F4700.0 tp4091 a(I8140000 g1 (g5 S'\xecQ\xb8\x1e\x05\xf1\xbb@' p4092 tp4093 Rp4094 g4078 F6996.0 tp4095 a(I8150000 g1 (g5 S'\xecQ\xb8\x1e\x05\xc7\xbb@' p4096 tp4097 Rp4098 g4078 F6410.0 tp4099 a(I8160000 g1 (g5 S'\n\xd7\xa3p=\x9e\xbb@' p4100 tp4101 Rp4102 g4078 F5132.0 tp4103 a(I8170000 g1 (g5 S'\x85\xebQ\xb8\x9e\x96\xbb@' p4104 tp4105 Rp4106 g4078 F6840.0 tp4107 a(I8180000 g1 (g5 S'q=\n\xd7\xa3\x99\xbb@' p4108 tp4109 Rp4110 g4078 F5246.0 tp4111 a(I8190000 g1 (g5 S'\x85\xebQ\xb8\x1e\x9c\xbb@' p4112 tp4113 Rp4114 g4078 F6024.0 tp4115 a(I8200000 g1 (g5 S'\x1f\x85\xebQ\xb8v\xbb@' p4116 tp4117 Rp4118 g4078 F6610.0 tp4119 a(I8210000 g1 (g5 S'3333\xb3\x86\xbb@' p4120 tp4121 Rp4122 g4078 F8024.0 tp4123 a(I8220000 g1 (g5 S'\x9a\x99\x99\x99\x19\xb6\xbb@' p4124 tp4125 Rp4126 g4078 F12030.0 tp4127 a(I8230000 g1 (g5 S'\xecQ\xb8\x1e\x85\xbc\xbb@' p4128 tp4129 Rp4130 g4078 F7476.0 tp4131 a(I8240000 g1 (g5 S'\\\x8f\xc2\xf5\xa8\xc9\xbb@' p4132 tp4133 Rp4134 g4078 F7614.0 tp4135 a(I8250000 g1 (g5 S'\xe1z\x14\xae\xc7\xa5\xbb@' p4136 tp4137 Rp4138 g4078 F8032.0 tp4139 a(I8260000 g1 (g5 S'\xe1z\x14\xae\xc7\xf8\xbb@' p4140 tp4141 Rp4142 g4078 F10630.0 tp4143 a(I8270000 g1 (g5 S'{\x14\xaeGa\x06\xbc@' p4144 tp4145 Rp4146 g1 (g5 S'{\x14\xaeGa\x06\xbc@' p4147 tp4148 Rp4149 F7410.0 tp4150 a(I8280000 g1 (g5 S'\x14\xaeG\xe1\xfa\x1e\xbc@' p4151 tp4152 Rp4153 g1 (g5 S'\x14\xaeG\xe1\xfa\x1e\xbc@' p4154 tp4155 Rp4156 F9780.0 tp4157 a(I8290000 g1 (g5 S'\xd7\xa3p=\nm\xbc@' p4158 tp4159 Rp4160 g1 (g5 S'\xd7\xa3p=\nm\xbc@' p4161 tp4162 Rp4163 F7486.0 tp4164 a(I8300000 g1 (g5 S'\xe1z\x14\xae\xc7]\xbc@' p4165 tp4166 Rp4167 g4163 F11170.0 tp4168 a(I8310000 g1 (g5 S'\x1f\x85\xebQ\xb8\x86\xbc@' p4169 tp4170 Rp4171 g1 (g5 S'\x1f\x85\xebQ\xb8\x86\xbc@' p4172 tp4173 Rp4174 F9880.0 tp4175 a(I8320000 g1 (g5 S'\xa4p=\nW\xe6\xbc@' p4176 tp4177 Rp4178 g1 (g5 S'{\x14\xaeG\xe1\xe6\xbc@' p4179 tp4180 Rp4181 F6186.0 tp4182 a(I8330000 g1 (g5 S'\x1f\x85\xebQ\xb8\xad\xbc@' p4183 tp4184 Rp4185 g4181 F7080.0 tp4186 a(I8340000 g1 (g5 S'33333\x83\xbc@' p4187 tp4188 Rp4189 g4181 F10150.0 tp4190 a(I8350000 g1 (g5 S'\x00\x00\x00\x00\x00\xc2\xbc@' p4191 tp4192 Rp4193 g4181 F11560.0 tp4194 a(I8360000 g1 (g5 S'\\\x8f\xc2\xf5(\x92\xbc@' p4195 tp4196 Rp4197 g4181 F9810.0 tp4198 a(I8370000 g1 (g5 S'\xe1z\x14\xae\xc7\xd5\xbc@' p4199 tp4200 Rp4201 g4181 F5934.0 tp4202 a(I8380000 g1 (g5 S'H\xe1z\x14\xae\xf7\xbc@' p4203 tp4204 Rp4205 g1 (g5 S'H\xe1z\x14\xae\xf7\xbc@' p4206 tp4207 Rp4208 F10368.0 tp4209 a(I8390000 g1 (g5 S'\x14\xaeG\xe1\xfa\x12\xbd@' p4210 tp4211 Rp4212 g1 (g5 S'\x14\xaeG\xe1\xfa\x12\xbd@' p4213 tp4214 Rp4215 F8406.0 tp4216 a(I8400000 g1 (g5 S'\x9a\x99\x99\x99\x99\xfb\xbc@' p4217 tp4218 Rp4219 g4215 F7492.0 tp4220 a(I8410000 g1 (g5 S'\x9a\x99\x99\x99\x19\xd0\xbc@' p4221 tp4222 Rp4223 g4215 F5790.0 tp4224 a(I8420000 g1 (g5 S'\x9a\x99\x99\x99\x19\xe1\xbc@' p4225 tp4226 Rp4227 g4215 F8316.0 tp4228 a(I8430000 g1 (g5 S')\\\x8f\xc2\xf5\xee\xbc@' p4229 tp4230 Rp4231 g4215 F7776.0 tp4232 a(I8440000 g1 (g5 S'=\n\xd7\xa3\xf0\xc5\xbc@' p4233 tp4234 Rp4235 g4215 F1588.0 tp4236 a(I8450000 g1 (g5 S'\xe1z\x14\xaeG\t\xbd@' p4237 tp4238 Rp4239 g4215 F4350.0 tp4240 a(I8460000 g1 (g5 S'\xf6(\\\x8fB\xf5\xbc@' p4241 tp4242 Rp4243 g4215 F4320.0 tp4244 a(I8470000 g1 (g5 S'\x8f\xc2\xf5(\\\xce\xbc@' p4245 tp4246 Rp4247 g4215 F6644.0 tp4248 a(I8480000 g1 (g5 S'\xb8\x1e\x85\xeb\xd1\xb5\xbc@' p4249 tp4250 Rp4251 g4215 F9468.0 tp4252 a(I8490000 g1 (g5 S'\xc3\xf5(\\\x0f\x01\xbd@' p4253 tp4254 Rp4255 g4215 F11424.0 tp4256 a(I8500000 g1 (g5 S'{\x14\xaeGa>\xbd@' p4257 tp4258 Rp4259 g1 (g5 S'{\x14\xaeGa>\xbd@' p4260 tp4261 Rp4262 F11088.0 tp4263 a(I8510000 g1 (g5 S'\x8f\xc2\xf5(\xdc=\xbd@' p4264 tp4265 Rp4266 g4262 F6720.0 tp4267 a(I8520000 g1 (g5 S'fffffU\xbd@' p4268 tp4269 Rp4270 g1 (g5 S'fffffU\xbd@' p4271 tp4272 Rp4273 F2888.0 tp4274 a(I8530000 g1 (g5 S')\\\x8f\xc2u\xfe\xbc@' p4275 tp4276 Rp4277 g4273 F6194.0 tp4278 a(I8540000 g1 (g5 S'\xecQ\xb8\x1e\x85\x01\xbd@' p4279 tp4280 Rp4281 g4273 F6606.0 tp4282 a(I8550000 g1 (g5 S'\xcd\xcc\xcc\xccL\x12\xbd@' p4283 tp4284 Rp4285 g4273 F6538.0 tp4286 a(I8560000 g1 (g5 S"\x1f\x85\xebQ\xb8'\xbd@" p4287 tp4288 Rp4289 g4273 F7866.0 tp4290 a(I8570000 g1 (g5 S'R\xb8\x1e\x85k\xfe\xbc@' p4291 tp4292 Rp4293 g4273 F7860.0 tp4294 a(I8580000 g1 (g5 S'\x85\xebQ\xb8\x1e\x13\xbd@' p4295 tp4296 Rp4297 g4273 F7950.0 tp4298 a(I8590000 g1 (g5 S'33333 \xbd@' p4299 tp4300 Rp4301 g4273 F6456.0 tp4302 a(I8600000 g1 (g5 S'\x85\xebQ\xb8\x1e\x7f\xbd@' p4303 tp4304 Rp4305 g1 (g5 S'\x85\xebQ\xb8\x1e\x7f\xbd@' p4306 tp4307 Rp4308 F10130.0 tp4309 a(I8610000 g1 (g5 S'\x00\x00\x00\x00\x80\x93\xbd@' p4310 tp4311 Rp4312 g1 (g5 S'\x00\x00\x00\x00\x80\x93\xbd@' p4313 tp4314 Rp4315 F9580.0 tp4316 a(I8620000 g1 (g5 S'33333\x88\xbd@' p4317 tp4318 Rp4319 g1 (g5 S'\x14\xaeG\xe1z\xa5\xbd@' p4320 tp4321 Rp4322 F4284.0 tp4323 a(I8630000 g1 (g5 S'\xa4p=\n\xd7\xaa\xbd@' p4324 tp4325 Rp4326 g1 (g5 S'\xa4p=\n\xd7\xaa\xbd@' p4327 tp4328 Rp4329 F9320.0 tp4330 a(I8640000 g1 (g5 S'\xb8\x1e\x85\xeb\xd1\x9d\xbd@' p4331 tp4332 Rp4333 g4329 F6636.0 tp4334 a(I8650000 g1 (g5 S'\x1f\x85\xebQ8\x8b\xbd@' p4335 tp4336 Rp4337 g4329 F7170.0 tp4338 a(I8660000 g1 (g5 S'H\xe1z\x14.\x02\xbe@' p4339 tp4340 Rp4341 g1 (g5 S'H\xe1z\x14.\x02\xbe@' p4342 tp4343 Rp4344 F9540.0 tp4345 a(I8670000 g1 (g5 S'\xc3\xf5(\\\x8f\x00\xbe@' p4346 tp4347 Rp4348 g4344 F6678.0 tp4349 a(I8680000 g1 (g5 S'\x85\xebQ\xb8\x9e\xfc\xbd@' p4350 tp4351 Rp4352 g4344 F9800.0 tp4353 a(I8690000 g1 (g5 S'\x1f\x85\xebQ\xb8\xc1\xbd@' p4354 tp4355 Rp4356 g4344 F5268.0 tp4357 a(I8700000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb9\xbd@' p4358 tp4359 Rp4360 g4344 F8022.0 tp4361 a(I8710000 g1 (g5 S'\xb8\x1e\x85\xebQ\xc3\xbd@' p4362 tp4363 Rp4364 g4344 F6312.0 tp4365 a(I8720000 g1 (g5 S'\xe1z\x14\xae\xc7\xbf\xbd@' p4366 tp4367 Rp4368 g4344 F10030.0 tp4369 a(I8730000 g1 (g5 S')\\\x8f\xc2u\xcc\xbd@' p4370 tp4371 Rp4372 g4344 F5744.0 tp4373 a(I8740000 g1 (g5 S'\xd7\xa3p=\x8a\x05\xbe@' p4374 tp4375 Rp4376 g1 (g5 S'\xd7\xa3p=\x8a\x05\xbe@' p4377 tp4378 Rp4379 F10602.0 tp4380 a(I8750000 g1 (g5 S'=\n\xd7\xa3\xf0\xf5\xbd@' p4381 tp4382 Rp4383 g4379 F9360.0 tp4384 a(I8760000 g1 (g5 S'fffff\xef\xbd@' p4385 tp4386 Rp4387 g4379 F7470.0 tp4388 a(I8770000 g1 (g5 S'\xd7\xa3p=\n\xe5\xbd@' p4389 tp4390 Rp4391 g4379 F5896.0 tp4392 a(I8780000 g1 (g5 S'\x00\x00\x00\x00\x80\xcf\xbd@' p4393 tp4394 Rp4395 g4379 F5756.0 tp4396 a(I8790000 g1 (g5 S'\xc3\xf5(\\\x8f\xdf\xbd@' p4397 tp4398 Rp4399 g4379 F7224.0 tp4400 a(I8800000 g1 (g5 S'\x1f\x85\xebQ8\x1a\xbe@' p4401 tp4402 Rp4403 g1 (g5 S'\x1f\x85\xebQ8\x1a\xbe@' p4404 tp4405 Rp4406 F15274.0 tp4407 a(I8810000 g1 (g5 S'\xe1z\x14\xae\xc7\x18\xbe@' p4408 tp4409 Rp4410 g4406 F6232.0 tp4411 a(I8820000 g1 (g5 S'3333\xb3\x19\xbe@' p4412 tp4413 Rp4414 g1 (g5 S"\x00\x00\x00\x00\x00'\xbe@" p4415 tp4416 Rp4417 F5280.0 tp4418 a(I8830000 g1 (g5 S'q=\n\xd7\xa3\x16\xbe@' p4419 tp4420 Rp4421 g1 (g5 S'fffff2\xbe@' p4422 tp4423 Rp4424 F5248.0 tp4425 a(I8840000 g1 (g5 S'\x85\xebQ\xb8\x9e\xe0\xbd@' p4426 tp4427 Rp4428 g4424 F6628.0 tp4429 a(I8850000 g1 (g5 S'fffff\x11\xbe@' p4430 tp4431 Rp4432 g4424 F9464.0 tp4433 a(I8860000 g1 (g5 S'H\xe1z\x14.\xdc\xbd@' p4434 tp4435 Rp4436 g4424 F5132.0 tp4437 a(I8870000 g1 (g5 S'\xf6(\\\x8fB\xee\xbd@' p4438 tp4439 Rp4440 g4424 F9840.0 tp4441 a(I8880000 g1 (g5 S'{\x14\xaeG\xe1\xf7\xbd@' p4442 tp4443 Rp4444 g4424 F10230.0 tp4445 a(I8890000 g1 (g5 S'R\xb8\x1e\x85\xeb\xd8\xbd@' p4446 tp4447 Rp4448 g4424 F8362.0 tp4449 a(I8900000 g1 (g5 S')\\\x8f\xc2\xf5q\xbd@' p4450 tp4451 Rp4452 g4424 F4110.0 tp4453 a(I8910000 g1 (g5 S'\xaeG\xe1z\x14:\xbd@' p4454 tp4455 Rp4456 g4424 F7704.0 tp4457 a(I8920000 g1 (g5 S'\n\xd7\xa3p=\xf6\xbc@' p4458 tp4459 Rp4460 g4424 F7398.0 tp4461 a(I8930000 g1 (g5 S'q=\n\xd7#\xf2\xbc@' p4462 tp4463 Rp4464 g4424 F3600.0 tp4465 a(I8940000 g1 (g5 S'\xf6(\\\x8fB\xc5\xbc@' p4466 tp4467 Rp4468 g4424 F5662.0 tp4469 a(I8950000 g1 (g5 S'\x8f\xc2\xf5(\xdc\xae\xbc@' p4470 tp4471 Rp4472 g4424 F11300.0 tp4473 a(I8960000 g1 (g5 S'fffff<\xbc@' p4474 tp4475 Rp4476 g4424 F2160.0 tp4477 a(I8970000 g1 (g5 S'\x8f\xc2\xf5(\\\xc6\xbc@' p4478 tp4479 Rp4480 g4424 F19730.0 tp4481 a(I8980000 g1 (g5 S'q=\n\xd7#\xac\xbc@' p4482 tp4483 Rp4484 g4424 F6292.0 tp4485 a(I8990000 g1 (g5 S'ffff\xe6\xa6\xbc@' p4486 tp4487 Rp4488 g4424 F8826.0 tp4489 a(I9000000 g1 (g5 S'\x00\x00\x00\x00\x00\x92\xbc@' p4490 tp4491 Rp4492 g4424 F6516.0 tp4493 a(I9010000 g1 (g5 S'\xe1z\x14\xaeG\xa6\xbc@' p4494 tp4495 Rp4496 g4424 F7818.0 tp4497 a(I9020000 g1 (g5 S'\x85\xebQ\xb8\x9e\xd5\xbc@' p4498 tp4499 Rp4500 g4424 F6266.0 tp4501 a(I9030000 g1 (g5 S'R\xb8\x1e\x85\xeb\xcc\xbc@' p4502 tp4503 Rp4504 g4424 F6906.0 tp4505 a(I9040000 g1 (g5 S'\xa4p=\nWJ\xbd@' p4506 tp4507 Rp4508 g4424 F5540.0 tp4509 a(I9050000 g1 (g5 S'\xb8\x1e\x85\xebQ*\xbd@' p4510 tp4511 Rp4512 g4424 F8592.0 tp4513 a(I9060000 g1 (g5 S'\x14\xaeG\xe1z3\xbd@' p4514 tp4515 Rp4516 g4424 F8764.0 tp4517 a(I9070000 g1 (g5 S'\\\x8f\xc2\xf5(R\xbd@' p4518 tp4519 Rp4520 g4424 F5602.0 tp4521 a(I9080000 g1 (g5 S'=\n\xd7\xa3p\xb5\xbd@' p4522 tp4523 Rp4524 g4424 F16572.0 tp4525 a(I9090000 g1 (g5 S'\xa4p=\n\xd7\xe4\xbd@' p4526 tp4527 Rp4528 g4424 F8704.0 tp4529 a(I9100000 g1 (g5 S'\xecQ\xb8\x1e\x85\x9a\xbd@' p4530 tp4531 Rp4532 g4424 F8178.0 tp4533 a(I9110000 g1 (g5 S'\\\x8f\xc2\xf5(n\xbd@' p4534 tp4535 Rp4536 g4424 F6652.0 tp4537 a(I9120000 g1 (g5 S'=\n\xd7\xa3p\xda\xbd@' p4538 tp4539 Rp4540 g4424 F12000.0 tp4541 a(I9130000 g1 (g5 S'\\\x8f\xc2\xf5(\xe3\xbd@' p4542 tp4543 Rp4544 g4424 F6810.0 tp4545 a(I9140000 g1 (g5 S'\x1f\x85\xebQ8\x07\xbe@' p4546 tp4547 Rp4548 g4424 F8148.0 tp4549 a(I9150000 g1 (g5 S'\x00\x00\x00\x00\x80\xfb\xbd@' p4550 tp4551 Rp4552 g4424 F7152.0 tp4553 a(I9160000 g1 (g5 S'\x1f\x85\xebQ\xb8*\xbe@' p4554 tp4555 Rp4556 g4424 F11328.0 tp4557 a(I9170000 g1 (g5 S')\\\x8f\xc2\xf5M\xbe@' p4558 tp4559 Rp4560 g1 (g5 S'\x1f\x85\xebQ\xb8]\xbe@' p4561 tp4562 Rp4563 F5216.0 tp4564 a(I9180000 g1 (g5 S'\xecQ\xb8\x1e\x85C\xbe@' p4565 tp4566 Rp4567 g4563 F6822.0 tp4568 a(I9190000 g1 (g5 S'\xc3\xf5(\\\x8fZ\xbe@' p4569 tp4570 Rp4571 g4563 F7920.0 tp4572 a(I9200000 g1 (g5 S'\xaeG\xe1z\x14[\xbe@' p4573 tp4574 Rp4575 g4563 F8002.0 tp4576 a(I9210000 g1 (g5 S'\x14\xaeG\xe1\xfa\x86\xbe@' p4577 tp4578 Rp4579 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x91\xbe@' p4580 tp4581 Rp4582 F9850.0 tp4583 a(I9220000 g1 (g5 S'\n\xd7\xa3p=\x7f\xbe@' p4584 tp4585 Rp4586 g4582 F9356.0 tp4587 a(I9230000 g1 (g5 S'ffff\xe6\x90\xbe@' p4588 tp4589 Rp4590 g4582 F11346.0 tp4591 a(I9240000 g1 (g5 S'\x00\x00\x00\x00\x00\xa5\xbe@' p4592 tp4593 Rp4594 g1 (g5 S'\x00\x00\x00\x00\x00\xa5\xbe@' p4595 tp4596 Rp4597 F8832.0 tp4598 a(I9250000 g1 (g5 S'\x85\xebQ\xb8\x1e\xd7\xbe@' p4599 tp4600 Rp4601 g1 (g5 S'\xf6(\\\x8fB\xeb\xbe@' p4602 tp4603 Rp4604 F5760.0 tp4605 a(I9260000 g1 (g5 S'\x8f\xc2\xf5(\\\x90\xbe@' p4606 tp4607 Rp4608 g4604 F4020.0 tp4609 a(I9270000 g1 (g5 S'\x1f\x85\xebQ\xb8\xa5\xbe@' p4610 tp4611 Rp4612 g4604 F6600.0 tp4613 a(I9280000 g1 (g5 S'\xf6(\\\x8f\xc2\x86\xbe@' p4614 tp4615 Rp4616 g4604 F6444.0 tp4617 a(I9290000 g1 (g5 S'fffff\x93\xbe@' p4618 tp4619 Rp4620 g4604 F7942.0 tp4621 a(I9300000 g1 (g5 S'q=\n\xd7#\xd4\xbe@' p4622 tp4623 Rp4624 g4604 F8208.0 tp4625 a(I9310000 g1 (g5 S'\xecQ\xb8\x1e\x05\xf3\xbe@' p4626 tp4627 Rp4628 g1 (g5 S'\xecQ\xb8\x1e\x05\xf3\xbe@' p4629 tp4630 Rp4631 F6384.0 tp4632 a(I9320000 g1 (g5 S')\\\x8f\xc2\xf5\xeb\xbe@' p4633 tp4634 Rp4635 g4631 F9266.0 tp4636 a(I9330000 g1 (g5 S'\xa4p=\n\xd7\xc9\xbe@' p4637 tp4638 Rp4639 g4631 F2832.0 tp4640 a(I9340000 g1 (g5 S'q=\n\xd7\xa3\x9a\xbe@' p4641 tp4642 Rp4643 g4631 F5310.0 tp4644 a(I9350000 g1 (g5 S'\x00\x00\x00\x00\x00\xf3\xbe@' p4645 tp4646 Rp4647 g1 (g5 S'\x9a\x99\x99\x99\x99\xf3\xbe@' p4648 tp4649 Rp4650 F6330.0 tp4651 a(I9360000 g1 (g5 S'33333\xc4\xbe@' p4652 tp4653 Rp4654 g4650 F5922.0 tp4655 a(I9370000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xf4\xbe@' p4656 tp4657 Rp4658 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xf4\xbe@' p4659 tp4660 Rp4661 F9090.0 tp4662 a(I9380000 g1 (g5 S'ffff\xe6\xf3\xbe@' p4663 tp4664 Rp4665 g4661 F7380.0 tp4666 a(I9390000 g1 (g5 S'=\n\xd7\xa3p/\xbf@' p4667 tp4668 Rp4669 g1 (g5 S'=\n\xd7\xa3p/\xbf@' p4670 tp4671 Rp4672 F7314.0 tp4673 a(I9400000 g1 (g5 S'\x8f\xc2\xf5(\xdcD\xbf@' p4674 tp4675 Rp4676 g1 (g5 S'\x8f\xc2\xf5(\xdcD\xbf@' p4677 tp4678 Rp4679 F7260.0 tp4680 a(I9410000 g1 (g5 S'3333\xb3#\xbf@' p4681 tp4682 Rp4683 g1 (g5 S'{\x14\xaeG\xe1[\xbf@' p4684 tp4685 Rp4686 F7820.0 tp4687 a(I9420000 g1 (g5 S'3333\xb3#\xbf@' p4688 tp4689 Rp4690 g4686 F7820.0 tp4691 a(I9430000 g1 (g5 S'=\n\xd7\xa3pP\xbf@' p4692 tp4693 Rp4694 g4686 F12060.0 tp4695 a(I9440000 g1 (g5 S'\\\x8f\xc2\xf5(\x97\xbf@' p4696 tp4697 Rp4698 g1 (g5 S'\\\x8f\xc2\xf5(\x97\xbf@' p4699 tp4700 Rp4701 F13304.0 tp4702 a(I9450000 g1 (g5 S'\xcd\xcc\xcc\xccL\xd3\xbf@' p4703 tp4704 Rp4705 g1 (g5 S'\xcd\xcc\xcc\xccL\xd3\xbf@' p4706 tp4707 Rp4708 F13460.0 tp4709 a(I9460000 g1 (g5 S'\x00\x00\x00\x00\x80\xfa\xbf@' p4710 tp4711 Rp4712 g1 (g5 S'\x00\x00\x00\x00\x80\xfa\xbf@' p4713 tp4714 Rp4715 F9200.0 tp4716 a(I9470000 g1 (g5 S'\x14\xaeG\xe1\xfa\x0c\xc0@' p4717 tp4718 Rp4719 g1 (g5 S'\x14\xaeG\xe1\xfa\x0c\xc0@' p4720 tp4721 Rp4722 F11088.0 tp4723 a(I9480000 g1 (g5 S'\xe1z\x14\xaeG\r\xc0@' p4724 tp4725 Rp4726 g1 (g5 S'ffff\xa6\x10\xc0@' p4727 tp4728 Rp4729 F5954.0 tp4730 a(I9490000 g1 (g5 S'=\n\xd7\xa3p\xe6\xbf@' p4731 tp4732 Rp4733 g4729 F5828.0 tp4734 a(I9500000 g1 (g5 S'H\xe1z\x14\xae\xfd\xbf@' p4735 tp4736 Rp4737 g4729 F4156.0 tp4738 a(I9510000 g1 (g5 S'\x8f\xc2\xf5(\xdc\xf0\xbf@' p4739 tp4740 Rp4741 g4729 F8518.0 tp4742 a(I9520000 g1 (g5 S'\x85\xebQ\xb8\x9e\xfe\xbf@' p4743 tp4744 Rp4745 g4729 F7488.0 tp4746 a(I9530000 g1 (g5 S'\x85\xebQ\xb8\x9e\xfe\xbf@' p4747 tp4748 Rp4749 g4729 F7488.0 tp4750 a(I9540000 g1 (g5 S'H\xe1z\x14\xae;\xc0@' p4751 tp4752 Rp4753 g1 (g5 S'H\xe1z\x14\xae;\xc0@' p4754 tp4755 Rp4756 F6096.0 tp4757 a(I9550000 g1 (g5 S'\xaeG\xe1z\xd48\xc0@' p4758 tp4759 Rp4760 g4756 F7188.0 tp4761 a(I9560000 g1 (g5 S'\xecQ\xb8\x1eEd\xc0@' p4762 tp4763 Rp4764 g1 (g5 S'\xecQ\xb8\x1eEd\xc0@' p4765 tp4766 Rp4767 F16392.0 tp4768 a(I9570000 g1 (g5 S'\x9a\x99\x99\x99\x99s\xc0@' p4769 tp4770 Rp4771 g1 (g5 S'\x1f\x85\xebQxt\xc0@' p4772 tp4773 Rp4774 F7224.0 tp4775 a(I9580000 g1 (g5 S'\x14\xaeG\xe1\xfa\x91\xbf@' p4776 tp4777 Rp4778 g4774 F352.0 tp4779 a(I9590000 g1 (g5 S'\xaeG\xe1z\x14\xe9\xbd@' p4780 tp4781 Rp4782 g4774 F440.0 tp4783 a(I9600000 g1 (g5 S'\x14\xaeG\xe1zn\xbd@' p4784 tp4785 Rp4786 g4774 F852.0 tp4787 a(I9610000 g1 (g5 S')\\\x8f\xc2u\xf2\xbc@' p4788 tp4789 Rp4790 g4774 F4542.0 tp4791 a(I9620000 g1 (g5 S'\xd7\xa3p=\x8a\xaf\xbc@' p4792 tp4793 Rp4794 g4774 F4320.0 tp4795 a(I9630000 g1 (g5 S'=\n\xd7\xa3\xf0}\xbc@' p4796 tp4797 Rp4798 g4774 F6028.0 tp4799 a(I9640000 g1 (g5 S'ffff\xe6x\xbc@' p4800 tp4801 Rp4802 g4774 F8916.0 tp4803 a(I9650000 g1 (g5 S')\\\x8f\xc2uu\xbc@' p4804 tp4805 Rp4806 g4774 F8420.0 tp4807 a(I9660000 g1 (g5 S'R\xb8\x1e\x85\xebf\xbc@' p4808 tp4809 Rp4810 g4774 F5582.0 tp4811 a(I9670000 g1 (g5 S'\x14\xaeG\xe1\xfa\xde\xbb@' p4812 tp4813 Rp4814 g4774 F6720.0 tp4815 a(I9680000 g1 (g5 S'fffff\xe8\xbb@' p4816 tp4817 Rp4818 g4774 F5582.0 tp4819 a(I9690000 g1 (g5 S'\xe1z\x14\xaeG\xb8\xbb@' p4820 tp4821 Rp4822 g4774 F7476.0 tp4823 a(I9700000 g1 (g5 S'\xaeG\xe1z\x94r\xbb@' p4824 tp4825 Rp4826 g4774 F5480.0 tp4827 a(I9710000 g1 (g5 S'\xaeG\xe1z\x14g\xbb@' p4828 tp4829 Rp4830 g4774 F4668.0 tp4831 a(I9720000 g1 (g5 S'H\xe1z\x14\xae\x91\xbb@' p4832 tp4833 Rp4834 g4774 F11430.0 tp4835 a(I9730000 g1 (g5 S')\\\x8f\xc2\xf5K\xbb@' p4836 tp4837 Rp4838 g4774 F5216.0 tp4839 a(I9740000 g1 (g5 S'\xf6(\\\x8fB\x0e\xbb@' p4840 tp4841 Rp4842 g4774 F3964.0 tp4843 a(I9750000 g1 (g5 S'\x1f\x85\xebQ8\x9e\xba@' p4844 tp4845 Rp4846 g4774 F3420.0 tp4847 a(I9760000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x90\xba@' p4848 tp4849 Rp4850 g4774 F6660.0 tp4851 a(I9770000 g1 (g5 S')\\\x8f\xc2\xf5L\xba@' p4852 tp4853 Rp4854 g4774 F7080.0 tp4855 a(I9780000 g1 (g5 S'R\xb8\x1e\x85k\xf0\xb9@' p4856 tp4857 Rp4858 g4774 F4740.0 tp4859 a(I9790000 g1 (g5 S'ffff\xe6\xe1\xb9@' p4860 tp4861 Rp4862 g4774 F7380.0 tp4863 a(I9800000 g1 (g5 S'ffff\xe6\x15\xba@' p4864 tp4865 Rp4866 g4774 F9130.0 tp4867 a(I9810000 g1 (g5 S'\xcd\xcc\xcc\xccL\xa2\xba@' p4868 tp4869 Rp4870 g4774 F18900.0 tp4871 a(I9820000 g1 (g5 S'\xe1z\x14\xaeG\x85\xba@' p4872 tp4873 Rp4874 g4774 F4800.0 tp4875 a(I9830000 g1 (g5 S'\xc3\xf5(\\\x0f\x84\xba@' p4876 tp4877 Rp4878 g4774 F6478.0 tp4879 a(I9840000 g1 (g5 S'\xc3\xf5(\\\x0f\x99\xba@' p4880 tp4881 Rp4882 g4774 F5896.0 tp4883 a(I9850000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xfa\xb9@' p4884 tp4885 Rp4886 g4774 F3750.0 tp4887 a(I9860000 g1 (g5 S'=\n\xd7\xa3p\x14\xba@' p4888 tp4889 Rp4890 g4774 F9300.0 tp4891 a(I9870000 g1 (g5 S'ffff\xe6.\xba@' p4892 tp4893 Rp4894 g4774 F8436.0 tp4895 a(I9880000 g1 (g5 S'\x14\xaeG\xe1\xfa\x1b\xba@' p4896 tp4897 Rp4898 g4774 F5922.0 tp4899 a(I9890000 g1 (g5 S'\xd7\xa3p=\x8a*\xba@' p4900 tp4901 Rp4902 g4774 F2776.0 tp4903 a(I9900000 g1 (g5 S'{\x14\xaeG\xe1\xed\xb9@' p4904 tp4905 Rp4906 g4774 F6300.0 tp4907 a(I9910000 g1 (g5 S'\xd7\xa3p=\x8a\xf4\xb9@' p4908 tp4909 Rp4910 g4774 F10430.0 tp4911 a(I9920000 g1 (g5 S'q=\n\xd7\xa3\x04\xba@' p4912 tp4913 Rp4914 g4774 F10700.0 tp4915 a(I9930000 g1 (g5 S'33333\xd6\xb9@' p4916 tp4917 Rp4918 g4774 F6720.0 tp4919 a(I9935208 g1 (g5 S'\\\x8f\xc2\xf5\xa8\xf6\xb9@' p4920 tp4921 Rp4922 g4774 F10560.0 tp4923 a.(lp0 F528.0 aF352.0 aF396.0 aF220.0 aF264.0 aF396.0 aF88.0 aF396.0 aF352.0 aF308.0 aF572.0 aF88.0 aF264.0 aF220.0 aF44.0 aF440.0 aF484.0 aF308.0 aF220.0 aF616.0 aF440.0 aF264.0 aF176.0 aF352.0 aF220.0 aF352.0 aF396.0 aF132.0 aF220.0 aF132.0 aF484.0 aF572.0 aF220.0 aF616.0 aF176.0 aF264.0 aF660.0 aF484.0 aF308.0 aF264.0 aF176.0 aF660.0 aF264.0 aF396.0 aF396.0 aF352.0 aF484.0 aF220.0 aF264.0 aF132.0 aF756.0 aF220.0 aF396.0 aF528.0 aF220.0 aF220.0 aF396.0 aF308.0 aF352.0 aF308.0 aF396.0 aF264.0 aF528.0 aF264.0 aF308.0 aF440.0 aF660.0 aF352.0 aF264.0 aF484.0 aF308.0 aF176.0 aF352.0 aF352.0 aF900.0 aF176.0 aF528.0 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S'\xa4p=\n\xd7Sw@' p268 tp269 Rp270 g110 F132.0 tp271 a(I660000 g1 (g5 S'=\n\xd7\xa3p\rw@' p272 tp273 Rp274 g110 F220.0 tp275 a(I670000 g1 (g5 S'\xaeG\xe1z\x14\xcev@' p276 tp277 Rp278 g110 F176.0 tp279 a(I680000 g1 (g5 S'33333\x93w@' p280 tp281 Rp282 g110 F308.0 tp283 a(I690000 g1 (g5 S'\x1f\x85\xebQ\xb8\xeew@' p284 tp285 Rp286 g110 F352.0 tp287 a(I700000 g1 (g5 S'=\n\xd7\xa3p\xbdw@' p288 tp289 Rp290 g110 F484.0 tp291 a(I710000 g1 (g5 S'\xecQ\xb8\x1e\x85\xcbw@' p292 tp293 Rp294 g110 F132.0 tp295 a(I720000 g1 (g5 S'\xe1z\x14\xaeGQx@' p296 tp297 Rp298 g110 F528.0 tp299 a(I730000 g1 (g5 S'\x85\xebQ\xb8\x1e5x@' p300 tp301 Rp302 g1 (g5 S'=\n\xd7\xa3pmx@' p303 tp304 Rp305 F220.0 tp306 a(I740000 g1 (g5 S'=\n\xd7\xa3p\xbdw@' p307 tp308 Rp309 g305 F528.0 tp310 a(I750000 g1 (g5 S'\x85\xebQ\xb8\x1e\x85w@' p311 tp312 Rp313 g305 F352.0 tp314 a(I760000 g1 (g5 S'\x85\xebQ\xb8\x1eEw@' p315 tp316 Rp317 g305 F308.0 tp318 a(I770000 g1 (g5 S'\x9a\x99\x99\x99\x99\xe9v@' p319 tp320 Rp321 g305 F440.0 tp322 a(I780000 g1 (g5 S'\x85\xebQ\xb8\x1e\x95v@' p323 tp324 Rp325 g305 F396.0 tp326 a(I790000 g1 (g5 S'\xb8\x1e\x85\xebQxv@' p327 tp328 Rp329 g305 F660.0 tp330 a(I800000 g1 (g5 S'\xf6(\\\x8f\xc2\x85v@' p331 tp332 Rp333 g305 F132.0 tp334 a(I810000 g1 (g5 S'\xe1z\x14\xaeG1v@' p335 tp336 Rp337 g305 F44.0 tp338 a(I820000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x8cv@' p339 tp340 Rp341 g305 F528.0 tp342 a(I830000 g1 (g5 S'fffffFv@' p343 tp344 Rp345 g305 F264.0 tp346 a(I840000 g1 (g5 S'=\n\xd7\xa3p\x9du@' p347 tp348 Rp349 g305 F308.0 tp350 a(I850000 g1 (g5 S'\x9a\x99\x99\x99\x99\tu@' p351 tp352 Rp353 g305 F176.0 tp354 a(I860000 g1 (g5 S'\n\xd7\xa3p=\xcat@' p355 tp356 Rp357 g305 F352.0 tp358 a(I870000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc|t@' p359 tp360 Rp361 g305 F440.0 tp362 a(I880000 g1 (g5 S'\\\x8f\xc2\xf5(\xbct@' p363 tp364 Rp365 g305 F484.0 tp366 a(I890000 g1 (g5 S'33333\xc3t@' p367 tp368 Rp369 g305 F660.0 tp370 a(I900000 g1 (g5 S'=\n\xd7\xa3p\xedt@' p371 tp372 Rp373 g305 F132.0 tp374 a(I910000 g1 (g5 S'H\xe1z\x14\xae\x17u@' p375 tp376 Rp377 g305 F352.0 tp378 a(I920000 g1 (g5 S'\x14\xaeG\xe1z\xf4t@' p379 tp380 Rp381 g305 F484.0 tp382 a(I930000 g1 (g5 S'\n\xd7\xa3p=\xcat@' p383 tp384 Rp385 g305 F660.0 tp386 a(I940000 g1 (g5 S'\x14\xaeG\xe1z4u@' p387 tp388 Rp389 g305 F308.0 tp390 a(I950000 g1 (g5 S'\x1f\x85\xebQ\xb8^u@' p391 tp392 Rp393 g305 F484.0 tp394 a(I960000 g1 (g5 S'{\x14\xaeG\xe1*v@' p395 tp396 Rp397 g305 F308.0 tp398 a(I970000 g1 (g5 S'\x00\x00\x00\x00\x00@v@' p399 tp400 Rp401 g305 F352.0 tp402 a(I980000 g1 (g5 S'\xb8\x1e\x85\xebQ\xc8u@' p403 tp404 Rp405 g305 F396.0 tp406 a(I990000 g1 (g5 S'\n\xd7\xa3p=jv@' p407 tp408 Rp409 g305 F132.0 tp410 a(I1000000 g1 (g5 S'\n\xd7\xa3p=jv@' p411 tp412 Rp413 g305 F264.0 tp414 a(I1010000 g1 (g5 S'\x9a\x99\x99\x99\x99\xa9v@' p415 tp416 Rp417 g305 F528.0 tp418 a(I1020000 g1 (g5 S'\xc3\xf5(\\\x8f\xa2v@' p419 tp420 Rp421 g305 F616.0 tp422 a(I1030000 g1 (g5 S'=\n\xd7\xa3p\x8dv@' p423 tp424 Rp425 g305 F176.0 tp426 a(I1040000 g1 (g5 S'\xa4p=\n\xd7\xd3v@' p427 tp428 Rp429 g305 F660.0 tp430 a(I1050000 g1 (g5 S'\xaeG\xe1z\x14Nv@' p431 tp432 Rp433 g305 F264.0 tp434 a(I1060000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x1cv@' p435 tp436 Rp437 g305 F264.0 tp438 a(I1070000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xccv@' p439 tp440 Rp441 g305 F440.0 tp442 a(I1080000 g1 (g5 S'\x85\xebQ\xb8\x1e\x05w@' p443 tp444 Rp445 g305 F308.0 tp446 a(I1090000 g1 (g5 S'\xa4p=\n\xd7\x93v@' p447 tp448 Rp449 g305 F264.0 tp450 a(I1100000 g1 (g5 S'\x8f\xc2\xf5(\\?v@' p451 tp452 Rp453 g305 F440.0 tp454 a(I1110000 g1 (g5 S')\\\x8f\xc2\xf5\xf8u@' p455 tp456 Rp457 g305 F176.0 tp458 a(I1120000 g1 (g5 S'=\n\xd7\xa3pMv@' p459 tp460 Rp461 g305 F484.0 tp462 a(I1130000 g1 (g5 S'R\xb8\x1e\x85\xeb\xa1v@' p463 tp464 Rp465 g305 F616.0 tp466 a(I1140000 g1 (g5 S'fffffFv@' p467 tp468 Rp469 g305 F572.0 tp470 a(I1150000 g1 (g5 S'\x1f\x85\xebQ\xb8~v@' p471 tp472 Rp473 g305 F44.0 tp474 a(I1160000 g1 (g5 S'R\xb8\x1e\x85\xebQw@' p475 tp476 Rp477 g305 F352.0 tp478 a(I1170000 g1 (g5 S'\x9a\x99\x99\x99\x99\x19w@' p479 tp480 Rp481 g305 F264.0 tp482 a(I1180000 g1 (g5 S'\x1f\x85\xebQ\xb8.w@' p483 tp484 Rp485 g305 F308.0 tp486 a(I1190000 g1 (g5 S'\x1f\x85\xebQ\xb8\xdew@' p487 tp488 Rp489 g305 F484.0 tp490 a(I1200000 g1 (g5 S'q=\n\xd7\xa3 w@' p491 tp492 Rp493 g305 F88.0 tp494 a(I1210000 g1 (g5 S'q=\n\xd7\xa3\xd0w@' p495 tp496 Rp497 g305 F660.0 tp498 a(I1220000 g1 (g5 S'=\n\xd7\xa3p]x@' p499 tp500 Rp501 g305 F660.0 tp502 a(I1230000 g1 (g5 S')\\\x8f\xc2\xf5\xf8x@' p503 tp504 Rp505 g1 (g5 S'\x85\xebQ\xb8\x1e\x15y@' p506 tp507 Rp508 F484.0 tp509 a(I1240000 g1 (g5 S'R\xb8\x1e\x85\xeb\xf1x@' p510 tp511 Rp512 g508 F440.0 tp513 a(I1250000 g1 (g5 S'33333sx@' p514 tp515 Rp516 g508 F132.0 tp517 a(I1260000 g1 (g5 S'\x8f\xc2\xf5(\\\x8fx@' p518 tp519 Rp520 g508 F264.0 tp521 a(I1270000 g1 (g5 S'q=\n\xd7\xa3\xc0x@' p522 tp523 Rp524 g508 F176.0 tp525 a(I1280000 g1 (g5 S'H\xe1z\x14\xae\xc7x@' p526 tp527 Rp528 g508 F528.0 tp529 a(I1290000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xecx@' p530 tp531 Rp532 g1 (g5 S'\x85\xebQ\xb8\x1e%y@' p533 tp534 Rp535 F176.0 tp536 a(I1300000 g1 (g5 S'\xb8\x1e\x85\xebQHy@' p537 tp538 Rp539 g1 (g5 S'\xb8\x1e\x85\xebQHy@' p540 tp541 Rp542 F528.0 tp543 a(I1310000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xacy@' p544 tp545 Rp546 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xacy@' p547 tp548 Rp549 F804.0 tp550 a(I1320000 g1 (g5 S'R\xb8\x1e\x85\xebqz@' p551 tp552 Rp553 g1 (g5 S'R\xb8\x1e\x85\xebqz@' p554 tp555 Rp556 F616.0 tp557 a(I1330000 g1 (g5 S'33333\xe3z@' p558 tp559 Rp560 g1 (g5 S'33333\xe3z@' p561 tp562 Rp563 F660.0 tp564 a(I1340000 g1 (g5 S'\x8f\xc2\xf5(\\\xffz@' p565 tp566 Rp567 g1 (g5 S'\x8f\xc2\xf5(\\\xffz@' p568 tp569 Rp570 F440.0 tp571 a(I1350000 g1 (g5 S'\xc3\xf5(\\\x8frz@' p572 tp573 Rp574 g570 F308.0 tp575 a(I1360000 g1 (g5 S'\xe1z\x14\xaeG\xc1z@' p576 tp577 Rp578 g570 F308.0 tp579 a(I1370000 g1 (g5 S'\xe1z\x14\xaeGq{@' p580 tp581 Rp582 g1 (g5 S'\x8f\xc2\xf5(\\\x7f{@' p583 tp584 Rp585 F308.0 tp586 a(I1380000 g1 (g5 S'\x8f\xc2\xf5(\\\x7f{@' p587 tp588 Rp589 g1 (g5 S'\xc3\xf5(\\\x8f\xa2{@' p590 tp591 Rp592 F396.0 tp593 a(I1390000 g1 (g5 S'=\n\xd7\xa3p\xcd{@' p594 tp595 Rp596 g1 (g5 S'\x1f\x85\xebQ\xb8\xfe{@' p597 tp598 Rp599 F528.0 tp600 a(I1400000 g1 (g5 S'33333c{@' p601 tp602 Rp603 g599 F440.0 tp604 a(I1410000 g1 (g5 S'fffff\xc6{@' p605 tp606 Rp607 g599 F352.0 tp608 a(I1420000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99|@' p609 tp610 Rp611 g1 (g5 S'\x9a\x99\x99\x99\x99\x99|@' p612 tp613 Rp614 F660.0 tp615 a(I1430000 g1 (g5 S'\x8f\xc2\xf5(\\o|@' p616 tp617 Rp618 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xbc|@' p619 tp620 Rp621 F352.0 tp622 a(I1440000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc5|@' p623 tp624 Rp625 g1 (g5 S'\x85\xebQ\xb8\x1e\xc5|@' p626 tp627 Rp628 F660.0 tp629 a(I1450000 g1 (g5 S'\xa4p=\n\xd7\x83}@' p630 tp631 Rp632 g1 (g5 S'\xa4p=\n\xd7\x83}@' p633 tp634 Rp635 F396.0 tp636 a(I1460000 g1 (g5 S'H\xe1z\x14\xae\xe7}@' p637 tp638 Rp639 g1 (g5 S'H\xe1z\x14\xae\xe7}@' p640 tp641 Rp642 F756.0 tp643 a(I1470000 g1 (g5 S'\xe1z\x14\xaeGA~@' p644 tp645 Rp646 g1 (g5 S'\xe1z\x14\xaeGA~@' p647 tp648 Rp649 F484.0 tp650 a(I1480000 g1 (g5 S'\xf6(\\\x8f\xc2\x95~@' p651 tp652 Rp653 g1 (g5 S'\xf6(\\\x8f\xc2\x95~@' p654 tp655 Rp656 F660.0 tp657 a(I1490000 g1 (g5 S'\xf6(\\\x8f\xc2\x95~@' p658 tp659 Rp660 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x9c~@' p661 tp662 Rp663 F804.0 tp664 a(I1500000 g1 (g5 S'\x9a\x99\x99\x99\x999\x7f@' p665 tp666 Rp667 g1 (g5 S'\x9a\x99\x99\x99\x999\x7f@' p668 tp669 Rp670 F660.0 tp671 a(I1510000 g1 (g5 S'\xf6(\\\x8f\xc2%\x7f@' p672 tp673 Rp674 g1 (g5 S'\x00\x00\x00\x00\x00P\x7f@' p675 tp676 Rp677 F396.0 tp678 a(I1520000 g1 (g5 S'\x1f\x85\xebQ\xb8\xde\x7f@' p679 tp680 Rp681 g1 (g5 S'\x1f\x85\xebQ\xb8\xde\x7f@' p682 tp683 Rp684 F484.0 tp685 a(I1530000 g1 (g5 S'\x9a\x99\x99\x99\x99)\x80@' p686 tp687 Rp688 g1 (g5 S'\xa4p=\n\xd7S\x80@' p689 tp690 Rp691 F132.0 tp692 a(I1540000 g1 (g5 S'\xf6(\\\x8f\xc2]\x80@' p693 tp694 Rp695 g1 (g5 S'=\n\xd7\xa3pe\x80@' p696 tp697 Rp698 F660.0 tp699 a(I1550000 g1 (g5 S'\xa4p=\n\xd7\x9b\x80@' p700 tp701 Rp702 g1 (g5 S'\xa4p=\n\xd7\x9b\x80@' p703 tp704 Rp705 F900.0 tp706 a(I1560000 g1 (g5 S'\x8f\xc2\xf5(\\\x07\x81@' p707 tp708 Rp709 g1 (g5 S'fffff\x0e\x81@' p710 tp711 Rp712 F220.0 tp713 a(I1570000 g1 (g5 S'\xe1z\x14\xaeG!\x81@' p714 tp715 Rp716 g1 (g5 S'\x8f\xc2\xf5(\\/\x81@' p717 tp718 Rp719 F528.0 tp720 a(I1580000 g1 (g5 S'\xecQ\xb8\x1e\x85+\x81@' p721 tp722 Rp723 g719 F660.0 tp724 a(I1590000 g1 (g5 S'\xf6(\\\x8f\xc2=\x81@' p725 tp726 Rp727 g1 (g5 S'\xa4p=\n\xd7K\x81@' p728 tp729 Rp730 F352.0 tp731 a(I1600000 g1 (g5 S'\xecQ\xb8\x1e\x85;\x81@' p732 tp733 Rp734 g1 (g5 S'\x85\xebQ\xb8\x1eM\x81@' p735 tp736 Rp737 F352.0 tp738 a(I1610000 g1 (g5 S'\xa4p=\n\xd7s\x81@' p739 tp740 Rp741 g1 (g5 S'\xa4p=\n\xd7s\x81@' p742 tp743 Rp744 F660.0 tp745 a(I1620000 g1 (g5 S'\x9a\x99\x99\x99\x99\xf1\x81@' p746 tp747 Rp748 g1 (g5 S'\x9a\x99\x99\x99\x99\xf1\x81@' p749 tp750 Rp751 F660.0 tp752 a(I1630000 g1 (g5 S'{\x14\xaeG\xe1\x82\x82@' p753 tp754 Rp755 g1 (g5 S'{\x14\xaeG\xe1\x82\x82@' p756 tp757 Rp758 F708.0 tp759 a(I1640000 g1 (g5 S'\xe1z\x14\xaeG\x89\x82@' p760 tp761 Rp762 g1 (g5 S'\xecQ\xb8\x1e\x85\x9b\x82@' p763 tp764 Rp765 F396.0 tp766 a(I1650000 g1 (g5 S'\n\xd7\xa3p=\xea\x82@' p767 tp768 Rp769 g1 (g5 S'\n\xd7\xa3p=\xea\x82@' p770 tp771 Rp772 F900.0 tp773 a(I1660000 g1 (g5 S'\x9a\x99\x99\x99\x99Y\x83@' p774 tp775 Rp776 g1 (g5 S'\x9a\x99\x99\x99\x99Y\x83@' p777 tp778 Rp779 F1140.0 tp780 a(I1670000 g1 (g5 S'\x1f\x85\xebQ\xb8n\x83@' p781 tp782 Rp783 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc|\x83@' p784 tp785 Rp786 F528.0 tp787 a(I1680000 g1 (g5 S'33333\x93\x83@' p788 tp789 Rp790 g1 (g5 S'\xf6(\\\x8f\xc2\x9d\x83@' p791 tp792 Rp793 F660.0 tp794 a(I1690000 g1 (g5 S'\xd7\xa3p=\nw\x83@' p795 tp796 Rp797 g793 F572.0 tp798 a(I1700000 g1 (g5 S'R\xb8\x1e\x85\xeb\x91\x83@' p799 tp800 Rp801 g793 F852.0 tp802 a(I1710000 g1 (g5 S'\xaeG\xe1z\x14~\x83@' p803 tp804 Rp805 g1 (g5 S'fffff\xbe\x83@' p806 tp807 Rp808 F616.0 tp809 a(I1720000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x8c\x83@' p810 tp811 Rp812 g808 F440.0 tp813 a(I1730000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc5\x83@' p814 tp815 Rp816 g1 (g5 S'\x85\xebQ\xb8\x1e\xc5\x83@' p817 tp818 Rp819 F852.0 tp820 a(I1740000 g1 (g5 S'\xd7\xa3p=\n\xaf\x83@' p821 tp822 Rp823 g819 F660.0 tp824 a(I1750000 g1 (g5 S'\n\xd7\xa3p=\xfa\x83@' p825 tp826 Rp827 g1 (g5 S'\n\xd7\xa3p=\xfa\x83@' p828 tp829 Rp830 F484.0 tp831 a(I1760000 g1 (g5 S'\n\xd7\xa3p=Z\x84@' p832 tp833 Rp834 g1 (g5 S'\n\xd7\xa3p=Z\x84@' p835 tp836 Rp837 F1044.0 tp838 a(I1770000 g1 (g5 S')\\\x8f\xc2\xf5x\x84@' p839 tp840 Rp841 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc|\x84@' p842 tp843 Rp844 F948.0 tp845 a(I1780000 g1 (g5 S'R\xb8\x1e\x85\xeb\x89\x84@' p846 tp847 Rp848 g1 (g5 S'\xecQ\xb8\x1e\x85\x9b\x84@' p849 tp850 Rp851 F440.0 tp852 a(I1790000 g1 (g5 S'33333\xd3\x84@' p853 tp854 Rp855 g1 (g5 S'33333\xd3\x84@' p856 tp857 Rp858 F1236.0 tp859 a(I1800000 g1 (g5 S'\\\x8f\xc2\xf5(\x0c\x85@' p860 tp861 Rp862 g1 (g5 S'\\\x8f\xc2\xf5(\x0c\x85@' p863 tp864 Rp865 F756.0 tp866 a(I1810000 g1 (g5 S'\xecQ\xb8\x1e\x85k\x85@' p867 tp868 Rp869 g1 (g5 S'\xecQ\xb8\x1e\x85k\x85@' p870 tp871 Rp872 F1380.0 tp873 a(I1820000 g1 (g5 S'\n\xd7\xa3p=\xd2\x85@' p874 tp875 Rp876 g1 (g5 S'\n\xd7\xa3p=\xd2\x85@' p877 tp878 Rp879 F804.0 tp880 a(I1830000 g1 (g5 S'H\xe1z\x14\xae\xf7\x85@' p881 tp882 Rp883 g1 (g5 S'H\xe1z\x14\xae\xf7\x85@' p884 tp885 Rp886 F352.0 tp887 a(I1840000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f\x85@' p888 tp889 Rp890 g886 F660.0 tp891 a(I1850000 g1 (g5 S'\xcd\xcc\xcc\xcc\xccd\x85@' p892 tp893 Rp894 g886 F660.0 tp895 a(I1860000 g1 (g5 S'\\\x8f\xc2\xf5(t\x85@' p896 tp897 Rp898 g886 F852.0 tp899 a(I1870000 g1 (g5 S'fffff\xa6\x85@' p900 tp901 Rp902 g886 F308.0 tp903 a(I1880000 g1 (g5 S'fffff\xa6\x85@' p904 tp905 Rp906 g886 F756.0 tp907 a(I1890000 g1 (g5 S'\x1f\x85\xebQ\xb8\xb6\x85@' p908 tp909 Rp910 g1 (g5 S'\x8f\xc2\xf5(\\\x07\x86@' p911 tp912 Rp913 F396.0 tp914 a(I1900000 g1 (g5 S'\x85\xebQ\xb8\x1e\xfd\x85@' p915 tp916 Rp917 g913 F756.0 tp918 a(I1910000 g1 (g5 S'\xb8\x1e\x85\xebQ@\x86@' p919 tp920 Rp921 g1 (g5 S'\xb8\x1e\x85\xebQ@\x86@' p922 tp923 Rp924 F756.0 tp925 a(I1920000 g1 (g5 S'33333\xfb\x86@' p926 tp927 Rp928 g1 (g5 S'33333\xfb\x86@' p929 tp930 Rp931 F2108.0 tp932 a(I1930000 g1 (g5 S'fffff\x86\x87@' p933 tp934 Rp935 g1 (g5 S'fffff\x86\x87@' p936 tp937 Rp938 F440.0 tp939 a(I1940000 g1 (g5 S'\xaeG\xe1z\x14\xde\x87@' p940 tp941 Rp942 g1 (g5 S'R\xb8\x1e\x85\xeb\xe1\x87@' p943 tp944 Rp945 F756.0 tp946 a(I1950000 g1 (g5 S'\xe1z\x14\xaeGQ\x88@' p947 tp948 Rp949 g1 (g5 S'\xe1z\x14\xaeGQ\x88@' p950 tp951 Rp952 F1092.0 tp953 a(I1960000 g1 (g5 S'{\x14\xaeG\xe1z\x88@' p954 tp955 Rp956 g1 (g5 S'\xaeG\xe1z\x14\x86\x88@' p957 tp958 Rp959 F528.0 tp960 a(I1970000 g1 (g5 S'\x14\xaeG\xe1z\xd4\x88@' p961 tp962 Rp963 g1 (g5 S'\x14\xaeG\xe1z\xd4\x88@' p964 tp965 Rp966 F1588.0 tp967 a(I1980000 g1 (g5 S'\xb8\x1e\x85\xebQP\x89@' p968 tp969 Rp970 g1 (g5 S'\xb8\x1e\x85\xebQP\x89@' p971 tp972 Rp973 F1380.0 tp974 a(I1990000 g1 (g5 S'\x00\x00\x00\x00\x00\x80\x89@' p975 tp976 Rp977 g1 (g5 S'\x00\x00\x00\x00\x00\x80\x89@' p978 tp979 Rp980 F1380.0 tp981 a(I2000000 g1 (g5 S'\xb8\x1e\x85\xebQ\x08\x8a@' p982 tp983 Rp984 g1 (g5 S'\xb8\x1e\x85\xebQ\x08\x8a@' p985 tp986 Rp987 F1380.0 tp988 a(I2010000 g1 (g5 S'33333\x03\x8a@' p989 tp990 Rp991 g987 F660.0 tp992 a(I2020000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\x8a@' p993 tp994 Rp995 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\x8a@' p996 tp997 Rp998 F1380.0 tp999 a(I2030000 g1 (g5 S'\x8f\xc2\xf5(\\\xf7\x8a@' p1000 tp1001 Rp1002 g1 (g5 S'\x8f\xc2\xf5(\\\xf7\x8a@' p1003 tp1004 Rp1005 F1380.0 tp1006 a(I2040000 g1 (g5 S'\x85\xebQ\xb8\x1e5\x8b@' p1007 tp1008 Rp1009 g1 (g5 S'\x85\xebQ\xb8\x1e5\x8b@' p1010 tp1011 Rp1012 F660.0 tp1013 a(I2050000 g1 (g5 S'\xf6(\\\x8f\xc2-\x8b@' p1014 tp1015 Rp1016 g1 (g5 S'\xa4p=\n\xd7S\x8b@' p1017 tp1018 Rp1019 F948.0 tp1020 a(I2060000 g1 (g5 S'\xc3\xf5(\\\x8f*\x8b@' p1021 tp1022 Rp1023 g1019 F660.0 tp1024 a(I2070000 g1 (g5 S'q=\n\xd7\xa38\x8b@' p1025 tp1026 Rp1027 g1019 F660.0 tp1028 a(I2080000 g1 (g5 S'\xecQ\xb8\x1e\x85\x93\x8b@' p1029 tp1030 Rp1031 g1 (g5 S'\xecQ\xb8\x1e\x85\x93\x8b@' p1032 tp1033 Rp1034 F1380.0 tp1035 a(I2090000 g1 (g5 S'\xe1z\x14\xaeG\xf9\x8b@' p1036 tp1037 Rp1038 g1 (g5 S'\xe1z\x14\xaeG\xf9\x8b@' p1039 tp1040 Rp1041 F1692.0 tp1042 a(I2100000 g1 (g5 S'\x9a\x99\x99\x99\x99)\x8c@' p1043 tp1044 Rp1045 g1 (g5 S'\x9a\x99\x99\x99\x99)\x8c@' p1046 tp1047 Rp1048 F1744.0 tp1049 a(I2110000 g1 (g5 S'q=\n\xd7\xa3\x00\x8d@' p1050 tp1051 Rp1052 g1 (g5 S'q=\n\xd7\xa3\x00\x8d@' p1053 tp1054 Rp1055 F756.0 tp1056 a(I2120000 g1 (g5 S'\xe1z\x14\xaeG\x81\x8d@' p1057 tp1058 Rp1059 g1 (g5 S'\xe1z\x14\xaeG\x81\x8d@' p1060 tp1061 Rp1062 F1284.0 tp1063 a(I2130000 g1 (g5 S'\x9a\x99\x99\x99\x99\xf1\x8d@' p1064 tp1065 Rp1066 g1 (g5 S'\x9a\x99\x99\x99\x99\xf1\x8d@' p1067 tp1068 Rp1069 F1432.0 tp1070 a(I2140000 g1 (g5 S'\xe1z\x14\xaeG\x1d\x8f@' p1071 tp1072 Rp1073 g1 (g5 S'\xe1z\x14\xaeG\x1d\x8f@' p1074 tp1075 Rp1076 F4230.0 tp1077 a(I2150000 g1 (g5 S'\xf6(\\\x8f\xc2\x81\x8f@' p1078 tp1079 Rp1080 g1 (g5 S'\xf6(\\\x8f\xc2\x81\x8f@' p1081 tp1082 Rp1083 F756.0 tp1084 a(I2160000 g1 (g5 S'\xd7\xa3p=\n\x11\x90@' p1085 tp1086 Rp1087 g1 (g5 S'\xd7\xa3p=\n\x11\x90@' p1088 tp1089 Rp1090 F1380.0 tp1091 a(I2170000 g1 (g5 S')\\\x8f\xc2\xf5"\x90@' p1092 tp1093 Rp1094 g1 (g5 S')\\\x8f\xc2\xf5"\x90@' p1095 tp1096 Rp1097 F1536.0 tp1098 a(I2180000 g1 (g5 S'\x85\xebQ\xb8\x1e\x03\x90@' p1099 tp1100 Rp1101 g1097 F660.0 tp1102 a(I2190000 g1 (g5 S'H\xe1z\x14\xaeQ\x90@' p1103 tp1104 Rp1105 g1 (g5 S'H\xe1z\x14\xaeQ\x90@' p1106 tp1107 Rp1108 F852.0 tp1109 a(I2200000 g1 (g5 S'\xb8\x1e\x85\xebQ&\x90@' p1110 tp1111 Rp1112 g1108 F352.0 tp1113 a(I2210000 g1 (g5 S'\xaeG\xe1z\x14x\x90@' p1114 tp1115 Rp1116 g1 (g5 S'\xaeG\xe1z\x14x\x90@' p1117 tp1118 Rp1119 F2160.0 tp1120 a(I2220000 g1 (g5 S'fffff\xc8\x90@' p1121 tp1122 Rp1123 g1 (g5 S'fffff\xc8\x90@' p1124 tp1125 Rp1126 F2860.0 tp1127 a(I2230000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xf2\x90@' p1128 tp1129 Rp1130 g1 (g5 S'\x14\xaeG\xe1z\xfa\x90@' p1131 tp1132 Rp1133 F1092.0 tp1134 a(I2240000 g1 (g5 S'\\\x8f\xc2\xf5(\xee\x90@' p1135 tp1136 Rp1137 g1 (g5 S'\\\x8f\xc2\xf5(&\x91@' p1138 tp1139 Rp1140 F708.0 tp1141 a(I2250000 g1 (g5 S'\\\x8f\xc2\xf5(\xee\x90@' p1142 tp1143 Rp1144 g1140 F1284.0 tp1145 a(I2260000 g1 (g5 S'\x9a\x99\x99\x99\x99\xff\x90@' p1146 tp1147 Rp1148 g1140 F1380.0 tp1149 a(I2270000 g1 (g5 S'\xd7\xa3p=\n\xb9\x91@' p1150 tp1151 Rp1152 g1 (g5 S'\xd7\xa3p=\n\xb9\x91@' p1153 tp1154 Rp1155 F2272.0 tp1156 a(I2280000 g1 (g5 S'\x8f\xc2\xf5(\\G\x92@' p1157 tp1158 Rp1159 g1 (g5 S'\xcd\xcc\xcc\xcc\xccT\x92@' p1160 tp1161 Rp1162 F756.0 tp1163 a(I2290000 g1 (g5 S'\xa4p=\n\xd7k\x92@' p1164 tp1165 Rp1166 g1 (g5 S'\xa4p=\n\xd7k\x92@' p1167 tp1168 Rp1169 F1380.0 tp1170 a(I2300000 g1 (g5 S'q=\n\xd7\xa3\x04\x93@' p1171 tp1172 Rp1173 g1 (g5 S'q=\n\xd7\xa3\x04\x93@' p1174 tp1175 Rp1176 F2552.0 tp1177 a(I2310000 g1 (g5 S'\xa4p=\n\xd7\xd7\x92@' p1178 tp1179 Rp1180 g1176 F996.0 tp1181 a(I2320000 g1 (g5 S'\xecQ\xb8\x1e\x85\x07\x93@' p1182 tp1183 Rp1184 g1 (g5 S'\xecQ\xb8\x1e\x85\x07\x93@' p1185 tp1186 Rp1187 F1044.0 tp1188 a(I2330000 g1 (g5 S'\x8f\xc2\xf5(\\\x0b\x93@' p1189 tp1190 Rp1191 g1 (g5 S'\xc3\xf5(\\\x8f\x1e\x93@' p1192 tp1193 Rp1194 F996.0 tp1195 a(I2340000 g1 (g5 S'\xf6(\\\x8f\xc2\x89\x93@' p1196 tp1197 Rp1198 g1 (g5 S'\xf6(\\\x8f\xc2\x89\x93@' p1199 tp1200 Rp1201 F2108.0 tp1202 a(I2350000 g1 (g5 S'\n\xd7\xa3p=\xb6\x93@' p1203 tp1204 Rp1205 g1 (g5 S'\n\xd7\xa3p=\xb6\x93@' p1206 tp1207 Rp1208 F1484.0 tp1209 a(I2360000 g1 (g5 S'=\n\xd7\xa3p\x95\x93@' p1210 tp1211 Rp1212 g1208 F1432.0 tp1213 a(I2370000 g1 (g5 S'\xc3\xf5(\\\x8f\xaa\x93@' p1214 tp1215 Rp1216 g1208 F1140.0 tp1217 a(I2380000 g1 (g5 S'{\x14\xaeG\xe1\x9e\x93@' p1218 tp1219 Rp1220 g1208 F1380.0 tp1221 a(I2390000 g1 (g5 S'=\n\xd7\xa3p\xbd\x93@' p1222 tp1223 Rp1224 g1 (g5 S'=\n\xd7\xa3p\xbd\x93@' p1225 tp1226 Rp1227 F1380.0 tp1228 a(I2400000 g1 (g5 S'\xaeG\xe1z\x14\xde\x93@' p1229 tp1230 Rp1231 g1 (g5 S'\xaeG\xe1z\x14\xde\x93@' p1232 tp1233 Rp1234 F1044.0 tp1235 a(I2410000 g1 (g5 S'H\xe1z\x14\xaeS\x94@' p1236 tp1237 Rp1238 g1 (g5 S'H\xe1z\x14\xaeS\x94@' p1239 tp1240 Rp1241 F1044.0 tp1242 a(I2420000 g1 (g5 S'\xf6(\\\x8f\xc2\x8d\x94@' p1243 tp1244 Rp1245 g1 (g5 S'H\xe1z\x14\xae\x8f\x94@' p1246 tp1247 Rp1248 F1092.0 tp1249 a(I2430000 g1 (g5 S'=\n\xd7\xa3p\r\x95@' p1250 tp1251 Rp1252 g1 (g5 S'=\n\xd7\xa3p\r\x95@' p1253 tp1254 Rp1255 F2160.0 tp1256 a(I2440000 g1 (g5 S'\x1f\x85\xebQ\xb8^\x95@' p1257 tp1258 Rp1259 g1 (g5 S'\x1f\x85\xebQ\xb8^\x95@' p1260 tp1261 Rp1262 F2692.0 tp1263 a(I2450000 g1 (g5 S'{\x14\xaeG\xe12\x95@' p1264 tp1265 Rp1266 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc`\x95@' p1267 tp1268 Rp1269 F616.0 tp1270 a(I2460000 g1 (g5 S'\x00\x00\x00\x00\x00\xa0\x95@' p1271 tp1272 Rp1273 g1 (g5 S'\x00\x00\x00\x00\x00\xa0\x95@' p1274 tp1275 Rp1276 F1380.0 tp1277 a(I2470000 g1 (g5 S'q=\n\xd7\xa3l\x95@' p1278 tp1279 Rp1280 g1 (g5 S'q=\n\xd7\xa3\xb0\x95@' p1281 tp1282 Rp1283 F756.0 tp1284 a(I2480000 g1 (g5 S'\xc3\xf5(\\\x8f\x92\x95@' p1285 tp1286 Rp1287 g1283 F2160.0 tp1288 a(I2490000 g1 (g5 S'\xc3\xf5(\\\x8f\xbe\x95@' p1289 tp1290 Rp1291 g1 (g5 S'\xc3\xf5(\\\x8f\xbe\x95@' p1292 tp1293 Rp1294 F1952.0 tp1295 a(I2500000 g1 (g5 S'q=\n\xd7\xa3~\x95@' p1296 tp1297 Rp1298 g1 (g5 S'\x1f\x85\xebQ\xb8\xee\x95@' p1299 tp1300 Rp1301 F2056.0 tp1302 a(I2510000 g1 (g5 S'\x9a\x99\x99\x99\x99\xd3\x95@' p1303 tp1304 Rp1305 g1301 F2056.0 tp1306 a(I2520000 g1 (g5 S'\xf6(\\\x8f\xc2/\x96@' p1307 tp1308 Rp1309 g1 (g5 S'\xf6(\\\x8f\xc2/\x96@' p1310 tp1311 Rp1312 F3060.0 tp1313 a(I2530000 g1 (g5 S'R\xb8\x1e\x85\xebg\x96@' p1314 tp1315 Rp1316 g1 (g5 S'R\xb8\x1e\x85\xebg\x96@' p1317 tp1318 Rp1319 F1332.0 tp1320 a(I2540000 g1 (g5 S'\x14\xaeG\xe1zb\x96@' p1321 tp1322 Rp1323 g1319 F1692.0 tp1324 a(I2550000 g1 (g5 S'\x14\xaeG\xe1z\xa6\x96@' p1325 tp1326 Rp1327 g1 (g5 S'\x14\xaeG\xe1z\xa6\x96@' p1328 tp1329 Rp1330 F1692.0 tp1331 a(I2560000 g1 (g5 S'\xd7\xa3p=\n\xe5\x96@' p1332 tp1333 Rp1334 g1 (g5 S'\xd7\xa3p=\n\xe5\x96@' p1335 tp1336 Rp1337 F1744.0 tp1338 a(I2570000 g1 (g5 S'\\\x8f\xc2\xf5(\xce\x96@' p1339 tp1340 Rp1341 g1337 F852.0 tp1342 a(I2580000 g1 (g5 S'H\xe1z\x14\xae)\x97@' p1343 tp1344 Rp1345 g1 (g5 S'\xecQ\xb8\x1e\x85-\x97@' p1346 tp1347 Rp1348 F708.0 tp1349 a(I2590000 g1 (g5 S'=\n\xd7\xa3pC\x97@' p1350 tp1351 Rp1352 g1 (g5 S'\x85\xebQ\xb8\x1e[\x97@' p1353 tp1354 Rp1355 F996.0 tp1356 a(I2600000 g1 (g5 S'\x14\xaeG\xe1z\xf8\x97@' p1357 tp1358 Rp1359 g1 (g5 S'\x14\xaeG\xe1z\xf8\x97@' p1360 tp1361 Rp1362 F2692.0 tp1363 a(I2610000 g1 (g5 S'fffff\xae\x97@' p1364 tp1365 Rp1366 g1362 F1332.0 tp1367 a(I2620000 g1 (g5 S'\xecQ\xb8\x1e\x85\x1d\x98@' p1368 tp1369 Rp1370 g1 (g5 S'\xecQ\xb8\x1e\x85\x1d\x98@' p1371 tp1372 Rp1373 F3870.0 tp1374 a(I2630000 g1 (g5 S'\xaeG\xe1z\x14\xa4\x98@' p1375 tp1376 Rp1377 g1 (g5 S'\xaeG\xe1z\x14\xa4\x98@' p1378 tp1379 Rp1380 F2972.0 tp1381 a(I2640000 g1 (g5 S'{\x14\xaeG\xe1\xb4\x98@' p1382 tp1383 Rp1384 g1 (g5 S'{\x14\xaeG\xe1\xb4\x98@' p1385 tp1386 Rp1387 F1484.0 tp1388 a(I2650000 g1 (g5 S'\xaeG\xe1z\x144\x99@' p1389 tp1390 Rp1391 g1 (g5 S'\xaeG\xe1z\x144\x99@' p1392 tp1393 Rp1394 F3084.0 tp1395 a(I2660000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xce\x98@' p1396 tp1397 Rp1398 g1394 F2860.0 tp1399 a(I2670000 g1 (g5 S')\\\x8f\xc2\xf5\xa8\x98@' p1400 tp1401 Rp1402 g1394 F2108.0 tp1403 a(I2680000 g1 (g5 S'fffff\xa2\x98@' p1404 tp1405 Rp1406 g1394 F2916.0 tp1407 a(I2690000 g1 (g5 S'=\n\xd7\xa3p\x95\x98@' p1408 tp1409 Rp1410 g1394 F1796.0 tp1411 a(I2700000 g1 (g5 S'\xe1z\x14\xaeG\xad\x98@' p1412 tp1413 Rp1414 g1394 F2160.0 tp1415 a(I2710000 g1 (g5 S'=\n\xd7\xa3p\xa9\x98@' p1416 tp1417 Rp1418 g1394 F1284.0 tp1419 a(I2720000 g1 (g5 S'{\x14\xaeG\xe1\x9e\x98@' p1420 tp1421 Rp1422 g1394 F660.0 tp1423 a(I2730000 g1 (g5 S'fffff\xfe\x98@' p1424 tp1425 Rp1426 g1394 F2108.0 tp1427 a(I2740000 g1 (g5 S'R\xb8\x1e\x85\xeby\x99@' p1428 tp1429 Rp1430 g1 (g5 S'R\xb8\x1e\x85\xeby\x99@' p1431 tp1432 Rp1433 F3900.0 tp1434 a(I2750000 g1 (g5 S'\n\xd7\xa3p=n\x99@' p1435 tp1436 Rp1437 g1 (g5 S'33333\x8b\x99@' p1438 tp1439 Rp1440 F1044.0 tp1441 a(I2760000 g1 (g5 S'fffff\xae\x99@' p1442 tp1443 Rp1444 g1 (g5 S'fffff\xae\x99@' p1445 tp1446 Rp1447 F2888.0 tp1448 a(I2770000 g1 (g5 S'{\x14\xaeG\xe1\xb6\x99@' p1449 tp1450 Rp1451 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xb8\x99@' p1452 tp1453 Rp1454 F1140.0 tp1455 a(I2780000 g1 (g5 S'\x9a\x99\x99\x99\x99%\x9a@' p1456 tp1457 Rp1458 g1 (g5 S'\x9a\x99\x99\x99\x99%\x9a@' p1459 tp1460 Rp1461 F3000.0 tp1462 a(I2790000 g1 (g5 S'\xb8\x1e\x85\xebQ\x08\x9a@' p1463 tp1464 Rp1465 g1 (g5 S'\xd7\xa3p=\n3\x9a@' p1466 tp1467 Rp1468 F1092.0 tp1469 a(I2800000 g1 (g5 S'\xc3\xf5(\\\x8fv\x9a@' p1470 tp1471 Rp1472 g1 (g5 S'q=\n\xd7\xa3x\x9a@' p1473 tp1474 Rp1475 F2108.0 tp1476 a(I2810000 g1 (g5 S')\\\x8f\xc2\xf5\x88\x9a@' p1477 tp1478 Rp1479 g1 (g5 S')\\\x8f\xc2\xf5\x88\x9a@' p1480 tp1481 Rp1482 F1900.0 tp1483 a(I2820000 g1 (g5 S'R\xb8\x1e\x85\xeb\xa1\x9a@' p1484 tp1485 Rp1486 g1 (g5 S'R\xb8\x1e\x85\xeb\xa1\x9a@' p1487 tp1488 Rp1489 F2004.0 tp1490 a(I2830000 g1 (g5 S'\x00\x00\x00\x00\x00\xc4\x9a@' p1491 tp1492 Rp1493 g1 (g5 S'\x00\x00\x00\x00\x00\xc4\x9a@' p1494 tp1495 Rp1496 F1188.0 tp1497 a(I2840000 g1 (g5 S'\xaeG\xe1z\x14\xee\x9a@' p1498 tp1499 Rp1500 g1 (g5 S'R\xb8\x1e\x85\xeb1\x9b@' p1501 tp1502 Rp1503 F1380.0 tp1504 a(I2850000 g1 (g5 S'\x14\xaeG\xe1z\x80\x9b@' p1505 tp1506 Rp1507 g1 (g5 S'\x14\xaeG\xe1z\x80\x9b@' p1508 tp1509 Rp1510 F2972.0 tp1511 a(I2860000 g1 (g5 S'\xc3\xf5(\\\x8f\xda\x9b@' p1512 tp1513 Rp1514 g1 (g5 S'R\xb8\x1e\x85\xeb\xe9\x9b@' p1515 tp1516 Rp1517 F804.0 tp1518 a(I2870000 g1 (g5 S'\xaeG\xe1z\x14~\x9b@' p1519 tp1520 Rp1521 g1517 F852.0 tp1522 a(I2880000 g1 (g5 S'\x8f\xc2\xf5(\\\xa3\x9b@' p1523 tp1524 Rp1525 g1517 F1380.0 tp1526 a(I2890000 g1 (g5 S'\xaeG\xe1z\x14\xca\x9b@' p1527 tp1528 Rp1529 g1517 F3420.0 tp1530 a(I2900000 g1 (g5 S'q=\n\xd7\xa34\x9c@' p1531 tp1532 Rp1533 g1 (g5 S'q=\n\xd7\xa34\x9c@' p1534 tp1535 Rp1536 F2888.0 tp1537 a(I2910000 g1 (g5 S'H\xe1z\x14\xaec\x9c@' p1538 tp1539 Rp1540 g1 (g5 S'\\\x8f\xc2\xf5(p\x9c@' p1541 tp1542 Rp1543 F1380.0 tp1544 a(I2920000 g1 (g5 S'33333\xab\x9c@' p1545 tp1546 Rp1547 g1 (g5 S'33333\xab\x9c@' p1548 tp1549 Rp1550 F1380.0 tp1551 a(I2930000 g1 (g5 S'\x8f\xc2\xf5(\\\xbb\x9c@' p1552 tp1553 Rp1554 g1 (g5 S'\x00\x00\x00\x00\x00\xc8\x9c@' p1555 tp1556 Rp1557 F1380.0 tp1558 a(I2940000 g1 (g5 S'\x1f\x85\xebQ\xb8\x02\x9d@' p1559 tp1560 Rp1561 g1 (g5 S'\x1f\x85\xebQ\xb8\x02\x9d@' p1562 tp1563 Rp1564 F1380.0 tp1565 a(I2950000 g1 (g5 S'\xb8\x1e\x85\xebQ\xc2\x9c@' p1566 tp1567 Rp1568 g1 (g5 S'{\x14\xaeG\xe12\x9d@' p1569 tp1570 Rp1571 F1536.0 tp1572 a(I2960000 g1 (g5 S'q=\n\xd7\xa3\xe2\x9c@' p1573 tp1574 Rp1575 g1571 F3900.0 tp1576 a(I2970000 g1 (g5 S'\xe1z\x14\xaeG\xf1\x9d@' p1577 tp1578 Rp1579 g1 (g5 S'\xe1z\x14\xaeG\xf1\x9d@' p1580 tp1581 Rp1582 F3480.0 tp1583 a(I2980000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xe4\x9d@' p1584 tp1585 Rp1586 g1 (g5 S'\x00\x00\x00\x00\x00j\x9e@' p1587 tp1588 Rp1589 F1380.0 tp1590 a(I2990000 g1 (g5 S'\x85\xebQ\xb8\x1e\xa1\x9d@' p1591 tp1592 Rp1593 g1589 F1380.0 tp1594 a(I3000000 g1 (g5 S'\xb8\x1e\x85\xebQP\x9d@' p1595 tp1596 Rp1597 g1589 F2160.0 tp1598 a(I3010000 g1 (g5 S')\\\x8f\xc2\xf5\x06\x9e@' p1599 tp1600 Rp1601 g1589 F2160.0 tp1602 a(I3020000 g1 (g5 S'\x1f\x85\xebQ\xb8\xe4\x9e@' p1603 tp1604 Rp1605 g1 (g5 S'\x1f\x85\xebQ\xb8\xe4\x9e@' p1606 tp1607 Rp1608 F4448.0 tp1609 a(I3030000 g1 (g5 S'\x85\xebQ\xb8\x1eY\x9f@' p1610 tp1611 Rp1612 g1 (g5 S'\xe1z\x14\xaeGa\x9f@' p1613 tp1614 Rp1615 F2160.0 tp1616 a(I3040000 g1 (g5 S'\xf6(\\\x8f\xc2\xb1\x9f@' p1617 tp1618 Rp1619 g1 (g5 S'\xf6(\\\x8f\xc2\xb1\x9f@' p1620 tp1621 Rp1622 F1692.0 tp1623 a(I3050000 g1 (g5 S'=\n\xd7\xa3p\x11\xa0@' p1624 tp1625 Rp1626 g1 (g5 S'=\n\xd7\xa3p\x11\xa0@' p1627 tp1628 Rp1629 F3364.0 tp1630 a(I3060000 g1 (g5 S'\\\x8f\xc2\xf5(\x1c\xa0@' p1631 tp1632 Rp1633 g1 (g5 S'\xe1z\x14\xaeG/\xa0@' p1634 tp1635 Rp1636 F1952.0 tp1637 a(I3070000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x16\xa0@' p1638 tp1639 Rp1640 g1 (g5 S'\xd7\xa3p=\n7\xa0@' p1641 tp1642 Rp1643 F1332.0 tp1644 a(I3080000 g1 (g5 S'\xa4p=\n\xd7\x07\xa0@' p1645 tp1646 Rp1647 g1643 F1640.0 tp1648 a(I3090000 g1 (g5 S'\\\x8f\xc2\xf5(l\xa0@' p1649 tp1650 Rp1651 g1 (g5 S'\\\x8f\xc2\xf5(l\xa0@' p1652 tp1653 Rp1654 F2056.0 tp1655 a(I3100000 g1 (g5 S'\n\xd7\xa3p=t\xa0@' p1656 tp1657 Rp1658 g1 (g5 S'fffff\x92\xa0@' p1659 tp1660 Rp1661 F1380.0 tp1662 a(I3110000 g1 (g5 S'\\\x8f\xc2\xf5(t\xa0@' p1663 tp1664 Rp1665 g1661 F1332.0 tp1666 a(I3120000 g1 (g5 S'q=\n\xd7\xa3\xbe\xa0@' p1667 tp1668 Rp1669 g1 (g5 S'q=\n\xd7\xa3\xbe\xa0@' p1670 tp1671 Rp1672 F4740.0 tp1673 a(I3130000 g1 (g5 S'fffff)\xa1@' p1674 tp1675 Rp1676 g1 (g5 S'fffff)\xa1@' p1677 tp1678 Rp1679 F3840.0 tp1680 a(I3140000 g1 (g5 S'\xa4p=\n\xd7;\xa1@' p1681 tp1682 Rp1683 g1 (g5 S'\xa4p=\n\xd7;\xa1@' p1684 tp1685 Rp1686 F4478.0 tp1687 a(I3150000 g1 (g5 S'fffff\xa6\xa1@' p1688 tp1689 Rp1690 g1 (g5 S'\xf6(\\\x8f\xc2\xaf\xa1@' p1691 tp1692 Rp1693 F1692.0 tp1694 a(I3160000 g1 (g5 S'\x00\x00\x00\x00\x00\xee\xa1@' p1695 tp1696 Rp1697 g1 (g5 S'\x00\x00\x00\x00\x00\xee\xa1@' p1698 tp1699 Rp1700 F4350.0 tp1701 a(I3170000 g1 (g5 S'33333|\xa2@' p1702 tp1703 Rp1704 g1 (g5 S'33333|\xa2@' p1705 tp1706 Rp1707 F5474.0 tp1708 a(I3180000 g1 (g5 S'H\xe1z\x14\xae\xb9\xa2@' p1709 tp1710 Rp1711 g1 (g5 S'H\xe1z\x14\xae\xb9\xa2@' p1712 tp1713 Rp1714 F2860.0 tp1715 a(I3190000 g1 (g5 S'\xd7\xa3p=\n)\xa3@' p1716 tp1717 Rp1718 g1 (g5 S'\xd7\xa3p=\n)\xa3@' p1719 tp1720 Rp1721 F5268.0 tp1722 a(I3200000 g1 (g5 S'\n\xd7\xa3p={\xa3@' p1723 tp1724 Rp1725 g1 (g5 S'\n\xd7\xa3p={\xa3@' p1726 tp1727 Rp1728 F3660.0 tp1729 a(I3210000 g1 (g5 S'\xecQ\xb8\x1e\x85\x9a\xa3@' p1730 tp1731 Rp1732 g1 (g5 S'\xecQ\xb8\x1e\x85\x9a\xa3@' p1733 tp1734 Rp1735 F2664.0 tp1736 a(I3220000 g1 (g5 S'\x8f\xc2\xf5(\\\x10\xa4@' p1737 tp1738 Rp1739 g1 (g5 S'\x8f\xc2\xf5(\\\x10\xa4@' p1740 tp1741 Rp1742 F2328.0 tp1743 a(I3230000 g1 (g5 S'\xc3\xf5(\\\x8fi\xa4@' p1744 tp1745 Rp1746 g1 (g5 S'\xc3\xf5(\\\x8fi\xa4@' p1747 tp1748 Rp1749 F2552.0 tp1750 a(I3240000 g1 (g5 S')\\\x8f\xc2\xf5\xe8\xa4@' p1751 tp1752 Rp1753 g1 (g5 S')\\\x8f\xc2\xf5\xe8\xa4@' p1754 tp1755 Rp1756 F2160.0 tp1757 a(I3250000 g1 (g5 S'\x00\x00\x00\x00\x00\xe0\xa4@' p1758 tp1759 Rp1760 g1 (g5 S'\x00\x00\x00\x00\x00\xee\xa4@' p1761 tp1762 Rp1763 F2720.0 tp1764 a(I3260000 g1 (g5 S'\xecQ\xb8\x1e\x85 \xa5@' p1765 tp1766 Rp1767 g1 (g5 S'\xecQ\xb8\x1e\x85 \xa5@' p1768 tp1769 Rp1770 F2440.0 tp1771 a(I3270000 g1 (g5 S'\x14\xaeG\xe1za\xa5@' p1772 tp1773 Rp1774 g1 (g5 S'\x14\xaeG\xe1za\xa5@' p1775 tp1776 Rp1777 F4156.0 tp1778 a(I3280000 g1 (g5 S'q=\n\xd7\xa3\xdb\xa5@' p1779 tp1780 Rp1781 g1 (g5 S'q=\n\xd7\xa3\xdb\xa5@' p1782 tp1783 Rp1784 F4320.0 tp1785 a(I3290000 g1 (g5 S'H\xe1z\x14\xae(\xa6@' p1786 tp1787 Rp1788 g1 (g5 S'H\xe1z\x14\xae(\xa6@' p1789 tp1790 Rp1791 F4156.0 tp1792 a(I3300000 g1 (g5 S'\n\xd7\xa3p=\xf5\xa6@' p1793 tp1794 Rp1795 g1 (g5 S'\n\xd7\xa3p=\xf5\xa6@' p1796 tp1797 Rp1798 F5734.0 tp1799 a(I3310000 g1 (g5 S'\x85\xebQ\xb8\x1eT\xa7@' p1800 tp1801 Rp1802 g1 (g5 S'\x85\xebQ\xb8\x1eT\xa7@' p1803 tp1804 Rp1805 F3420.0 tp1806 a(I3320000 g1 (g5 S'q=\n\xd7\xa3]\xa7@' p1807 tp1808 Rp1809 g1 (g5 S'R\xb8\x1e\x85\xebd\xa7@' p1810 tp1811 Rp1812 F2440.0 tp1813 a(I3330000 g1 (g5 S'{\x14\xaeG\xe1\xb9\xa7@' p1814 tp1815 Rp1816 g1 (g5 S'{\x14\xaeG\xe1\xb9\xa7@' p1817 tp1818 Rp1819 F2004.0 tp1820 a(I3340000 g1 (g5 S'=\n\xd7\xa3p/\xa8@' p1821 tp1822 Rp1823 g1 (g5 S'=\n\xd7\xa3p/\xa8@' p1824 tp1825 Rp1826 F5602.0 tp1827 a(I3350000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x19\xa8@' p1828 tp1829 Rp1830 g1 (g5 S'\xb8\x1e\x85\xebQP\xa8@' p1831 tp1832 Rp1833 F3000.0 tp1834 a(I3360000 g1 (g5 S'\xaeG\xe1z\x14\x03\xa8@' p1835 tp1836 Rp1837 g1833 F1744.0 tp1838 a(I3370000 g1 (g5 S'\xc3\xf5(\\\x8fs\xa8@' p1839 tp1840 Rp1841 g1 (g5 S'\xc3\xf5(\\\x8fs\xa8@' p1842 tp1843 Rp1844 F4320.0 tp1845 a(I3380000 g1 (g5 S'\xc3\xf5(\\\x8f\xe1\xa8@' p1846 tp1847 Rp1848 g1 (g5 S'\xc3\xf5(\\\x8f\xe1\xa8@' p1849 tp1850 Rp1851 F3900.0 tp1852 a(I3390000 g1 (g5 S')\\\x8f\xc2\xf5\x8d\xa9@' p1853 tp1854 Rp1855 g1 (g5 S')\\\x8f\xc2\xf5\x8d\xa9@' p1856 tp1857 Rp1858 F5508.0 tp1859 a(I3400000 g1 (g5 S'\xe1z\x14\xaeG\xb4\xa9@' p1860 tp1861 Rp1862 g1 (g5 S'\xc3\xf5(\\\x8f\xbb\xa9@' p1863 tp1864 Rp1865 F1796.0 tp1866 a(I3410000 g1 (g5 S'\xe1z\x14\xaeG\x8c\xa9@' p1867 tp1868 Rp1869 g1865 F4734.0 tp1870 a(I3420000 g1 (g5 S'H\xe1z\x14\xae\xab\xa9@' p1871 tp1872 Rp1873 g1865 F4960.0 tp1874 a(I3430000 g1 (g5 S'\n\xd7\xa3p=\xff\xa9@' p1875 tp1876 Rp1877 g1 (g5 S'\n\xd7\xa3p=\xff\xa9@' p1878 tp1879 Rp1880 F5310.0 tp1881 a(I3440000 g1 (g5 S'\xecQ\xb8\x1e\x85\x02\xaa@' p1882 tp1883 Rp1884 g1 (g5 S'\xecQ\xb8\x1e\x85\x02\xaa@' p1885 tp1886 Rp1887 F2888.0 tp1888 a(I3450000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x1f\xaa@' p1889 tp1890 Rp1891 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x1f\xaa@' p1892 tp1893 Rp1894 F3000.0 tp1895 a(I3460000 g1 (g5 S'\xe1z\x14\xaeG\xa0\xaa@' p1896 tp1897 Rp1898 g1 (g5 S'\xe1z\x14\xaeG\xa0\xaa@' p1899 tp1900 Rp1901 F5340.0 tp1902 a(I3470000 g1 (g5 S'\xc3\xf5(\\\x8f\xcf\xaa@' p1903 tp1904 Rp1905 g1 (g5 S'\xc3\xf5(\\\x8f\xcf\xaa@' p1906 tp1907 Rp1908 F1848.0 tp1909 a(I3480000 g1 (g5 S'\n\xd7\xa3p=:\xab@' p1910 tp1911 Rp1912 g1 (g5 S'\\\x8f\xc2\xf5(N\xab@' p1913 tp1914 Rp1915 F2004.0 tp1916 a(I3490000 g1 (g5 S'{\x14\xaeG\xe1\xc0\xab@' p1917 tp1918 Rp1919 g1 (g5 S'{\x14\xaeG\xe1\xc0\xab@' p1920 tp1921 Rp1922 F6596.0 tp1923 a(I3500000 g1 (g5 S")\\\x8f\xc2\xf5'\xac@" p1924 tp1925 Rp1926 g1 (g5 S")\\\x8f\xc2\xf5'\xac@" p1927 tp1928 Rp1929 F5190.0 tp1930 a(I3510000 g1 (g5 S'q=\n\xd7\xa3\xd9\xac@' p1931 tp1932 Rp1933 g1 (g5 S'q=\n\xd7\xa3\xd9\xac@' p1934 tp1935 Rp1936 F5676.0 tp1937 a(I3520000 g1 (g5 S'33333\x0e\xad@' p1938 tp1939 Rp1940 g1 (g5 S'33333\x0e\xad@' p1941 tp1942 Rp1943 F4320.0 tp1944 a(I3530000 g1 (g5 S'\x14\xaeG\xe1z\xf9\xad@' p1945 tp1946 Rp1947 g1 (g5 S'\x14\xaeG\xe1z\xf9\xad@' p1948 tp1949 Rp1950 F5662.0 tp1951 a(I3540000 g1 (g5 S'\x14\xaeG\xe1zp\xad@' p1952 tp1953 Rp1954 g1950 F2160.0 tp1955 a(I3550000 g1 (g5 S'\xecQ\xb8\x1e\x85{\xad@' p1956 tp1957 Rp1958 g1950 F1640.0 tp1959 a(I3560000 g1 (g5 S'fffffy\xad@' p1960 tp1961 Rp1962 g1950 F4050.0 tp1963 a(I3570000 g1 (g5 S'\xf6(\\\x8f\xc2\xb4\xad@' p1964 tp1965 Rp1966 g1950 F5884.0 tp1967 a(I3580000 g1 (g5 S'\\\x8f\xc2\xf5(\xf2\xad@' p1968 tp1969 Rp1970 g1950 F4604.0 tp1971 a(I3590000 g1 (g5 S'\xb8\x1e\x85\xebQ \xae@' p1972 tp1973 Rp1974 g1 (g5 S'\xb8\x1e\x85\xebQ \xae@' p1975 tp1976 Rp1977 F5312.0 tp1978 a(I3600000 g1 (g5 S'R\xb8\x1e\x85\xeb\xf5\xad@' p1979 tp1980 Rp1981 g1977 F5312.0 tp1982 a(I3610000 g1 (g5 S'\xc3\xf5(\\\x8f\xf0\xad@' p1983 tp1984 Rp1985 g1 (g5 S'\xb8\x1e\x85\xebQ&\xae@' p1986 tp1987 Rp1988 F3000.0 tp1989 a(I3620000 g1 (g5 S'\n\xd7\xa3p=\xb1\xad@' p1990 tp1991 Rp1992 g1988 F4540.0 tp1993 a(I3630000 g1 (g5 S'33333\xbe\xad@' p1994 tp1995 Rp1996 g1988 F3900.0 tp1997 a(I3640000 g1 (g5 S'{\x14\xaeG\xe1\xf9\xad@' p1998 tp1999 Rp2000 g1988 F3420.0 tp2001 a(I3650000 g1 (g5 S"{\x14\xaeG\xe1'\xae@" p2002 tp2003 Rp2004 g1 (g5 S'\xd7\xa3p=\n(\xae@' p2005 tp2006 Rp2007 F5718.0 tp2008 a(I3660000 g1 (g5 S'\x9a\x99\x99\x99\x99H\xae@' p2009 tp2010 Rp2011 g1 (g5 S'\x9a\x99\x99\x99\x99H\xae@' p2012 tp2013 Rp2014 F4640.0 tp2015 a(I3670000 g1 (g5 S'fffff\x8a\xae@' p2016 tp2017 Rp2018 g1 (g5 S'fffff\x8a\xae@' p2019 tp2020 Rp2021 F5446.0 tp2022 a(I3680000 g1 (g5 S'\x8f\xc2\xf5(\\\xd1\xae@' p2023 tp2024 Rp2025 g1 (g5 S'\x8f\xc2\xf5(\\\xd1\xae@' p2026 tp2027 Rp2028 F5726.0 tp2029 a(I3690000 g1 (g5 S'\xc3\xf5(\\\x8f\xd8\xae@' p2030 tp2031 Rp2032 g1 (g5 S'\xc3\xf5(\\\x8f\xd8\xae@' p2033 tp2034 Rp2035 F4604.0 tp2036 a(I3700000 g1 (g5 S'\n\xd7\xa3p=\xd6\xae@' p2037 tp2038 Rp2039 g2035 F4140.0 tp2040 a(I3710000 g1 (g5 S'\xc3\xf5(\\\x8f\x97\xae@' p2041 tp2042 Rp2043 g1 (g5 S'33333\x13\xaf@' p2044 tp2045 Rp2046 F1952.0 tp2047 a(I3720000 g1 (g5 S'R\xb8\x1e\x85\xeb\xc2\xae@' p2048 tp2049 Rp2050 g2046 F5786.0 tp2051 a(I3730000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xb7\xae@' p2052 tp2053 Rp2054 g2046 F3060.0 tp2055 a(I3740000 g1 (g5 S'q=\n\xd7\xa3\x96\xaf@' p2056 tp2057 Rp2058 g1 (g5 S'q=\n\xd7\xa3\x96\xaf@' p2059 tp2060 Rp2061 F8512.0 tp2062 a(I3750000 g1 (g5 S'\xecQ\xb8\x1e\x85c\xaf@' p2063 tp2064 Rp2065 g1 (g5 S'\x85\xebQ\xb8\x1e\x9b\xaf@' p2066 tp2067 Rp2068 F1952.0 tp2069 a(I3760000 g1 (g5 S'33333\x8d\xaf@' p2070 tp2071 Rp2072 g2068 F6082.0 tp2073 a(I3770000 g1 (g5 S'H\xe1z\x14\xae\x9a\xaf@' p2074 tp2075 Rp2076 g2068 F5378.0 tp2077 a(I3780000 g1 (g5 S'q=\n\xd7\xa3\x90\xaf@' p2078 tp2079 Rp2080 g2068 F3900.0 tp2081 a(I3790000 g1 (g5 S'=\n\xd7\xa3p\x9d\xaf@' p2082 tp2083 Rp2084 g1 (g5 S'\xa4p=\n\xd7\xb7\xaf@' p2085 tp2086 Rp2087 F3000.0 tp2088 a(I3800000 g1 (g5 S'\xf6(\\\x8f\xc2\xed\xaf@' p2089 tp2090 Rp2091 g1 (g5 S'\xf6(\\\x8f\xc2\xed\xaf@' p2092 tp2093 Rp2094 F6576.0 tp2095 a(I3810000 g1 (g5 S'q=\n\xd7\xa3\xbc\xaf@' p2096 tp2097 Rp2098 g2094 F4960.0 tp2099 a(I3820000 g1 (g5 S'\xc3\xf5(\\\x0f\x04\xb0@' p2100 tp2101 Rp2102 g1 (g5 S'\xc3\xf5(\\\x0f\x04\xb0@' p2103 tp2104 Rp2105 F3900.0 tp2106 a(I3830000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xe5\xaf@' p2107 tp2108 Rp2109 g1 (g5 S'33333\x0e\xb0@' p2110 tp2111 Rp2112 F2004.0 tp2113 a(I3840000 g1 (g5 S'\xe1z\x14\xaeG\xc0\xaf@' p2114 tp2115 Rp2116 g2112 F3660.0 tp2117 a(I3850000 g1 (g5 S'{\x14\xaeG\xe1\xb4\xaf@' p2118 tp2119 Rp2120 g2112 F3900.0 tp2121 a(I3860000 g1 (g5 S"=\n\xd7\xa3p'\xb0@" p2122 tp2123 Rp2124 g1 (g5 S"=\n\xd7\xa3p'\xb0@" p2125 tp2126 Rp2127 F4156.0 tp2128 a(I3870000 g1 (g5 S'\xb8\x1e\x85\xeb\xd16\xb0@' p2129 tp2130 Rp2131 g1 (g5 S')\\\x8f\xc2uE\xb0@' p2132 tp2133 Rp2134 F1536.0 tp2135 a(I3880000 g1 (g5 S'H\xe1z\x14\xae2\xb0@' p2136 tp2137 Rp2138 g2134 F4448.0 tp2139 a(I3890000 g1 (g5 S'\xc3\xf5(\\\x0fg\xb0@' p2140 tp2141 Rp2142 g1 (g5 S'\xc3\xf5(\\\x0fg\xb0@' p2143 tp2144 Rp2145 F6894.0 tp2146 a(I3900000 g1 (g5 S'=\n\xd7\xa3p\x83\xb0@' p2147 tp2148 Rp2149 g1 (g5 S'=\n\xd7\xa3p\x83\xb0@' p2150 tp2151 Rp2152 F6978.0 tp2153 a(I3910000 g1 (g5 S'\x00\x00\x00\x00\x00\xa3\xb0@' p2154 tp2155 Rp2156 g1 (g5 S'\x00\x00\x00\x00\x00\xa3\xb0@' p2157 tp2158 Rp2159 F6164.0 tp2160 a(I3920000 g1 (g5 S'\xaeG\xe1z\x94[\xb0@' p2161 tp2162 Rp2163 g2159 F2160.0 tp2164 a(I3930000 g1 (g5 S'\\\x8f\xc2\xf5\xa8K\xb0@' p2165 tp2166 Rp2167 g2159 F4350.0 tp2168 a(I3940000 g1 (g5 S'\xe1z\x14\xae\xc7h\xb0@' p2169 tp2170 Rp2171 g2159 F4996.0 tp2172 a(I3950000 g1 (g5 S'\n\xd7\xa3p=v\xb0@' p2173 tp2174 Rp2175 g2159 F4768.0 tp2176 a(I3960000 g1 (g5 S'H\xe1z\x14.g\xb0@' p2177 tp2178 Rp2179 g2159 F4092.0 tp2180 a(I3970000 g1 (g5 S')\\\x8f\xc2u\x91\xb0@' p2181 tp2182 Rp2183 g2159 F3900.0 tp2184 a(I3980000 g1 (g5 S'\x8f\xc2\xf5(\xdc\xae\xb0@' p2185 tp2186 Rp2187 g1 (g5 S'\x8f\xc2\xf5(\xdc\xae\xb0@' p2188 tp2189 Rp2190 F6780.0 tp2191 a(I3990000 g1 (g5 S'\xf6(\\\x8f\xc2\xf5\xb0@' p2192 tp2193 Rp2194 g1 (g5 S'\xf6(\\\x8f\xc2\xf5\xb0@' p2195 tp2196 Rp2197 F4220.0 tp2198 a(I4000000 g1 (g5 S'\x9a\x99\x99\x99\x99\x0f\xb1@' p2199 tp2200 Rp2201 g1 (g5 S'\x9a\x99\x99\x99\x99\x0f\xb1@' p2202 tp2203 Rp2204 F3720.0 tp2205 a(I4010000 g1 (g5 S'=\n\xd7\xa3\xf0\x1a\xb1@' p2206 tp2207 Rp2208 g1 (g5 S'\n\xd7\xa3p=\x1b\xb1@' p2209 tp2210 Rp2211 F4670.0 tp2212 a(I4020000 g1 (g5 S'\x14\xaeG\xe1z)\xb1@' p2213 tp2214 Rp2215 g1 (g5 S'q=\n\xd7#A\xb1@' p2216 tp2217 Rp2218 F2944.0 tp2219 a(I4030000 g1 (g5 S'\x9a\x99\x99\x99\x99G\xb1@' p2220 tp2221 Rp2222 g1 (g5 S'\x9a\x99\x99\x99\x99G\xb1@' p2223 tp2224 Rp2225 F5440.0 tp2226 a(I4040000 g1 (g5 S'\x14\xaeG\xe1\xfa=\xb1@' p2227 tp2228 Rp2229 g1 (g5 S'\xaeG\xe1z\x94H\xb1@' p2230 tp2231 Rp2232 F4220.0 tp2233 a(I4050000 g1 (g5 S'{\x14\xaeGaS\xb1@' p2234 tp2235 Rp2236 g1 (g5 S'{\x14\xaeGaS\xb1@' p2237 tp2238 Rp2239 F5952.0 tp2240 a(I4060000 g1 (g5 S'\xc3\xf5(\\\x8f\x85\xb1@' p2241 tp2242 Rp2243 g1 (g5 S'\xc3\xf5(\\\x8f\x85\xb1@' p2244 tp2245 Rp2246 F5064.0 tp2247 a(I4070000 g1 (g5 S'\x8f\xc2\xf5(\xdc\xaa\xb1@' p2248 tp2249 Rp2250 g1 (g5 S'\x8f\xc2\xf5(\xdc\xaa\xb1@' p2251 tp2252 Rp2253 F6562.0 tp2254 a(I4080000 g1 (g5 S'\n\xd7\xa3p=\xe4\xb1@' p2255 tp2256 Rp2257 g1 (g5 S'\n\xd7\xa3p=\xe4\xb1@' p2258 tp2259 Rp2260 F6840.0 tp2261 a(I4090000 g1 (g5 S'R\xb8\x1e\x85k\x11\xb2@' p2262 tp2263 Rp2264 g1 (g5 S'R\xb8\x1e\x85k\x11\xb2@' p2265 tp2266 Rp2267 F4832.0 tp2268 a(I4100000 g1 (g5 S'\x9a\x99\x99\x99\x99\xe5\xb1@' p2269 tp2270 Rp2271 g2267 F2160.0 tp2272 a(I4110000 g1 (g5 S'\xe1z\x14\xaeG\xf5\xb1@' p2273 tp2274 Rp2275 g2267 F5084.0 tp2276 a(I4120000 g1 (g5 S'\\\x8f\xc2\xf5(\xef\xb1@' p2277 tp2278 Rp2279 g2267 F4410.0 tp2280 a(I4130000 g1 (g5 S'\x9a\x99\x99\x99\x19\x03\xb2@' p2281 tp2282 Rp2283 g2267 F6780.0 tp2284 a(I4140000 g1 (g5 S'H\xe1z\x14\xae&\xb2@' p2285 tp2286 Rp2287 g1 (g5 S'H\xe1z\x14\xae&\xb2@' p2288 tp2289 Rp2290 F7834.0 tp2291 a(I4150000 g1 (g5 S'\x9a\x99\x99\x99\x99/\xb2@' p2292 tp2293 Rp2294 g1 (g5 S'\x9a\x99\x99\x99\x99/\xb2@' p2295 tp2296 Rp2297 F4576.0 tp2298 a(I4160000 g1 (g5 S'\n\xd7\xa3p\xbdQ\xb2@' p2299 tp2300 Rp2301 g1 (g5 S'\n\xd7\xa3p\xbdQ\xb2@' p2302 tp2303 Rp2304 F8214.0 tp2305 a(I4170000 g1 (g5 S'\xb8\x1e\x85\xeb\xd1M\xb2@' p2306 tp2307 Rp2308 g1 (g5 S'\x1f\x85\xebQ\xb8i\xb2@' p2309 tp2310 Rp2311 F3240.0 tp2312 a(I4180000 g1 (g5 S'q=\n\xd7\xa3!\xb2@' p2313 tp2314 Rp2315 g2311 F4092.0 tp2316 a(I4190000 g1 (g5 S'\n\xd7\xa3p=\x14\xb2@' p2317 tp2318 Rp2319 g2311 F4704.0 tp2320 a(I4200000 g1 (g5 S'\xa4p=\n\xd7V\xb2@' p2321 tp2322 Rp2323 g2311 F5408.0 tp2324 a(I4210000 g1 (g5 S'\xcd\xcc\xcc\xcc\xccn\xb2@' p2325 tp2326 Rp2327 g1 (g5 S'\x85\xebQ\xb8\x1e\x83\xb2@' p2328 tp2329 Rp2330 F4050.0 tp2331 a(I4220000 g1 (g5 S'\xb8\x1e\x85\xeb\xd1\x87\xb2@' p2332 tp2333 Rp2334 g1 (g5 S'\xb8\x1e\x85\xeb\xd1\x87\xb2@' p2335 tp2336 Rp2337 F6342.0 tp2338 a(I4230000 g1 (g5 S'\xb8\x1e\x85\xeb\xd1\xeb\xb2@' p2339 tp2340 Rp2341 g1 (g5 S'\xb8\x1e\x85\xeb\xd1\xeb\xb2@' p2342 tp2343 Rp2344 F6728.0 tp2345 a(I4240000 g1 (g5 S'=\n\xd7\xa3\xf0\x04\xb3@' p2346 tp2347 Rp2348 g1 (g5 S'\n\xd7\xa3p\xbd\t\xb3@' p2349 tp2350 Rp2351 F5700.0 tp2352 a(I4250000 g1 (g5 S'\xecQ\xb8\x1e\x055\xb3@' p2353 tp2354 Rp2355 g1 (g5 S'\xecQ\xb8\x1e\x055\xb3@' p2356 tp2357 Rp2358 F6810.0 tp2359 a(I4260000 g1 (g5 S'\x8f\xc2\xf5(\xdcI\xb3@' p2360 tp2361 Rp2362 g1 (g5 S'\x8f\xc2\xf5(\xdcI\xb3@' p2363 tp2364 Rp2365 F8660.0 tp2366 a(I4270000 g1 (g5 S'\x8f\xc2\xf5(\xdcV\xb3@' p2367 tp2368 Rp2369 g1 (g5 S'\x8f\xc2\xf5(\xdcV\xb3@' p2370 tp2371 Rp2372 F5744.0 tp2373 a(I4280000 g1 (g5 S'{\x14\xaeG\xe1M\xb3@' p2374 tp2375 Rp2376 g2372 F4260.0 tp2377 a(I4290000 g1 (g5 S'\xcd\xcc\xcc\xccLo\xb3@' p2378 tp2379 Rp2380 g1 (g5 S'\xcd\xcc\xcc\xccLo\xb3@' p2381 tp2382 Rp2383 F5926.0 tp2384 a(I4300000 g1 (g5 S'\x8f\xc2\xf5(\\\xb6\xb3@' p2385 tp2386 Rp2387 g1 (g5 S'\x8f\xc2\xf5(\\\xb6\xb3@' p2388 tp2389 Rp2390 F4200.0 tp2391 a(I4310000 g1 (g5 S'\n\xd7\xa3p=\xe0\xb3@' p2392 tp2393 Rp2394 g1 (g5 S'\n\xd7\xa3p=\xe0\xb3@' p2395 tp2396 Rp2397 F2832.0 tp2398 a(I4320000 g1 (g5 S"=\n\xd7\xa3p'\xb4@" p2399 tp2400 Rp2401 g1 (g5 S"=\n\xd7\xa3p'\xb4@" p2402 tp2403 Rp2404 F5200.0 tp2405 a(I4330000 g1 (g5 S'\xb8\x1e\x85\xebQ7\xb4@' p2406 tp2407 Rp2408 g1 (g5 S'\xe1z\x14\xae\xc7;\xb4@' p2409 tp2410 Rp2411 F5054.0 tp2412 a(I4340000 g1 (g5 S'\xa4p=\n\xd7Z\xb4@' p2413 tp2414 Rp2415 g1 (g5 S'\xa4p=\n\xd7Z\xb4@' p2416 tp2417 Rp2418 F5820.0 tp2419 a(I4350000 g1 (g5 S'\xc3\xf5(\\\x8f`\xb4@' p2420 tp2421 Rp2422 g1 (g5 S'\x14\xaeG\xe1za\xb4@' p2423 tp2424 Rp2425 F5378.0 tp2426 a(I4360000 g1 (g5 S'ffff\xe6\x93\xb4@' p2427 tp2428 Rp2429 g1 (g5 S'ffff\xe6\x93\xb4@' p2430 tp2431 Rp2432 F5484.0 tp2433 a(I4370000 g1 (g5 S'\xb8\x1e\x85\xeb\xd1\xde\xb4@' p2434 tp2435 Rp2436 g1 (g5 S'\xb8\x1e\x85\xeb\xd1\xde\xb4@' p2437 tp2438 Rp2439 F10302.0 tp2440 a(I4380000 g1 (g5 S')\\\x8f\xc2\xf5\x8a\xb4@' p2441 tp2442 Rp2443 g2439 F1900.0 tp2444 a(I4390000 g1 (g5 S'33333\xa3\xb4@' p2445 tp2446 Rp2447 g2439 F7182.0 tp2448 a(I4400000 g1 (g5 S'33333\xb9\xb4@' p2449 tp2450 Rp2451 g2439 F6100.0 tp2452 a(I4410000 g1 (g5 S'\x85\xebQ\xb8\x9e\xf6\xb4@' p2453 tp2454 Rp2455 g1 (g5 S'\x9a\x99\x99\x99\x99\xfe\xb4@' p2456 tp2457 Rp2458 F4542.0 tp2459 a(I4420000 g1 (g5 S'\xc3\xf5(\\\x8f\xef\xb4@' p2460 tp2461 Rp2462 g2458 F4156.0 tp2463 a(I4430000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc$\xb5@' p2464 tp2465 Rp2466 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc$\xb5@' p2467 tp2468 Rp2469 F10320.0 tp2470 a(I4440000 g1 (g5 S'\xb8\x1e\x85\xebQ5\xb5@' p2471 tp2472 Rp2473 g1 (g5 S'fffffB\xb5@' p2474 tp2475 Rp2476 F4732.0 tp2477 a(I4450000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcci\xb5@' p2478 tp2479 Rp2480 g1 (g5 S'\xcd\xcc\xcc\xcc\xcci\xb5@' p2481 tp2482 Rp2483 F7800.0 tp2484 a(I4460000 g1 (g5 S'\x1f\x85\xebQ8\x9b\xb5@' p2485 tp2486 Rp2487 g1 (g5 S'\xecQ\xb8\x1e\x05\xa0\xb5@' p2488 tp2489 Rp2490 F3420.0 tp2491 a(I4470000 g1 (g5 S'=\n\xd7\xa3\xf0s\xb5@' p2492 tp2493 Rp2494 g2490 F5602.0 tp2495 a(I4480000 g1 (g5 S'H\xe1z\x14\xae\x88\xb5@' p2496 tp2497 Rp2498 g2490 F4860.0 tp2499 a(I4490000 g1 (g5 S'\xf6(\\\x8f\xc2\xb0\xb5@' p2500 tp2501 Rp2502 g1 (g5 S'\xf6(\\\x8f\xc2\xb0\xb5@' p2503 tp2504 Rp2505 F5540.0 tp2506 a(I4500000 g1 (g5 S'\xaeG\xe1z\x94\x86\xb5@' p2507 tp2508 Rp2509 g2505 F5340.0 tp2510 a(I4510000 g1 (g5 S'\x8f\xc2\xf5(\xdc\xa8\xb5@' p2511 tp2512 Rp2513 g2505 F9486.0 tp2514 a(I4520000 g1 (g5 S'=\n\xd7\xa3\xf0\x98\xb5@' p2515 tp2516 Rp2517 g2505 F6732.0 tp2518 a(I4530000 g1 (g5 S')\\\x8f\xc2\xf5\xaf\xb5@' p2519 tp2520 Rp2521 g2505 F5744.0 tp2522 a(I4540000 g1 (g5 S')\\\x8f\xc2u\x96\xb5@' p2523 tp2524 Rp2525 g2505 F4560.0 tp2526 a(I4550000 g1 (g5 S')\\\x8f\xc2u\x9c\xb5@' p2527 tp2528 Rp2529 g2505 F4320.0 tp2530 a(I4560000 g1 (g5 S'\n\xd7\xa3p=\xd4\xb5@' p2531 tp2532 Rp2533 g1 (g5 S'\x85\xebQ\xb8\x9e\xe3\xb5@' p2534 tp2535 Rp2536 F5024.0 tp2537 a(I4570000 g1 (g5 S'33333\xd0\xb5@' p2538 tp2539 Rp2540 g2536 F7164.0 tp2541 a(I4580000 g1 (g5 S'\xc3\xf5(\\\x0f\xd9\xb5@' p2542 tp2543 Rp2544 g1 (g5 S'{\x14\xaeGa\xea\xb5@' p2545 tp2546 Rp2547 F6300.0 tp2548 a(I4590000 g1 (g5 S'\\\x8f\xc2\xf5(\xf9\xb5@' p2549 tp2550 Rp2551 g1 (g5 S'\\\x8f\xc2\xf5(\xf9\xb5@' p2552 tp2553 Rp2554 F3060.0 tp2555 a(I4600000 g1 (g5 S'\xcd\xcc\xcc\xccL\x05\xb6@' p2556 tp2557 Rp2558 g1 (g5 S'\xcd\xcc\xcc\xccL\x05\xb6@' p2559 tp2560 Rp2561 F5154.0 tp2562 a(I4610000 g1 (g5 S'fffff\x01\xb6@' p2563 tp2564 Rp2565 g1 (g5 S'\xf6(\\\x8fB\x07\xb6@' p2566 tp2567 Rp2568 F4478.0 tp2569 a(I4620000 g1 (g5 S'\xcd\xcc\xcc\xccL2\xb6@' p2570 tp2571 Rp2572 g1 (g5 S'ffff\xe67\xb6@' p2573 tp2574 Rp2575 F4640.0 tp2576 a(I4630000 g1 (g5 S'\x85\xebQ\xb8\x9eE\xb6@' p2577 tp2578 Rp2579 g1 (g5 S'\x85\xebQ\xb8\x9eE\xb6@' p2580 tp2581 Rp2582 F8712.0 tp2583 a(I4640000 g1 (g5 S'q=\n\xd7#C\xb6@' p2584 tp2585 Rp2586 g1 (g5 S'\xb8\x1e\x85\xebQ]\xb6@' p2587 tp2588 Rp2589 F5216.0 tp2590 a(I4650000 g1 (g5 S'\x1f\x85\xebQ8n\xb6@' p2591 tp2592 Rp2593 g1 (g5 S'\x1f\x85\xebQ8n\xb6@' p2594 tp2595 Rp2596 F5700.0 tp2597 a(I4660000 g1 (g5 S'\xc3\xf5(\\\x0fS\xb6@' p2598 tp2599 Rp2600 g2596 F5756.0 tp2601 a(I4670000 g1 (g5 S'\xe1z\x14\xaeG\x82\xb6@' p2602 tp2603 Rp2604 g1 (g5 S'\xe1z\x14\xaeG\x82\xb6@' p2605 tp2606 Rp2607 F6062.0 tp2608 a(I4680000 g1 (g5 S'{\x14\xaeGa\x94\xb6@' p2609 tp2610 Rp2611 g1 (g5 S'{\x14\xaeGa\x94\xb6@' p2612 tp2613 Rp2614 F5410.0 tp2615 a(I4690000 g1 (g5 S')\\\x8f\xc2u\xc2\xb6@' p2616 tp2617 Rp2618 g1 (g5 S'\xa4p=\nW\xf9\xb6@' p2619 tp2620 Rp2621 F1796.0 tp2622 a(I4700000 g1 (g5 S'=\n\xd7\xa3p\xca\xb6@' p2623 tp2624 Rp2625 g2621 F7098.0 tp2626 a(I4710000 g1 (g5 S'\xaeG\xe1z\x14\xd3\xb6@' p2627 tp2628 Rp2629 g2621 F5280.0 tp2630 a(I4720000 g1 (g5 S'\xaeG\xe1z\x94\xb8\xb6@' p2631 tp2632 Rp2633 g2621 F2552.0 tp2634 a(I4730000 g1 (g5 S')\\\x8f\xc2\xf5\x97\xb6@' p2635 tp2636 Rp2637 g2621 F6096.0 tp2638 a(I4740000 g1 (g5 S'\xaeG\xe1z\x94\x9e\xb6@' p2639 tp2640 Rp2641 g2621 F4796.0 tp2642 a(I4750000 g1 (g5 S'\xaeG\xe1z\x14\x9a\xb6@' p2643 tp2644 Rp2645 g2621 F4576.0 tp2646 a(I4760000 g1 (g5 S'\xcd\xcc\xcc\xccL\x99\xb6@' p2647 tp2648 Rp2649 g2621 F6732.0 tp2650 a(I4770000 g1 (g5 S'fffff\xf4\xb6@' p2651 tp2652 Rp2653 g2621 F5880.0 tp2654 a(I4780000 g1 (g5 S'\x14\xaeG\xe1z\xef\xb6@' p2655 tp2656 Rp2657 g2621 F5280.0 tp2658 a(I4790000 g1 (g5 S'q=\n\xd7\xa3\x1a\xb7@' p2659 tp2660 Rp2661 g1 (g5 S"H\xe1z\x14.'\xb7@" p2662 tp2663 Rp2664 F4412.0 tp2665 a(I4800000 g1 (g5 S'\xc3\xf5(\\\x0f\xfd\xb6@' p2666 tp2667 Rp2668 g2664 F3300.0 tp2669 a(I4810000 g1 (g5 S')\\\x8f\xc2u\x13\xb7@' p2670 tp2671 Rp2672 g2664 F10230.0 tp2673 a(I4820000 g1 (g5 S'H\xe1z\x14\xae*\xb7@' p2674 tp2675 Rp2676 g1 (g5 S'H\xe1z\x14\xae*\xb7@' p2677 tp2678 Rp2679 F5154.0 tp2680 a(I4830000 g1 (g5 S'\xb8\x1e\x85\xeb\xd1\x1a\xb7@' p2681 tp2682 Rp2683 g1 (g5 S'\xf6(\\\x8f\xc26\xb7@' p2684 tp2685 Rp2686 F3140.0 tp2687 a(I4840000 g1 (g5 S'\xd7\xa3p=\n\x0e\xb7@' p2688 tp2689 Rp2690 g2686 F3360.0 tp2691 a(I4850000 g1 (g5 S'\xc3\xf5(\\\x8f\xe9\xb6@' p2692 tp2693 Rp2694 g2686 F3000.0 tp2695 a(I4860000 g1 (g5 S'fffff\xeb\xb6@' p2696 tp2697 Rp2698 g2686 F5246.0 tp2699 a(I4870000 g1 (g5 S'fffff\xb6\xb6@' p2700 tp2701 Rp2702 g2686 F4448.0 tp2703 a(I4880000 g1 (g5 S'\xaeG\xe1z\x14\xdf\xb6@' p2704 tp2705 Rp2706 g2686 F8224.0 tp2707 a(I4890000 g1 (g5 S'\xf6(\\\x8fBK\xb7@' p2708 tp2709 Rp2710 g1 (g5 S'\xf6(\\\x8fBK\xb7@' p2711 tp2712 Rp2713 F8024.0 tp2714 a(I4900000 g1 (g5 S'\x9a\x99\x99\x99\x99=\xb7@' p2715 tp2716 Rp2717 g2713 F5820.0 tp2718 a(I4910000 g1 (g5 S'q=\n\xd7#\xe0\xb6@' p2719 tp2720 Rp2721 g2713 F3600.0 tp2722 a(I4920000 g1 (g5 S'33333\xde\xb6@' p2723 tp2724 Rp2725 g2713 F6002.0 tp2726 a(I4930000 g1 (g5 S'\x00\x00\x00\x00\x80\x9a\xb6@' p2727 tp2728 Rp2729 g2713 F3840.0 tp2730 a(I4940000 g1 (g5 S'\x1f\x85\xebQ8\xc7\xb6@' p2731 tp2732 Rp2733 g2713 F5340.0 tp2734 a(I4950000 g1 (g5 S'\xe1z\x14\xae\xc7\xd6\xb6@' p2735 tp2736 Rp2737 g2713 F7188.0 tp2738 a(I4960000 g1 (g5 S'\x9a\x99\x99\x99\x99\xea\xb6@' p2739 tp2740 Rp2741 g2713 F5280.0 tp2742 a(I4970000 g1 (g5 S'q=\n\xd7#*\xb7@' p2743 tp2744 Rp2745 g2713 F6096.0 tp2746 a(I4980000 g1 (g5 S'\xb8\x1e\x85\xeb\xd1J\xb7@' p2747 tp2748 Rp2749 g2713 F8870.0 tp2750 a(I4990000 g1 (g5 S'\xaeG\xe1z\x14;\xb7@' p2751 tp2752 Rp2753 g2713 F5152.0 tp2754 a(I5000000 g1 (g5 S'\n\xd7\xa3p\xbdI\xb7@' p2755 tp2756 Rp2757 g1 (g5 S'\xaeG\xe1z\x94W\xb7@' p2758 tp2759 Rp2760 F4156.0 tp2761 a(I5010000 g1 (g5 S'{\x14\xaeGan\xb7@' p2762 tp2763 Rp2764 g1 (g5 S'{\x14\xaeGan\xb7@' p2765 tp2766 Rp2767 F5824.0 tp2768 a(I5020000 g1 (g5 S'\xe1z\x14\xae\xc7n\xb7@' p2769 tp2770 Rp2771 g1 (g5 S'\xecQ\xb8\x1e\x85\x99\xb7@' p2772 tp2773 Rp2774 F5212.0 tp2775 a(I5030000 g1 (g5 S'\n\xd7\xa3p=\x9b\xb7@' p2776 tp2777 Rp2778 g1 (g5 S'\n\xd7\xa3p=\x9b\xb7@' p2779 tp2780 Rp2781 F7446.0 tp2782 a(I5040000 g1 (g5 S'\xc3\xf5(\\\x0f\xb8\xb7@' p2783 tp2784 Rp2785 g1 (g5 S'\xc3\xf5(\\\x0f\xb8\xb7@' p2786 tp2787 Rp2788 F4508.0 tp2789 a(I5050000 g1 (g5 S'\x9a\x99\x99\x99\x99\xcb\xb7@' p2790 tp2791 Rp2792 g1 (g5 S'q=\n\xd7\xa3\xde\xb7@' p2793 tp2794 Rp2795 F3840.0 tp2796 a(I5060000 g1 (g5 S'R\xb8\x1e\x85\xeb\xcf\xb7@' p2797 tp2798 Rp2799 g2795 F5200.0 tp2800 a(I5070000 g1 (g5 S'\x1f\x85\xebQ8\t\xb8@' p2801 tp2802 Rp2803 g1 (g5 S'\x1f\x85\xebQ8\t\xb8@' p2804 tp2805 Rp2806 F10290.0 tp2807 a(I5080000 g1 (g5 S'fffff\x0f\xb8@' p2808 tp2809 Rp2810 g1 (g5 S'\xf6(\\\x8fBe\xb8@' p2811 tp2812 Rp2813 F3240.0 tp2814 a(I5090000 g1 (g5 S'\x8f\xc2\xf5(\\\x1e\xb8@' p2815 tp2816 Rp2817 g2813 F8660.0 tp2818 a(I5100000 g1 (g5 S'\xf6(\\\x8f\xc2\x0f\xb8@' p2819 tp2820 Rp2821 g2813 F7998.0 tp2822 a(I5110000 g1 (g5 S'\n\xd7\xa3p=J\xb8@' p2823 tp2824 Rp2825 g2813 F9960.0 tp2826 a(I5120000 g1 (g5 S'\n\xd7\xa3p=\x92\xb8@' p2827 tp2828 Rp2829 g1 (g5 S'\n\xd7\xa3p=\x92\xb8@' p2830 tp2831 Rp2832 F10260.0 tp2833 a(I5130000 g1 (g5 S'\x8f\xc2\xf5(\xdc\xfa\xb8@' p2834 tp2835 Rp2836 g1 (g5 S'\x8f\xc2\xf5(\xdc\xfa\xb8@' p2837 tp2838 Rp2839 F6852.0 tp2840 a(I5140000 g1 (g5 S'\xecQ\xb8\x1e\x05*\xb9@' p2841 tp2842 Rp2843 g1 (g5 S'\xecQ\xb8\x1e\x05*\xb9@' p2844 tp2845 Rp2846 F9870.0 tp2847 a(I5150000 g1 (g5 S'H\xe1z\x14\xae`\xb9@' p2848 tp2849 Rp2850 g1 (g5 S'H\xe1z\x14\xae`\xb9@' p2851 tp2852 Rp2853 F9030.0 tp2854 a(I5160000 g1 (g5 S'\x8f\xc2\xf5(\\T\xb9@' p2855 tp2856 Rp2857 g2853 F8628.0 tp2858 a(I5170000 g1 (g5 S'\xd7\xa3p=\x8a}\xb9@' p2859 tp2860 Rp2861 g1 (g5 S'\xd7\xa3p=\x8a}\xb9@' p2862 tp2863 Rp2864 F12510.0 tp2865 a(I5180000 g1 (g5 S'\xa4p=\n\xd7\x86\xb9@' p2866 tp2867 Rp2868 g1 (g5 S'\xa4p=\n\xd7\x86\xb9@' p2869 tp2870 Rp2871 F8232.0 tp2872 a(I5190000 g1 (g5 S'\xaeG\xe1z\x94\x8f\xb9@' p2873 tp2874 Rp2875 g1 (g5 S'\xaeG\xe1z\x94\x8f\xb9@' p2876 tp2877 Rp2878 F6456.0 tp2879 a(I5200000 g1 (g5 S'q=\n\xd7\xa3\xd3\xb9@' p2880 tp2881 Rp2882 g1 (g5 S'q=\n\xd7\xa3\xd3\xb9@' p2883 tp2884 Rp2885 F8388.0 tp2886 a(I5210000 g1 (g5 S'ffff\xe6\xee\xb9@' p2887 tp2888 Rp2889 g1 (g5 S'ffff\xe6\xee\xb9@' p2890 tp2891 Rp2892 F7128.0 tp2893 a(I5220000 g1 (g5 S'\x85\xebQ\xb8\x9e\xfe\xb9@' p2894 tp2895 Rp2896 g1 (g5 S'\x85\xebQ\xb8\x9e\xfe\xb9@' p2897 tp2898 Rp2899 F7192.0 tp2900 a(I5230000 g1 (g5 S'\x8f\xc2\xf5(\xdc\x1f\xba@' p2901 tp2902 Rp2903 g1 (g5 S'\x8f\xc2\xf5(\xdc\x1f\xba@' p2904 tp2905 Rp2906 F7112.0 tp2907 a(I5240000 g1 (g5 S'\xcd\xcc\xcc\xccL\xc2\xb9@' p2908 tp2909 Rp2910 g2906 F4832.0 tp2911 a(I5250000 g1 (g5 S'\x8f\xc2\xf5(\\?\xba@' p2912 tp2913 Rp2914 g1 (g5 S'\x8f\xc2\xf5(\\?\xba@' p2915 tp2916 Rp2917 F6456.0 tp2918 a(I5260000 g1 (g5 S'q=\n\xd7\xa38\xba@' p2919 tp2920 Rp2921 g1 (g5 S'\x9a\x99\x99\x99\x19A\xba@' p2922 tp2923 Rp2924 F7650.0 tp2925 a(I5270000 g1 (g5 S'=\n\xd7\xa3p\x81\xba@' p2926 tp2927 Rp2928 g1 (g5 S'=\n\xd7\xa3p\x81\xba@' p2929 tp2930 Rp2931 F10760.0 tp2932 a(I5280000 g1 (g5 S'\xf6(\\\x8fB\x91\xba@' p2933 tp2934 Rp2935 g1 (g5 S'\xf6(\\\x8fB\x91\xba@' p2936 tp2937 Rp2938 F6322.0 tp2939 a(I5290000 g1 (g5 S'\xaeG\xe1z\x14\xad\xba@' p2940 tp2941 Rp2942 g1 (g5 S'\xcd\xcc\xcc\xccL\xb1\xba@' p2943 tp2944 Rp2945 F7498.0 tp2946 a(I5300000 g1 (g5 S'\x85\xebQ\xb8\x1e\xe6\xba@' p2947 tp2948 Rp2949 g1 (g5 S'\x85\xebQ\xb8\x1e\xe6\xba@' p2950 tp2951 Rp2952 F9390.0 tp2953 a(I5310000 g1 (g5 S'=\n\xd7\xa3p\xf1\xba@' p2954 tp2955 Rp2956 g1 (g5 S'=\n\xd7\xa3p\xf1\xba@' p2957 tp2958 Rp2959 F7620.0 tp2960 a(I5320000 g1 (g5 S'H\xe1z\x14.\x04\xbb@' p2961 tp2962 Rp2963 g1 (g5 S'\x85\xebQ\xb8\x1e\x10\xbb@' p2964 tp2965 Rp2966 F5538.0 tp2967 a(I5330000 g1 (g5 S'\n\xd7\xa3p\xbd\x8d\xba@' p2968 tp2969 Rp2970 g2966 F6810.0 tp2971 a(I5340000 g1 (g5 S'3333\xb3\x8c\xba@' p2972 tp2973 Rp2974 g2966 F5756.0 tp2975 a(I5350000 g1 (g5 S'\xd7\xa3p=\x8a,\xba@' p2976 tp2977 Rp2978 g2966 F3300.0 tp2979 a(I5360000 g1 (g5 S'q=\n\xd7#k\xba@' p2980 tp2981 Rp2982 g2966 F9560.0 tp2983 a(I5370000 g1 (g5 S'\xa4p=\n\xd7>\xba@' p2984 tp2985 Rp2986 g2966 F5200.0 tp2987 a(I5380000 g1 (g5 S'\xaeG\xe1z\x149\xba@' p2988 tp2989 Rp2990 g2966 F7212.0 tp2991 a(I5390000 g1 (g5 S'\xa4p=\nW~\xba@' p2992 tp2993 Rp2994 g2966 F5544.0 tp2995 a(I5400000 g1 (g5 S'\xaeG\xe1z\x94\xb1\xba@' p2996 tp2997 Rp2998 g2966 F10400.0 tp2999 a(I5410000 g1 (g5 S')\\\x8f\xc2\xf5\x03\xbb@' p3000 tp3001 Rp3002 g2966 F11598.0 tp3003 a(I5420000 g1 (g5 S'=\n\xd7\xa3\xf0f\xbb@' p3004 tp3005 Rp3006 g1 (g5 S'=\n\xd7\xa3\xf0f\xbb@' p3007 tp3008 Rp3009 F9124.0 tp3010 a(I5430000 g1 (g5 S'\x14\xaeG\xe1\xfaK\xbb@' p3011 tp3012 Rp3013 g1 (g5 S'\xa4p=\n\xd7j\xbb@' p3014 tp3015 Rp3016 F2160.0 tp3017 a(I5440000 g1 (g5 S'\x85\xebQ\xb8\x9eZ\xbb@' p3018 tp3019 Rp3020 g3016 F8820.0 tp3021 a(I5450000 g1 (g5 S'q=\n\xd7#\xb8\xbb@' p3022 tp3023 Rp3024 g1 (g5 S'q=\n\xd7#\xb8\xbb@' p3025 tp3026 Rp3027 F5130.0 tp3028 a(I5460000 g1 (g5 S'33333\x93\xbb@' p3029 tp3030 Rp3031 g3027 F8584.0 tp3032 a(I5470000 g1 (g5 S'\x9a\x99\x99\x99\x99\xa1\xbb@' p3033 tp3034 Rp3035 g3027 F8482.0 tp3036 a(I5480000 g1 (g5 S'\xa4p=\nW\xac\xbb@' p3037 tp3038 Rp3039 g3027 F6894.0 tp3040 a(I5490000 g1 (g5 S'\xaeG\xe1z\x14\xcf\xbb@' p3041 tp3042 Rp3043 g1 (g5 S'\xaeG\xe1z\x14\xcf\xbb@' p3044 tp3045 Rp3046 F8214.0 tp3047 a(I5500000 g1 (g5 S'\x1f\x85\xebQ\xb8J\xbc@' p3048 tp3049 Rp3050 g1 (g5 S'\x1f\x85\xebQ\xb8J\xbc@' p3051 tp3052 Rp3053 F10230.0 tp3054 a(I5510000 g1 (g5 S'\n\xd7\xa3p\xbd\x99\xbc@' p3055 tp3056 Rp3057 g1 (g5 S'\n\xd7\xa3p\xbd\x99\xbc@' p3058 tp3059 Rp3060 F8644.0 tp3061 a(I5520000 g1 (g5 S'\x14\xaeG\xe1\xfa\x90\xbc@' p3062 tp3063 Rp3064 g3060 F9174.0 tp3065 a(I5530000 g1 (g5 S'q=\n\xd7\xa3\xdc\xbc@' p3066 tp3067 Rp3068 g1 (g5 S'\xe1z\x14\xae\xc7\xef\xbc@' p3069 tp3070 Rp3071 F5090.0 tp3072 a(I5540000 g1 (g5 S'q=\n\xd7\xa3\xe6\xbc@' p3073 tp3074 Rp3075 g3071 F6340.0 tp3076 a(I5550000 g1 (g5 S'\xaeG\xe1z\x94\xb6\xbc@' p3077 tp3078 Rp3079 g3071 F7170.0 tp3080 a(I5560000 g1 (g5 S'H\xe1z\x14.\xb1\xbc@' p3081 tp3082 Rp3083 g3071 F3900.0 tp3084 a(I5570000 g1 (g5 S'ffff\xe6\x81\xbc@' p3085 tp3086 Rp3087 g3071 F3140.0 tp3088 a(I5580000 g1 (g5 S'H\xe1z\x14\xae\xb8\xbc@' p3089 tp3090 Rp3091 g3071 F10630.0 tp3092 a(I5590000 g1 (g5 S'\xc3\xf5(\\\x0f\xd1\xbc@' p3093 tp3094 Rp3095 g3071 F5884.0 tp3096 a(I5600000 g1 (g5 S'\xb8\x1e\x85\xebQ%\xbd@' p3097 tp3098 Rp3099 g1 (g5 S'\xb8\x1e\x85\xebQ%\xbd@' p3100 tp3101 Rp3102 F14250.0 tp3103 a(I5610000 g1 (g5 S'=\n\xd7\xa3p\xe9\xbc@' p3104 tp3105 Rp3106 g3102 F4050.0 tp3107 a(I5620000 g1 (g5 S'\x85\xebQ\xb8\x9e\x19\xbd@' p3108 tp3109 Rp3110 g3102 F10030.0 tp3111 a(I5630000 g1 (g5 S'\x1f\x85\xebQ\xb8\x93\xbd@' p3112 tp3113 Rp3114 g1 (g5 S'\x1f\x85\xebQ\xb8\x93\xbd@' p3115 tp3116 Rp3117 F19656.0 tp3118 a(I5640000 g1 (g5 S'\x1f\x85\xebQ\xb8\x95\xbd@' p3119 tp3120 Rp3121 g1 (g5 S'\x1f\x85\xebQ\xb8\x95\xbd@' p3122 tp3123 Rp3124 F9270.0 tp3125 a(I5650000 g1 (g5 S'\xc3\xf5(\\\x0f\xb8\xbd@' p3126 tp3127 Rp3128 g1 (g5 S'\xd7\xa3p=\x8a\xcb\xbd@' p3129 tp3130 Rp3131 F6742.0 tp3132 a(I5660000 g1 (g5 S'\xc3\xf5(\\\x0f\xe5\xbd@' p3133 tp3134 Rp3135 g1 (g5 S'\xc3\xf5(\\\x0f\xe5\xbd@' p3136 tp3137 Rp3138 F8340.0 tp3139 a(I5670000 g1 (g5 S'\x1f\x85\xebQ\xb8\x08\xbe@' p3140 tp3141 Rp3142 g1 (g5 S'\xf6(\\\x8fB\x18\xbe@' p3143 tp3144 Rp3145 F8736.0 tp3146 a(I5680000 g1 (g5 S')\\\x8f\xc2u\xd4\xbd@' p3147 tp3148 Rp3149 g3145 F8298.0 tp3150 a(I5690000 g1 (g5 S'fffff.\xbe@' p3151 tp3152 Rp3153 g1 (g5 S'fffff.\xbe@' p3154 tp3155 Rp3156 F14372.0 tp3157 a(I5700000 g1 (g5 S'3333\xb3\x80\xbe@' p3158 tp3159 Rp3160 g1 (g5 S'3333\xb3\x80\xbe@' p3161 tp3162 Rp3163 F10980.0 tp3164 a(I5710000 g1 (g5 S'\x85\xebQ\xb8\x1e\x93\xbe@' p3165 tp3166 Rp3167 g1 (g5 S'\x85\xebQ\xb8\x1e\x93\xbe@' p3168 tp3169 Rp3170 F9840.0 tp3171 a(I5720000 g1 (g5 S'\x8f\xc2\xf5(\\~\xbe@' p3172 tp3173 Rp3174 g1 (g5 S'R\xb8\x1e\x85\xeb\x9e\xbe@' p3175 tp3176 Rp3177 F6704.0 tp3178 a(I5730000 g1 (g5 S'\x8f\xc2\xf5(\xdcx\xbe@' p3179 tp3180 Rp3181 g3177 F9710.0 tp3182 a(I5740000 g1 (g5 S'\x85\xebQ\xb8\x1eh\xbe@' p3183 tp3184 Rp3185 g3177 F9336.0 tp3186 a(I5750000 g1 (g5 S'fffff@\xbe@' p3187 tp3188 Rp3189 g3177 F4860.0 tp3190 a(I5760000 g1 (g5 S'\xb8\x1e\x85\xeb\xd1z\xbe@' p3191 tp3192 Rp3193 g3177 F12036.0 tp3194 a(I5770000 g1 (g5 S')\\\x8f\xc2u\x89\xbe@' p3195 tp3196 Rp3197 g3177 F10494.0 tp3198 a(I5780000 g1 (g5 S'{\x14\xaeGa\xfc\xbe@' p3199 tp3200 Rp3201 g1 (g5 S'{\x14\xaeGa\xfc\xbe@' p3202 tp3203 Rp3204 F10978.0 tp3205 a(I5790000 g1 (g5 S'\x85\xebQ\xb8\x9e\xbc\xbe@' p3206 tp3207 Rp3208 g3204 F5880.0 tp3209 a(I5800000 g1 (g5 S'\x85\xebQ\xb8\x9e\xbc\xbe@' p3210 tp3211 Rp3212 g3204 F5880.0 tp3213 a(I5810000 g1 (g5 S'\xa4p=\n\xd7X\xbf@' p3214 tp3215 Rp3216 g1 (g5 S'\xa4p=\n\xd7X\xbf@' p3217 tp3218 Rp3219 F11532.0 tp3220 a(I5820000 g1 (g5 S'\xecQ\xb8\x1e\x05\x07\xbf@' p3221 tp3222 Rp3223 g3219 F4350.0 tp3224 a(I5830000 g1 (g5 S'\xaeG\xe1z\x94*\xbf@' p3225 tp3226 Rp3227 g3219 F6240.0 tp3228 a(I5840000 g1 (g5 S'{\x14\xaeG\xe1*\xbf@' p3229 tp3230 Rp3231 g3219 F8330.0 tp3232 a(I5850000 g1 (g5 S'\xc3\xf5(\\\x8f\x19\xbf@' p3233 tp3234 Rp3235 g3219 F3964.0 tp3236 a(I5860000 g1 (g5 S'\x8f\xc2\xf5(\xdc(\xbf@' p3237 tp3238 Rp3239 g3219 F11928.0 tp3240 a(I5870000 g1 (g5 S'\x8f\xc2\xf5(\xdc>\xbf@' p3241 tp3242 Rp3243 g3219 F6804.0 tp3244 a(I5880000 g1 (g5 S'\xaeG\xe1z\x142\xbf@' p3245 tp3246 Rp3247 g3219 F6372.0 tp3248 a(I5890000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xf4\xbe@' p3249 tp3250 Rp3251 g3219 F5024.0 tp3252 a(I5900000 g1 (g5 S'{\x14\xaeG\xe1\xde\xbe@' p3253 tp3254 Rp3255 g3219 F7098.0 tp3256 a(I5910000 g1 (g5 S'\xf6(\\\x8f\xc2\xe3\xbe@' p3257 tp3258 Rp3259 g3219 F8208.0 tp3260 a(I5920000 g1 (g5 S'\x85\xebQ\xb8\x1e\xcc\xbe@' p3261 tp3262 Rp3263 g3219 F7026.0 tp3264 a(I5930000 g1 (g5 S'\x14\xaeG\xe1z\x07\xbf@' p3265 tp3266 Rp3267 g3219 F5246.0 tp3268 a(I5940000 g1 (g5 S'\x8f\xc2\xf5(\\\x0b\xbf@' p3269 tp3270 Rp3271 g3219 F8162.0 tp3272 a(I5950000 g1 (g5 S')\\\x8f\xc2\xf5F\xbf@' p3273 tp3274 Rp3275 g3219 F8210.0 tp3276 a(I5960000 g1 (g5 S'3333\xb3i\xbf@' p3277 tp3278 Rp3279 g1 (g5 S'3333\xb3i\xbf@' p3280 tp3281 Rp3282 F7758.0 tp3283 a(I5970000 g1 (g5 S'\x1f\x85\xebQ8\x8b\xbf@' p3284 tp3285 Rp3286 g1 (g5 S'\x8f\xc2\xf5(\xdc\x91\xbf@' p3287 tp3288 Rp3289 F3720.0 tp3290 a(I5980000 g1 (g5 S'\x00\x00\x00\x00\x00\xfc\xbf@' p3291 tp3292 Rp3293 g1 (g5 S'\x00\x00\x00\x00\x00\xfc\xbf@' p3294 tp3295 Rp3296 F6508.0 tp3297 a(I5990000 g1 (g5 S'\xcd\xcc\xcc\xccL\xf3\xbf@' p3298 tp3299 Rp3300 g3296 F7154.0 tp3301 a(I6000000 g1 (g5 S'H\xe1z\x14n\x0c\xc0@' p3302 tp3303 Rp3304 g1 (g5 S'H\xe1z\x14n\x0c\xc0@' p3305 tp3306 Rp3307 F8956.0 tp3308 a(I6010000 g1 (g5 S'\x8f\xc2\xf5(\x9c\x1f\xc0@' p3309 tp3310 Rp3311 g1 (g5 S'\xcd\xcc\xcc\xcc\x8c%\xc0@' p3312 tp3313 Rp3314 F6024.0 tp3315 a(I6020000 g1 (g5 S'\xa4p=\n\x97\x0b\xc0@' p3316 tp3317 Rp3318 g3314 F6716.0 tp3319 a(I6030000 g1 (g5 S'\xaeG\xe1zT%\xc0@' p3320 tp3321 Rp3322 g3314 F10692.0 tp3323 a(I6040000 g1 (g5 S'\x85\xebQ\xb8\x1e!\xc0@' p3324 tp3325 Rp3326 g1 (g5 S'\xc3\xf5(\\\xcf6\xc0@' p3327 tp3328 Rp3329 F7260.0 tp3330 a(I6050000 g1 (g5 S'\xe1z\x14\xae\xc7\x0f\xc0@' p3331 tp3332 Rp3333 g3329 F5990.0 tp3334 a(I6060000 g1 (g5 S'\xf6(\\\x8f\xc2+\xc0@' p3335 tp3336 Rp3337 g3329 F7284.0 tp3338 a(I6070000 g1 (g5 S'fffff\xf3\xbf@' p3339 tp3340 Rp3341 g3329 F6644.0 tp3342 a(I6080000 g1 (g5 S'\xa4p=\n\x97\n\xc0@' p3343 tp3344 Rp3345 g3329 F8508.0 tp3346 a(I6090000 g1 (g5 S"\xb8\x1e\x85\xeb\x91'\xc0@" p3347 tp3348 Rp3349 g3329 F9700.0 tp3350 a(I6100000 g1 (g5 S'\x8f\xc2\xf5(\x1c/\xc0@' p3351 tp3352 Rp3353 g3329 F7702.0 tp3354 a(I6110000 g1 (g5 S'\xb8\x1e\x85\xeb\x912\xc0@' p3355 tp3356 Rp3357 g1 (g5 S'\xd7\xa3p=J?\xc0@' p3358 tp3359 Rp3360 F4350.0 tp3361 a(I6120000 g1 (g5 S'H\xe1z\x14\xee9\xc0@' p3362 tp3363 Rp3364 g3360 F8568.0 tp3365 a(I6130000 g1 (g5 S'H\xe1z\x14\xae\x1a\xc0@' p3366 tp3367 Rp3368 g3360 F6508.0 tp3369 a(I6140000 g1 (g5 S'R\xb8\x1e\x85\xab4\xc0@' p3370 tp3371 Rp3372 g3360 F13842.0 tp3373 a(I6150000 g1 (g5 S'ffff\xe6\x00\xc0@' p3374 tp3375 Rp3376 g3360 F6924.0 tp3377 a(I6160000 g1 (g5 S'\x9a\x99\x99\x99Y$\xc0@' p3378 tp3379 Rp3380 g3360 F12180.0 tp3381 a(I6170000 g1 (g5 S'\n\xd7\xa3p}\x1d\xc0@' p3382 tp3383 Rp3384 g3360 F7494.0 tp3385 a(I6180000 g1 (g5 S'\xaeG\xe1z\x14\t\xc0@' p3386 tp3387 Rp3388 g3360 F4768.0 tp3389 a(I6190000 g1 (g5 S')\\\x8f\xc2\xf5\x1d\xc0@' p3390 tp3391 Rp3392 g3360 F2832.0 tp3393 a(I6200000 g1 (g5 S'\x8f\xc2\xf5(\\4\xc0@' p3394 tp3395 Rp3396 g3360 F6810.0 tp3397 a(I6210000 g1 (g5 S'3333s\x05\xc0@' p3398 tp3399 Rp3400 g3360 F6546.0 tp3401 a(I6220000 g1 (g5 S'\x85\xebQ\xb8\x9e\xc0\xbf@' p3402 tp3403 Rp3404 g3360 F5812.0 tp3405 a(I6230000 g1 (g5 S'{\x14\xaeGa\xdb\xbf@' p3406 tp3407 Rp3408 g3360 F8232.0 tp3409 a(I6240000 g1 (g5 S'\x8f\xc2\xf5(\xdc=\xbf@' p3410 tp3411 Rp3412 g3360 F7914.0 tp3413 a(I6250000 g1 (g5 S'\x14\xaeG\xe1zv\xbf@' p3414 tp3415 Rp3416 g3360 F14932.0 tp3417 a(I6260000 g1 (g5 S'33333H\xbf@' p3418 tp3419 Rp3420 g3360 F4576.0 tp3421 a(I6270000 g1 (g5 S'\x00\x00\x00\x00\x80\xf7\xbe@' p3422 tp3423 Rp3424 g3360 F8430.0 tp3425 a(I6280000 g1 (g5 S'=\n\xd7\xa3\xf0\x0f\xbf@' p3426 tp3427 Rp3428 g3360 F11180.0 tp3429 a(I6290000 g1 (g5 S'\x9a\x99\x99\x99\x197\xbf@' p3430 tp3431 Rp3432 g3360 F12214.0 tp3433 a(I6300000 g1 (g5 S'\xc3\xf5(\\\x8f(\xbf@' p3434 tp3435 Rp3436 g3360 F9410.0 tp3437 a(I6310000 g1 (g5 S'\xcd\xcc\xcc\xccL-\xbf@' p3438 tp3439 Rp3440 g3360 F11454.0 tp3441 a(I6320000 g1 (g5 S'\xd7\xa3p=\n\x04\xbf@' p3442 tp3443 Rp3444 g3360 F5730.0 tp3445 a(I6330000 g1 (g5 S'\xf6(\\\x8fB\xfc\xbe@' p3446 tp3447 Rp3448 g3360 F5926.0 tp3449 a(I6340000 g1 (g5 S'\x85\xebQ\xb8\x9e\xeb\xbe@' p3450 tp3451 Rp3452 g3360 F8322.0 tp3453 a(I6350000 g1 (g5 S'\xe1z\x14\xaeG\xd7\xbe@' p3454 tp3455 Rp3456 g3360 F5856.0 tp3457 a(I6360000 g1 (g5 S'\xecQ\xb8\x1e\x05\xf1\xbe@' p3458 tp3459 Rp3460 g3360 F9900.0 tp3461 a(I6370000 g1 (g5 S'\\\x8f\xc2\xf5(\xd7\xbe@' p3462 tp3463 Rp3464 g3360 F7908.0 tp3465 a(I6380000 g1 (g5 S'\n\xd7\xa3p\xbd\x9c\xbe@' p3466 tp3467 Rp3468 g3360 F8260.0 tp3469 a(I6390000 g1 (g5 S'\n\xd7\xa3p\xbd\xe2\xbe@' p3470 tp3471 Rp3472 g3360 F6146.0 tp3473 a(I6400000 g1 (g5 S'\x85\xebQ\xb8\x1e\xea\xbe@' p3474 tp3475 Rp3476 g3360 F6618.0 tp3477 a(I6410000 g1 (g5 S'H\xe1z\x14\xae\xa2\xbe@' p3478 tp3479 Rp3480 g3360 F6198.0 tp3481 a(I6420000 g1 (g5 S'=\n\xd7\xa3p\xba\xbe@' p3482 tp3483 Rp3484 g3360 F7692.0 tp3485 a(I6430000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xf9\xbe@' p3486 tp3487 Rp3488 g3360 F9396.0 tp3489 a(I6440000 g1 (g5 S'q=\n\xd7\xa3<\xbf@' p3490 tp3491 Rp3492 g3360 F12924.0 tp3493 a(I6450000 g1 (g5 S'\xaeG\xe1z\x94#\xbf@' p3494 tp3495 Rp3496 g3360 F6546.0 tp3497 a(I6460000 g1 (g5 S'\x1f\x85\xebQ8j\xbf@' p3498 tp3499 Rp3500 g3360 F13312.0 tp3501 a(I6470000 g1 (g5 S'\x85\xebQ\xb8\x9e\xa4\xbf@' p3502 tp3503 Rp3504 g3360 F9804.0 tp3505 a(I6480000 g1 (g5 S'\x9a\x99\x99\x99\x997\xbf@' p3506 tp3507 Rp3508 g3360 F4680.0 tp3509 a(I6490000 g1 (g5 S'\n\xd7\xa3p=T\xbf@' p3510 tp3511 Rp3512 g3360 F8720.0 tp3513 a(I6500000 g1 (g5 S'\xaeG\xe1z\x14\x98\xbf@' p3514 tp3515 Rp3516 g3360 F15444.0 tp3517 a(I6510000 g1 (g5 S'\x00\x00\x00\x00\x80\xfa\xbf@' p3518 tp3519 Rp3520 g3360 F12842.0 tp3521 a(I6520000 g1 (g5 S'q=\n\xd7c.\xc0@' p3522 tp3523 Rp3524 g3360 F5070.0 tp3525 a(I6530000 g1 (g5 S'ffff\xe6*\xc0@' p3526 tp3527 Rp3528 g3360 F9716.0 tp3529 a(I6540000 g1 (g5 S'\xb8\x1e\x85\xeb\x11)\xc0@' p3530 tp3531 Rp3532 g3360 F6660.0 tp3533 a(I6550000 g1 (g5 S'\xaeG\xe1z\x14.\xc0@' p3534 tp3535 Rp3536 g3360 F7652.0 tp3537 a(I6560000 g1 (g5 S'q=\n\xd7c8\xc0@' p3538 tp3539 Rp3540 g3360 F7470.0 tp3541 a(I6570000 g1 (g5 S'q=\n\xd7#7\xc0@' p3542 tp3543 Rp3544 g3360 F9640.0 tp3545 a(I6580000 g1 (g5 S'\xc3\xf5(\\\x0f)\xc0@' p3546 tp3547 Rp3548 g3360 F6096.0 tp3549 a(I6590000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc"\xc0@' p3550 tp3551 Rp3552 g3360 F7920.0 tp3553 a(I6600000 g1 (g5 S'\x00\x00\x00\x00\x00H\xc0@' p3554 tp3555 Rp3556 g1 (g5 S'\x00\x00\x00\x00\x00H\xc0@' p3557 tp3558 Rp3559 F18930.0 tp3560 a(I6610000 g1 (g5 S'q=\n\xd7\xa3C\xc0@' p3561 tp3562 Rp3563 g3559 F5636.0 tp3564 a(I6620000 g1 (g5 S'\xb8\x1e\x85\xeb\x11B\xc0@' p3565 tp3566 Rp3567 g3559 F7692.0 tp3568 a(I6630000 g1 (g5 S'\xecQ\xb8\x1eE3\xc0@' p3569 tp3570 Rp3571 g1 (g5 S'\xd7\xa3p=\x8aL\xc0@' p3572 tp3573 Rp3574 F6126.0 tp3575 a(I6640000 g1 (g5 S'\x14\xaeG\xe1\xfa7\xc0@' p3576 tp3577 Rp3578 g3574 F8886.0 tp3579 a(I6650000 g1 (g5 S'\xecQ\xb8\x1e\x855\xc0@' p3580 tp3581 Rp3582 g3574 F10200.0 tp3583 a(I6660000 g1 (g5 S'\xf6(\\\x8fB\x1c\xc0@' p3584 tp3585 Rp3586 g3574 F9010.0 tp3587 a(I6670000 g1 (g5 S'\n\xd7\xa3p}.\xc0@' p3588 tp3589 Rp3590 g3574 F11946.0 tp3591 a(I6680000 g1 (g5 S'fffff*\xc0@' p3592 tp3593 Rp3594 g3574 F5440.0 tp3595 a(I6690000 g1 (g5 S'\xcd\xcc\xcc\xcc\x8c\x1c\xc0@' p3596 tp3597 Rp3598 g3574 F4606.0 tp3599 a(I6700000 g1 (g5 S'\xecQ\xb8\x1e\xc5+\xc0@' p3600 tp3601 Rp3602 g3574 F7896.0 tp3603 a(I6710000 g1 (g5 S'\xf6(\\\x8f\x824\xc0@' p3604 tp3605 Rp3606 g3574 F10968.0 tp3607 a(I6720000 g1 (g5 S'\xcd\xcc\xcc\xcc\x8c"\xc0@' p3608 tp3609 Rp3610 g3574 F7098.0 tp3611 a(I6730000 g1 (g5 S'\\\x8f\xc2\xf5\xe8\x1c\xc0@' p3612 tp3613 Rp3614 g3574 F10590.0 tp3615 a(I6740000 g1 (g5 S"R\xb8\x1e\x85\xab'\xc0@" p3616 tp3617 Rp3618 g3574 F8444.0 tp3619 a(I6750000 g1 (g5 S'\xaeG\xe1z\xd4B\xc0@' p3620 tp3621 Rp3622 g3574 F14000.0 tp3623 a(I6760000 g1 (g5 S'\x14\xaeG\xe1\xfa[\xc0@' p3624 tp3625 Rp3626 g1 (g5 S'\x14\xaeG\xe1\xfa[\xc0@' p3627 tp3628 Rp3629 F8310.0 tp3630 a(I6770000 g1 (g5 S'\x8f\xc2\xf5(\\M\xc0@' p3631 tp3632 Rp3633 g3629 F5880.0 tp3634 a(I6780000 g1 (g5 S'\xecQ\xb8\x1e\xc5A\xc0@' p3635 tp3636 Rp3637 g3629 F4606.0 tp3638 a(I6790000 g1 (g5 S'q=\n\xd7\xe3/\xc0@' p3639 tp3640 Rp3641 g3629 F3990.0 tp3642 a(I6800000 g1 (g5 S'\xcd\xcc\xcc\xcc\x0c9\xc0@' p3643 tp3644 Rp3645 g3629 F6712.0 tp3646 a(I6810000 g1 (g5 S'q=\n\xd7c\x1f\xc0@' p3647 tp3648 Rp3649 g3629 F6864.0 tp3650 a(I6820000 g1 (g5 S'\xc3\xf5(\\OS\xc0@' p3651 tp3652 Rp3653 g3629 F13216.0 tp3654 a(I6830000 g1 (g5 S'\xaeG\xe1z\x94V\xc0@' p3655 tp3656 Rp3657 g3629 F8226.0 tp3658 a(I6840000 g1 (g5 S')\\\x8f\xc2\xf5\x8c\xc0@' p3659 tp3660 Rp3661 g1 (g5 S')\\\x8f\xc2\xf5\x8c\xc0@' p3662 tp3663 Rp3664 F10360.0 tp3665 a(I6850000 g1 (g5 S'\x85\xebQ\xb8^\x80\xc0@' p3666 tp3667 Rp3668 g3664 F4028.0 tp3669 a(I6860000 g1 (g5 S'\x14\xaeG\xe1z\x9d\xc0@' p3670 tp3671 Rp3672 g1 (g5 S'\x14\xaeG\xe1z\x9d\xc0@' p3673 tp3674 Rp3675 F12716.0 tp3676 a(I6870000 g1 (g5 S'ffff&\xe6\xc0@' p3677 tp3678 Rp3679 g1 (g5 S'ffff&\xe6\xc0@' p3680 tp3681 Rp3682 F8346.0 tp3683 a(I6880000 g1 (g5 S'q=\n\xd7#\x05\xc1@' p3684 tp3685 Rp3686 g1 (g5 S'q=\n\xd7#\x05\xc1@' p3687 tp3688 Rp3689 F14430.0 tp3690 a(I6890000 g1 (g5 S'\x1f\x85\xebQ8\xea\xc0@' p3691 tp3692 Rp3693 g3689 F4230.0 tp3694 a(I6900000 g1 (g5 S'\xa4p=\n\x97\xd5\xc0@' p3695 tp3696 Rp3697 g3689 F5280.0 tp3698 a(I6910000 g1 (g5 S'ffff\xe6\xe1\xc0@' p3699 tp3700 Rp3701 g3689 F7038.0 tp3702 a(I6920000 g1 (g5 S'ffff&\x12\xc1@' p3703 tp3704 Rp3705 g1 (g5 S'ffff&\x12\xc1@' p3706 tp3707 Rp3708 F9800.0 tp3709 a(I6930000 g1 (g5 S'\x14\xaeG\xe1\xfa\xdf\xc0@' p3710 tp3711 Rp3712 g3708 F9268.0 tp3713 a(I6940000 g1 (g5 S'\n\xd7\xa3p\xfd\xb8\xc0@' p3714 tp3715 Rp3716 g3708 F4860.0 tp3717 a(I6950000 g1 (g5 S'\xc3\xf5(\\\x0f\x89\xc0@' p3718 tp3719 Rp3720 g3708 F2108.0 tp3721 a(I6960000 g1 (g5 S'\xa4p=\n\x17|\xc0@' p3722 tp3723 Rp3724 g3708 F8740.0 tp3725 a(I6970000 g1 (g5 S'\x8f\xc2\xf5(\xdc\xae\xc0@' p3726 tp3727 Rp3728 g3708 F7530.0 tp3729 a(I6980000 g1 (g5 S'\xc3\xf5(\\O\xae\xc0@' p3730 tp3731 Rp3732 g3708 F8950.0 tp3733 a(I6990000 g1 (g5 S'\x85\xebQ\xb8\xde\xb4\xc0@' p3734 tp3735 Rp3736 g3708 F5760.0 tp3737 a(I7000000 g1 (g5 S'\xb8\x1e\x85\xebQ{\xc0@' p3738 tp3739 Rp3740 g3708 F5866.0 tp3741 a(I7010000 g1 (g5 S'\xecQ\xb8\x1eE\x90\xc0@' p3742 tp3743 Rp3744 g3708 F12450.0 tp3745 a(I7020000 g1 (g5 S'\x1f\x85\xebQ\xb8^\xc0@' p3746 tp3747 Rp3748 g3708 F7200.0 tp3749 a(I7030000 g1 (g5 S'q=\n\xd7#\x8b\xc0@' p3750 tp3751 Rp3752 g3708 F8580.0 tp3753 a(I7040000 g1 (g5 S'\x1f\x85\xebQ8V\xc0@' p3754 tp3755 Rp3756 g3708 F6384.0 tp3757 a(I7050000 g1 (g5 S'=\n\xd7\xa3pw\xc0@' p3758 tp3759 Rp3760 g3708 F13352.0 tp3761 a(I7060000 g1 (g5 S'=\n\xd7\xa30e\xc0@' p3762 tp3763 Rp3764 g3708 F7530.0 tp3765 a(I7070000 g1 (g5 S'R\xb8\x1e\x85\xabQ\xc0@' p3766 tp3767 Rp3768 g3708 F5216.0 tp3769 a(I7080000 g1 (g5 S'R\xb8\x1e\x85+V\xc0@' p3770 tp3771 Rp3772 g3708 F6516.0 tp3773 a(I7090000 g1 (g5 S'\n\xd7\xa3p\xfd\x7f\xc0@' p3774 tp3775 Rp3776 g3708 F21676.0 tp3777 a(I7100000 g1 (g5 S'3333sm\xc0@' p3778 tp3779 Rp3780 g3708 F6096.0 tp3781 a(I7110000 g1 (g5 S'ffff\xe6\x95\xc0@' p3782 tp3783 Rp3784 g3708 F5280.0 tp3785 a(I7120000 g1 (g5 S'\\\x8f\xc2\xf5\xa8~\xc0@' p3786 tp3787 Rp3788 g3708 F7428.0 tp3789 a(I7130000 g1 (g5 S'\xd7\xa3p=\n\x90\xc0@' p3790 tp3791 Rp3792 g3708 F18920.0 tp3793 a(I7140000 g1 (g5 S'R\xb8\x1e\x85+y\xc0@' p3794 tp3795 Rp3796 g3708 F8268.0 tp3797 a(I7150000 g1 (g5 S'\xecQ\xb8\x1e\x05\xb0\xc0@' p3798 tp3799 Rp3800 g3708 F24716.0 tp3801 a(I7160000 g1 (g5 S'R\xb8\x1e\x85\xeb\xd4\xc0@' p3802 tp3803 Rp3804 g3708 F12450.0 tp3805 a(I7170000 g1 (g5 S'\n\xd7\xa3p=\x1a\xc1@' p3806 tp3807 Rp3808 g1 (g5 S'\n\xd7\xa3p=\x1a\xc1@' p3809 tp3810 Rp3811 F18756.0 tp3812 a(I7180000 g1 (g5 S'R\xb8\x1e\x85k\x18\xc1@' p3813 tp3814 Rp3815 g3811 F7230.0 tp3816 a(I7190000 g1 (g5 S'=\n\xd7\xa3\xf0\x11\xc1@' p3817 tp3818 Rp3819 g3811 F9810.0 tp3820 a(I7200000 g1 (g5 S'H\xe1z\x14\xeeS\xc1@' p3821 tp3822 Rp3823 g1 (g5 S'H\xe1z\x14\xeeS\xc1@' p3824 tp3825 Rp3826 F20850.0 tp3827 a(I7210000 g1 (g5 S'\xd7\xa3p=\n\\\xc1@' p3828 tp3829 Rp3830 g1 (g5 S'\xd7\xa3p=\n\\\xc1@' p3831 tp3832 Rp3833 F6804.0 tp3834 a(I7220000 g1 (g5 S'\x85\xebQ\xb8\x1eL\xc1@' p3835 tp3836 Rp3837 g3833 F6456.0 tp3838 a(I7230000 g1 (g5 S'q=\n\xd7c\x9d\xc1@' p3839 tp3840 Rp3841 g1 (g5 S'q=\n\xd7c\x9d\xc1@' p3842 tp3843 Rp3844 F23310.0 tp3845 a(I7240000 g1 (g5 S'\x8f\xc2\xf5(\xdc\xc8\xc1@' p3846 tp3847 Rp3848 g1 (g5 S'\x8f\xc2\xf5(\xdc\xc8\xc1@' p3849 tp3850 Rp3851 F14790.0 tp3852 a(I7250000 g1 (g5 S'=\n\xd7\xa3\xf0\x02\xc2@' p3853 tp3854 Rp3855 g1 (g5 S'=\n\xd7\xa3\xf0\x02\xc2@' p3856 tp3857 Rp3858 F15936.0 tp3859 a(I7260000 g1 (g5 S'\n\xd7\xa3p=\x18\xc2@' p3860 tp3861 Rp3862 g1 (g5 S'\n\xd7\xa3p=\x18\xc2@' p3863 tp3864 Rp3865 F12180.0 tp3866 a(I7270000 g1 (g5 S'\xc3\xf5(\\\xcfJ\xc2@' p3867 tp3868 Rp3869 g1 (g5 S'\xc3\xf5(\\\xcfJ\xc2@' p3870 tp3871 Rp3872 F29044.0 tp3873 a(I7280000 g1 (g5 S'H\xe1z\x14n\x81\xc2@' p3874 tp3875 Rp3876 g1 (g5 S'H\xe1z\x14n\x81\xc2@' p3877 tp3878 Rp3879 F16560.0 tp3880 a(I7290000 g1 (g5 S'\xecQ\xb8\x1e\x85\x9c\xc2@' p3881 tp3882 Rp3883 g1 (g5 S'\xecQ\xb8\x1e\x85\x9c\xc2@' p3884 tp3885 Rp3886 F11820.0 tp3887 a(I7300000 g1 (g5 S'\xa4p=\nW\x96\xc2@' p3888 tp3889 Rp3890 g3886 F6456.0 tp3891 a(I7310000 g1 (g5 S'\xd7\xa3p=\n\xb5\xc2@' p3892 tp3893 Rp3894 g1 (g5 S'\xd7\xa3p=\n\xb5\xc2@' p3895 tp3896 Rp3897 F12132.0 tp3898 a(I7320000 g1 (g5 S'\xf6(\\\x8f\xc2\xb3\xc2@' p3899 tp3900 Rp3901 g3897 F4540.0 tp3902 a(I7330000 g1 (g5 S'\xaeG\xe1z\x14\xc1\xc2@' p3903 tp3904 Rp3905 g1 (g5 S'\xd7\xa3p=J\xcd\xc2@' p3906 tp3907 Rp3908 F7758.0 tp3909 a(I7340000 g1 (g5 S'\x8f\xc2\xf5(\x1c\xb1\xc2@' p3910 tp3911 Rp3912 g3908 F3900.0 tp3913 a(I7350000 g1 (g5 S'\x1f\x85\xebQ\xb8\xc9\xc2@' p3914 tp3915 Rp3916 g1 (g5 S'\xc3\xf5(\\\xcf\xe4\xc2@' p3917 tp3918 Rp3919 F6528.0 tp3920 a(I7360000 g1 (g5 S'ffff\xe6\xea\xc2@' p3921 tp3922 Rp3923 g1 (g5 S'ffff\xe6\xea\xc2@' p3924 tp3925 Rp3926 F13530.0 tp3927 a(I7370000 g1 (g5 S'\\\x8f\xc2\xf5\xe8\x13\xc3@' p3928 tp3929 Rp3930 g1 (g5 S'\\\x8f\xc2\xf5\xe8\x13\xc3@' p3931 tp3932 Rp3933 F13642.0 tp3934 a(I7380000 g1 (g5 S'\x14\xaeG\xe1\xfa$\xc3@' p3935 tp3936 Rp3937 g1 (g5 S'\x14\xaeG\xe1\xfa$\xc3@' p3938 tp3939 Rp3940 F7320.0 tp3941 a(I7390000 g1 (g5 S')\\\x8f\xc25\x13\xc3@' p3942 tp3943 Rp3944 g3940 F9750.0 tp3945 a(I7400000 g1 (g5 S'H\xe1z\x14.\x06\xc3@' p3946 tp3947 Rp3948 g3940 F8362.0 tp3949 a(I7410000 g1 (g5 S'=\n\xd7\xa30\x18\xc3@' p3950 tp3951 Rp3952 g3940 F8740.0 tp3953 a(I7420000 g1 (g5 S'fffff\x03\xc3@' p3954 tp3955 Rp3956 g3940 F6432.0 tp3957 a(I7430000 g1 (g5 S'q=\n\xd7c\x0f\xc3@' p3958 tp3959 Rp3960 g3940 F4606.0 tp3961 a(I7440000 g1 (g5 S'\n\xd7\xa3p\xfd\xf6\xc2@' p3962 tp3963 Rp3964 g3940 F5086.0 tp3965 a(I7450000 g1 (g5 S'q=\n\xd7\xe3\xf4\xc2@' p3966 tp3967 Rp3968 g3940 F7890.0 tp3969 a(I7460000 g1 (g5 S'\xcd\xcc\xcc\xcc\x8c\xec\xc2@' p3970 tp3971 Rp3972 g3940 F8600.0 tp3973 a(I7470000 g1 (g5 S'=\n\xd7\xa3p\xfd\xc2@' p3974 tp3975 Rp3976 g3940 F6648.0 tp3977 a(I7480000 g1 (g5 S'\xf6(\\\x8f\xc2\xfa\xc2@' p3978 tp3979 Rp3980 g3940 F5472.0 tp3981 a(I7490000 g1 (g5 S'{\x14\xaeGa\x12\xc3@' p3982 tp3983 Rp3984 g3940 F11436.0 tp3985 a(I7500000 g1 (g5 S'\xf6(\\\x8f\x82)\xc3@' p3986 tp3987 Rp3988 g1 (g5 S'\xf6(\\\x8f\x82)\xc3@' p3989 tp3990 Rp3991 F9422.0 tp3992 a(I7510000 g1 (g5 S'\x1f\x85\xebQ\xb8\x16\xc3@' p3993 tp3994 Rp3995 g1 (g5 S'\xa4p=\nW5\xc3@' p3996 tp3997 Rp3998 F7092.0 tp3999 a(I7520000 g1 (g5 S'\xf6(\\\x8f\xc2\xf9\xc2@' p4000 tp4001 Rp4002 g3998 F7060.0 tp4003 a(I7530000 g1 (g5 S'\xa4p=\n\x17\xe9\xc2@' p4004 tp4005 Rp4006 g3998 F7026.0 tp4007 a(I7540000 g1 (g5 S'\xa4p=\n\xd7\xf6\xc2@' p4008 tp4009 Rp4010 g3998 F4860.0 tp4011 a(I7550000 g1 (g5 S'\xf6(\\\x8f\x02\xc9\xc2@' p4012 tp4013 Rp4014 g3998 F6254.0 tp4015 a(I7560000 g1 (g5 S'\x1f\x85\xebQ\xf8\xdd\xc2@' p4016 tp4017 Rp4018 g3998 F12538.0 tp4019 a(I7570000 g1 (g5 S'\xaeG\xe1z\x94\xe3\xc2@' p4020 tp4021 Rp4022 g3998 F15552.0 tp4023 a(I7580000 g1 (g5 S')\\\x8f\xc2\xb5>\xc3@' p4024 tp4025 Rp4026 g1 (g5 S')\\\x8f\xc2\xb5>\xc3@' p4027 tp4028 Rp4029 F22546.0 tp4030 a(I7590000 g1 (g5 S'=\n\xd7\xa3\xb0N\xc3@' p4031 tp4032 Rp4033 g1 (g5 S'\xf6(\\\x8f\x82Z\xc3@' p4034 tp4035 Rp4036 F10590.0 tp4037 a(I7600000 g1 (g5 S'q=\n\xd7\xa3d\xc3@' p4038 tp4039 Rp4040 g1 (g5 S'q=\n\xd7\xa3d\xc3@' p4041 tp4042 Rp4043 F9670.0 tp4044 a(I7610000 g1 (g5 S'\n\xd7\xa3p\xbd\x88\xc3@' p4045 tp4046 Rp4047 g1 (g5 S'\n\xd7\xa3p\xbd\x88\xc3@' p4048 tp4049 Rp4050 F14258.0 tp4051 a(I7620000 g1 (g5 S'{\x14\xaeG\xe1b\xc3@' p4052 tp4053 Rp4054 g4050 F5632.0 tp4055 a(I7630000 g1 (g5 S'\xcd\xcc\xcc\xcc\x0c\x90\xc3@' p4056 tp4057 Rp4058 g1 (g5 S'\xcd\xcc\xcc\xcc\x0c\x90\xc3@' p4059 tp4060 Rp4061 F13126.0 tp4062 a(I7640000 g1 (g5 S'\\\x8f\xc2\xf5\xa8\xb1\xc3@' p4063 tp4064 Rp4065 g1 (g5 S'\\\x8f\xc2\xf5\xa8\xb1\xc3@' p4066 tp4067 Rp4068 F15990.0 tp4069 a(I7650000 g1 (g5 S'{\x14\xaeG\xa1\xb8\xc3@' p4070 tp4071 Rp4072 g1 (g5 S'{\x14\xaeG\xa1\xb8\xc3@' p4073 tp4074 Rp4075 F6254.0 tp4076 a(I7660000 g1 (g5 S'3333\xb3\xe0\xc3@' p4077 tp4078 Rp4079 g1 (g5 S'3333\xb3\xe0\xc3@' p4080 tp4081 Rp4082 F17184.0 tp4083 a(I7670000 g1 (g5 S'\x85\xebQ\xb8\x9e\xf1\xc3@' p4084 tp4085 Rp4086 g1 (g5 S'R\xb8\x1e\x85\xeb\xff\xc3@' p4087 tp4088 Rp4089 F5880.0 tp4090 a(I7680000 g1 (g5 S'\x85\xebQ\xb8^\xd7\xc3@' p4091 tp4092 Rp4093 g4089 F9030.0 tp4094 a(I7690000 g1 (g5 S'\x8f\xc2\xf5(\x1c\xf8\xc3@' p4095 tp4096 Rp4097 g1 (g5 S'\xcd\xcc\xcc\xcc\x8c\x05\xc4@' p4098 tp4099 Rp4100 F6262.0 tp4101 a(I7700000 g1 (g5 S'q=\n\xd7#2\xc4@' p4102 tp4103 Rp4104 g1 (g5 S'q=\n\xd7#2\xc4@' p4105 tp4106 Rp4107 F21336.0 tp4108 a(I7710000 g1 (g5 S'q=\n\xd7#2\xc4@' p4109 tp4110 Rp4111 g4107 F21336.0 tp4112 a(I7720000 g1 (g5 S'\x9a\x99\x99\x99\xd9\x87\xc4@' p4113 tp4114 Rp4115 g1 (g5 S'\x9a\x99\x99\x99\xd9\x87\xc4@' p4116 tp4117 Rp4118 F4992.0 tp4119 a(I7730000 g1 (g5 S'\x9a\x99\x99\x99\xd9\x87\xc4@' p4120 tp4121 Rp4122 g4118 F4992.0 tp4123 a(I7740000 g1 (g5 S'\xaeG\xe1z\x14\xb9\xc4@' p4124 tp4125 Rp4126 g1 (g5 S'\xe1z\x14\xaeG\xdc\xc4@' p4127 tp4128 Rp4129 F5410.0 tp4130 a(I7750000 g1 (g5 S'\xd7\xa3p=J\x9b\xc4@' p4131 tp4132 Rp4133 g4129 F6996.0 tp4134 a(I7760000 g1 (g5 S'=\n\xd7\xa3p\x98\xc4@' p4135 tp4136 Rp4137 g4129 F8010.0 tp4138 a(I7770000 g1 (g5 S'\x9a\x99\x99\x99\x99\xc4\xc4@' p4139 tp4140 Rp4141 g4129 F15216.0 tp4142 a(I7780000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xd3\xc4@' p4143 tp4144 Rp4145 g1 (g5 S'\xc3\xf5(\\O\xea\xc4@' p4146 tp4147 Rp4148 F8850.0 tp4149 a(I7790000 g1 (g5 S'\x85\xebQ\xb8\xde\xd3\xc4@' p4150 tp4151 Rp4152 g4148 F7544.0 tp4153 a(I7800000 g1 (g5 S'\x1f\x85\xebQx\xe8\xc4@' p4154 tp4155 Rp4156 g4148 F8734.0 tp4157 a(I7810000 g1 (g5 S'H\xe1z\x14\xee\xe2\xc4@' p4158 tp4159 Rp4160 g4148 F5676.0 tp4161 a(I7820000 g1 (g5 S'\xe1z\x14\xae\x87\x96\xc4@' p4162 tp4163 Rp4164 g4148 F6384.0 tp4165 a(I7830000 g1 (g5 S'\xe1z\x14\xaeG\x87\xc4@' p4166 tp4167 Rp4168 g4148 F4384.0 tp4169 a(I7840000 g1 (g5 S'\x85\xebQ\xb8\x9e\xae\xc4@' p4170 tp4171 Rp4172 g4148 F5598.0 tp4173 a(I7850000 g1 (g5 S'\n\xd7\xa3p=_\xc4@' p4174 tp4175 Rp4176 g4148 F4796.0 tp4177 a(I7860000 g1 (g5 S'=\n\xd7\xa3\xf0Q\xc4@' p4178 tp4179 Rp4180 g4148 F5608.0 tp4181 a(I7870000 g1 (g5 S'\x85\xebQ\xb8\x9e\xf2\xc3@' p4182 tp4183 Rp4184 g4148 F6300.0 tp4185 a(I7880000 g1 (g5 S'\xc3\xf5(\\\xcf\xab\xc3@' p4186 tp4187 Rp4188 g4148 F6470.0 tp4189 a(I7890000 g1 (g5 S'\x8f\xc2\xf5(\x1c\xbb\xc3@' p4190 tp4191 Rp4192 g4148 F10290.0 tp4193 a(I7900000 g1 (g5 S'H\xe1z\x14.p\xc3@' p4194 tp4195 Rp4196 g4148 F6690.0 tp4197 a(I7910000 g1 (g5 S'=\n\xd7\xa3pV\xc3@' p4198 tp4199 Rp4200 g4148 F1284.0 tp4201 a(I7920000 g1 (g5 S'\xa4p=\n\x17\x19\xc3@' p4202 tp4203 Rp4204 g4148 F11040.0 tp4205 a(I7930000 g1 (g5 S'\xcd\xcc\xcc\xcc\x8c\xd7\xc2@' p4206 tp4207 Rp4208 g4148 F8298.0 tp4209 a(I7940000 g1 (g5 S'\xecQ\xb8\x1e\xc5\xd7\xc2@' p4210 tp4211 Rp4212 g4148 F12224.0 tp4213 a(I7950000 g1 (g5 S'\x9a\x99\x99\x99Y5\xc2@' p4214 tp4215 Rp4216 g4148 F4092.0 tp4217 a(I7960000 g1 (g5 S'\xecQ\xb8\x1e\xc5\x0f\xc2@' p4218 tp4219 Rp4220 g4148 F4832.0 tp4221 a(I7970000 g1 (g5 S'fffff\x06\xc2@' p4222 tp4223 Rp4224 g4148 F9310.0 tp4225 a(I7980000 g1 (g5 S'\xd7\xa3p=\x8a\xf8\xc1@' p4226 tp4227 Rp4228 g4148 F7264.0 tp4229 a(I7990000 g1 (g5 S'fffff\xd0\xc1@' p4230 tp4231 Rp4232 g4148 F5964.0 tp4233 a(I8000000 g1 (g5 S'\xc3\xf5(\\\x0f\xfe\xc1@' p4234 tp4235 Rp4236 g4148 F8012.0 tp4237 a(I8010000 g1 (g5 S'\xe1z\x14\xae\xc7\xe6\xc1@' p4238 tp4239 Rp4240 g4148 F14694.0 tp4241 a(I8020000 g1 (g5 S'\x85\xebQ\xb8^\n\xc2@' p4242 tp4243 Rp4244 g4148 F13646.0 tp4245 a(I8030000 g1 (g5 S'\xa4p=\n\x97\xc8\xc1@' p4246 tp4247 Rp4248 g4148 F6864.0 tp4249 a(I8040000 g1 (g5 S'q=\n\xd7c\xd8\xc1@' p4250 tp4251 Rp4252 g4148 F9014.0 tp4253 a(I8050000 g1 (g5 S'\n\xd7\xa3p}\n\xc2@' p4254 tp4255 Rp4256 g4148 F17340.0 tp4257 a(I8060000 g1 (g5 S'\n\xd7\xa3p}\x0b\xc2@' p4258 tp4259 Rp4260 g4148 F5880.0 tp4261 a(I8070000 g1 (g5 S'\x1f\x85\xebQx\xf9\xc1@' p4262 tp4263 Rp4264 g4148 F5924.0 tp4265 a(I8080000 g1 (g5 S'3333s\x1b\xc2@' p4266 tp4267 Rp4268 g4148 F15536.0 tp4269 a(I8090000 g1 (g5 S'\n\xd7\xa3p}G\xc2@' p4270 tp4271 Rp4272 g4148 F15240.0 tp4273 a(I8100000 g1 (g5 S'R\xb8\x1e\x85kY\xc2@' p4274 tp4275 Rp4276 g4148 F16788.0 tp4277 a(I8110000 g1 (g5 S'\x14\xaeG\xe1z\x8b\xc2@' p4278 tp4279 Rp4280 g4148 F14618.0 tp4281 a(I8120000 g1 (g5 S'\x8f\xc2\xf5(\x1c\x87\xc2@' p4282 tp4283 Rp4284 g4148 F8764.0 tp4285 a(I8130000 g1 (g5 S'\xc3\xf5(\\\x0f\x8a\xc2@' p4286 tp4287 Rp4288 g4148 F7446.0 tp4289 a(I8140000 g1 (g5 S'\xc3\xf5(\\\x0f\x8a\xc2@' p4290 tp4291 Rp4292 g4148 F7446.0 tp4293 a(I8150000 g1 (g5 S'\\\x8f\xc2\xf5h\xa2\xc2@' p4294 tp4295 Rp4296 g4148 F6762.0 tp4297 a(I8160000 g1 (g5 S'\xc3\xf5(\\\xcf\xa2\xc2@' p4298 tp4299 Rp4300 g4148 F8962.0 tp4301 a(I8170000 g1 (g5 S'\xd7\xa3p=J\x7f\xc2@' p4302 tp4303 Rp4304 g4148 F6804.0 tp4305 a(I8180000 g1 (g5 S'=\n\xd7\xa30x\xc2@' p4306 tp4307 Rp4308 g4148 F8172.0 tp4309 a(I8190000 g1 (g5 S'\x1f\x85\xebQ\xf8t\xc2@' p4310 tp4311 Rp4312 g4148 F7576.0 tp4313 a(I8200000 g1 (g5 S'\x1f\x85\xebQ\xf8\x80\xc2@' p4314 tp4315 Rp4316 g4148 F9460.0 tp4317 a(I8210000 g1 (g5 S'\xf6(\\\x8f\x82b\xc2@' p4318 tp4319 Rp4320 g4148 F5730.0 tp4321 a(I8220000 g1 (g5 S'\x8f\xc2\xf5(\\\\\xc2@' p4322 tp4323 Rp4324 g4148 F4928.0 tp4325 a(I8230000 g1 (g5 S'\xb8\x1e\x85\xeb\xd1A\xc2@' p4326 tp4327 Rp4328 g4148 F7230.0 tp4329 a(I8240000 g1 (g5 S'H\xe1z\x14\xaeL\xc2@' p4330 tp4331 Rp4332 g4148 F17724.0 tp4333 a(I8250000 g1 (g5 S'\xd7\xa3p=\x8a\x10\xc2@' p4334 tp4335 Rp4336 g4148 F8584.0 tp4337 a(I8260000 g1 (g5 S'\x9a\x99\x99\x99\x99\xde\xc1@' p4338 tp4339 Rp4340 g4148 F4320.0 tp4341 a(I8270000 g1 (g5 S'\xf6(\\\x8f\xc2\xe2\xc1@' p4342 tp4343 Rp4344 g4148 F15090.0 tp4345 a(I8280000 g1 (g5 S'H\xe1z\x14\xae\xf5\xc1@' p4346 tp4347 Rp4348 g4148 F6864.0 tp4349 a(I8290000 g1 (g5 S'q=\n\xd7c\xa3\xc1@' p4350 tp4351 Rp4352 g4148 F7380.0 tp4353 a(I8300000 g1 (g5 S'\xb8\x1e\x85\xeb\x91\xc9\xc1@' p4354 tp4355 Rp4356 g4148 F13890.0 tp4357 a(I8310000 g1 (g5 S'\x1f\x85\xebQ8\xa4\xc1@' p4358 tp4359 Rp4360 g4148 F4604.0 tp4361 a(I8320000 g1 (g5 S'\xf6(\\\x8f\x02\xb3\xc1@' p4362 tp4363 Rp4364 g4148 F8838.0 tp4365 a(I8330000 g1 (g5 S'\x8f\xc2\xf5(\xdc\xcd\xc1@' p4366 tp4367 Rp4368 g4148 F14400.0 tp4369 a(I8340000 g1 (g5 S'{\x14\xaeG\xa1\xac\xc1@' p4370 tp4371 Rp4372 g4148 F7762.0 tp4373 a(I8350000 g1 (g5 S'q=\n\xd7\xa3"\xc1@' p4374 tp4375 Rp4376 g4148 F7622.0 tp4377 a(I8360000 g1 (g5 S'{\x14\xaeG\xa1 \xc1@' p4378 tp4379 Rp4380 g4148 F6810.0 tp4381 a(I8370000 g1 (g5 S'H\xe1z\x14\xae\xdf\xc0@' p4382 tp4383 Rp4384 g4148 F6414.0 tp4385 a(I8380000 g1 (g5 S'H\xe1z\x14n\xfc\xc0@' p4386 tp4387 Rp4388 g4148 F9044.0 tp4389 a(I8390000 g1 (g5 S'H\xe1z\x14\xee\xf5\xc0@' p4390 tp4391 Rp4392 g4148 F5696.0 tp4393 a(I8400000 g1 (g5 S'{\x14\xaeG!\x1d\xc1@' p4394 tp4395 Rp4396 g4148 F15850.0 tp4397 a(I8410000 g1 (g5 S'\x9a\x99\x99\x99\xd9\xf3\xc0@' p4398 tp4399 Rp4400 g4148 F6960.0 tp4401 a(I8420000 g1 (g5 S')\\\x8f\xc2u\x03\xc1@' p4402 tp4403 Rp4404 g4148 F7386.0 tp4405 a(I8430000 g1 (g5 S'H\xe1z\x14n\xfe\xc0@' p4406 tp4407 Rp4408 g4148 F6538.0 tp4409 a(I8440000 g1 (g5 S'{\x14\xaeG\xa1\xf7\xc0@' p4410 tp4411 Rp4412 g4148 F6390.0 tp4413 a(I8450000 g1 (g5 S'\x9a\x99\x99\x99\x19@\xc1@' p4414 tp4415 Rp4416 g4148 F20170.0 tp4417 a(I8460000 g1 (g5 S'\xe1z\x14\xae\xc7\x82\xc1@' p4418 tp4419 Rp4420 g4148 F19864.0 tp4421 a(I8470000 g1 (g5 S')\\\x8f\xc25j\xc1@' p4422 tp4423 Rp4424 g4148 F4860.0 tp4425 a(I8480000 g1 (g5 S'\xa4p=\n\x17\x9c\xc1@' p4426 tp4427 Rp4428 g4148 F14360.0 tp4429 a(I8490000 g1 (g5 S'3333\xf3\x87\xc1@' p4430 tp4431 Rp4432 g4148 F7486.0 tp4433 a(I8500000 g1 (g5 S'\xb8\x1e\x85\xeb\xd1\xa7\xc1@' p4434 tp4435 Rp4436 g4148 F11972.0 tp4437 a(I8510000 g1 (g5 S'\xcd\xcc\xcc\xcc\x8c\xe5\xc1@' p4438 tp4439 Rp4440 g4148 F9098.0 tp4441 a(I8520000 g1 (g5 S'\x8f\xc2\xf5(\\\xfc\xc1@' p4442 tp4443 Rp4444 g4148 F10170.0 tp4445 a(I8530000 g1 (g5 S'3333\xb3\x08\xc2@' p4446 tp4447 Rp4448 g4148 F10680.0 tp4449 a(I8540000 g1 (g5 S'\x9a\x99\x99\x99\x19.\xc2@' p4450 tp4451 Rp4452 g4148 F14386.0 tp4453 a(I8550000 g1 (g5 S'\x9a\x99\x99\x99\x19H\xc2@' p4454 tp4455 Rp4456 g4148 F11670.0 tp4457 a(I8560000 g1 (g5 S'3333\xf3Y\xc2@' p4458 tp4459 Rp4460 g4148 F13860.0 tp4461 a(I8570000 g1 (g5 S'\xa4p=\nWl\xc2@' p4462 tp4463 Rp4464 g4148 F11112.0 tp4465 a(I8580000 g1 (g5 S'=\n\xd7\xa30a\xc2@' p4466 tp4467 Rp4468 g4148 F5544.0 tp4469 a(I8590000 g1 (g5 S'\x14\xaeG\xe1\xfa\x83\xc2@' p4470 tp4471 Rp4472 g4148 F6402.0 tp4473 a(I8600000 g1 (g5 S'\n\xd7\xa3p=u\xc2@' p4474 tp4475 Rp4476 g4148 F6652.0 tp4477 a(I8610000 g1 (g5 S'\x14\xaeG\xe1:j\xc2@' p4478 tp4479 Rp4480 g4148 F6096.0 tp4481 a(I8620000 g1 (g5 S'\xe1z\x14\xae\x87J\xc2@' p4482 tp4483 Rp4484 g4148 F7290.0 tp4485 a(I8630000 g1 (g5 S'\xaeG\xe1z\x14\x96\xc2@' p4486 tp4487 Rp4488 g4148 F19202.0 tp4489 a(I8640000 g1 (g5 S'{\x14\xaeG\xe1\xbb\xc2@' p4490 tp4491 Rp4492 g4148 F4898.0 tp4493 a(I8650000 g1 (g5 S'\x1f\x85\xebQ\xf8\xa5\xc2@' p4494 tp4495 Rp4496 g4148 F4928.0 tp4497 a(I8660000 g1 (g5 S'\\\x8f\xc2\xf5\xe8\xbb\xc2@' p4498 tp4499 Rp4500 g4148 F7038.0 tp4501 a(I8670000 g1 (g5 S'\xc3\xf5(\\O\x9f\xc2@' p4502 tp4503 Rp4504 g4148 F5666.0 tp4505 a(I8680000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xfd\xc2@' p4506 tp4507 Rp4508 g4148 F26910.0 tp4509 a(I8690000 g1 (g5 S'\x9a\x99\x99\x99Y\xcb\xc2@' p4510 tp4511 Rp4512 g4148 F4604.0 tp4513 a(I8700000 g1 (g5 S'{\x14\xaeG!\xe0\xc2@' p4514 tp4515 Rp4516 g4148 F7416.0 tp4517 a(I8710000 g1 (g5 S'\xc3\xf5(\\\xcf\xf8\xc2@' p4518 tp4519 Rp4520 g4148 F11800.0 tp4521 a(I8720000 g1 (g5 S'\xecQ\xb8\x1e\x85\x15\xc3@' p4522 tp4523 Rp4524 g4148 F14756.0 tp4525 a(I8730000 g1 (g5 S'\x14\xaeG\xe1:\xe9\xc2@' p4526 tp4527 Rp4528 g4148 F8482.0 tp4529 a(I8740000 g1 (g5 S'\x1f\x85\xebQ8\x05\xc3@' p4530 tp4531 Rp4532 g4148 F6156.0 tp4533 a(I8750000 g1 (g5 S'\xd7\xa3p=\xca\t\xc3@' p4534 tp4535 Rp4536 g4148 F8420.0 tp4537 a(I8760000 g1 (g5 S'\x85\xebQ\xb8\xde!\xc3@' p4538 tp4539 Rp4540 g4148 F10740.0 tp4541 a(I8770000 g1 (g5 S'\xa4p=\nW\x19\xc3@' p4542 tp4543 Rp4544 g4148 F10490.0 tp4545 a(I8780000 g1 (g5 S'ffff\xe6\xe4\xc2@' p4546 tp4547 Rp4548 g4148 F6300.0 tp4549 a(I8790000 g1 (g5 S'\x85\xebQ\xb8^\xcf\xc2@' p4550 tp4551 Rp4552 g4148 F10312.0 tp4553 a(I8800000 g1 (g5 S'\xc3\xf5(\\\x8f\xee\xc2@' p4554 tp4555 Rp4556 g4148 F9660.0 tp4557 a(I8810000 g1 (g5 S'\xd7\xa3p=\n\xf7\xc2@' p4558 tp4559 Rp4560 g4148 F10566.0 tp4561 a(I8820000 g1 (g5 S'\n\xd7\xa3p}\xa7\xc2@' p4562 tp4563 Rp4564 g4148 F4320.0 tp4565 a(I8830000 g1 (g5 S'\x8f\xc2\xf5(\xdc\xa2\xc2@' p4566 tp4567 Rp4568 g4148 F6690.0 tp4569 a(I8840000 g1 (g5 S'q=\n\xd7\xa3\xca\xc2@' p4570 tp4571 Rp4572 g4148 F12420.0 tp4573 a(I8850000 g1 (g5 S'q=\n\xd7c\xc5\xc2@' p4574 tp4575 Rp4576 g4148 F8010.0 tp4577 a(I8860000 g1 (g5 S'\xc3\xf5(\\O\xab\xc2@' p4578 tp4579 Rp4580 g4148 F6274.0 tp4581 a(I8870000 g1 (g5 S'q=\n\xd7\xa3\xa0\xc2@' p4582 tp4583 Rp4584 g4148 F7326.0 tp4585 a(I8880000 g1 (g5 S'R\xb8\x1e\x85\xeb\xb6\xc2@' p4586 tp4587 Rp4588 g4148 F13064.0 tp4589 a(I8890000 g1 (g5 S'{\x14\xaeGa\xc5\xc2@' p4590 tp4591 Rp4592 g4148 F5348.0 tp4593 a(I8900000 g1 (g5 S'\\\x8f\xc2\xf5(\xc6\xc2@' p4594 tp4595 Rp4596 g4148 F7386.0 tp4597 a(I8910000 g1 (g5 S'{\x14\xaeG!\x9f\xc2@' p4598 tp4599 Rp4600 g4148 F15312.0 tp4601 a(I8920000 g1 (g5 S'\x1f\x85\xebQ\xf8\x8f\xc2@' p4602 tp4603 Rp4604 g4148 F5552.0 tp4605 a(I8930000 g1 (g5 S'\x8f\xc2\xf5(\xdc\xbe\xc2@' p4606 tp4607 Rp4608 g4148 F8690.0 tp4609 a(I8940000 g1 (g5 S'\\\x8f\xc2\xf5h\xab\xc2@' p4610 tp4611 Rp4612 g4148 F11200.0 tp4613 a(I8950000 g1 (g5 S'\xb8\x1e\x85\xeb\x11\xa8\xc2@' p4614 tp4615 Rp4616 g4148 F5212.0 tp4617 a(I8960000 g1 (g5 S')\\\x8f\xc2\xf5\xac\xc2@' p4618 tp4619 Rp4620 g4148 F5812.0 tp4621 a(I8970000 g1 (g5 S'\xc3\xf5(\\\xcf\xb5\xc2@' p4622 tp4623 Rp4624 g4148 F15660.0 tp4625 a(I8980000 g1 (g5 S'\x1f\x85\xebQ\xf8\xaf\xc2@' p4626 tp4627 Rp4628 g4148 F12294.0 tp4629 a(I8990000 g1 (g5 S'33333\xf7\xc2@' p4630 tp4631 Rp4632 g4148 F18850.0 tp4633 a(I9000000 g1 (g5 S'\xcd\xcc\xcc\xccL\xf5\xc2@' p4634 tp4635 Rp4636 g4148 F8458.0 tp4637 a(I9010000 g1 (g5 S'\x00\x00\x00\x00\x80\x04\xc3@' p4638 tp4639 Rp4640 g4148 F8750.0 tp4641 a(I9020000 g1 (g5 S'{\x14\xaeGa\xf1\xc2@' p4642 tp4643 Rp4644 g4148 F4284.0 tp4645 a(I9030000 g1 (g5 S')\\\x8f\xc2\xf5\xc3\xc2@' p4646 tp4647 Rp4648 g4148 F2552.0 tp4649 a(I9040000 g1 (g5 S'=\n\xd7\xa30\xf6\xc2@' p4650 tp4651 Rp4652 g4148 F16460.0 tp4653 a(I9050000 g1 (g5 S'=\n\xd7\xa30\xf6\xc2@' p4654 tp4655 Rp4656 g4148 F16460.0 tp4657 a(I9060000 g1 (g5 S'\x9a\x99\x99\x99\x19A\xc3@' p4658 tp4659 Rp4660 g4148 F14310.0 tp4661 a(I9070000 g1 (g5 S'\x85\xebQ\xb8^X\xc3@' p4662 tp4663 Rp4664 g4148 F10350.0 tp4665 a(I9080000 g1 (g5 S'\x1f\x85\xebQ8,\xc3@' p4666 tp4667 Rp4668 g4148 F8764.0 tp4669 a(I9090000 g1 (g5 S'\xb8\x1e\x85\xeb\x11\xfb\xc2@' p4670 tp4671 Rp4672 g4148 F9480.0 tp4673 a(I9100000 g1 (g5 S'{\x14\xaeG\xe1\x0e\xc3@' p4674 tp4675 Rp4676 g4148 F7956.0 tp4677 a(I9110000 g1 (g5 S'R\xb8\x1e\x85k\x06\xc3@' p4678 tp4679 Rp4680 g4148 F13094.0 tp4681 a(I9120000 g1 (g5 S"\x1f\x85\xebQx'\xc3@" p4682 tp4683 Rp4684 g4148 F13000.0 tp4685 a(I9130000 g1 (g5 S'\xecQ\xb8\x1eE\xab\xc2@' p4686 tp4687 Rp4688 g4148 F5858.0 tp4689 a(I9140000 g1 (g5 S'q=\n\xd7c\xa6\xc2@' p4690 tp4691 Rp4692 g4148 F5726.0 tp4693 a(I9150000 g1 (g5 S'\\\x8f\xc2\xf5h\xdb\xc2@' p4694 tp4695 Rp4696 g4148 F24964.0 tp4697 a(I9160000 g1 (g5 S'\x14\xaeG\xe1:\xaf\xc2@' p4698 tp4699 Rp4700 g4148 F6210.0 tp4701 a(I9170000 g1 (g5 S'\xd7\xa3p=J\x93\xc2@' p4702 tp4703 Rp4704 g4148 F8132.0 tp4705 a(I9180000 g1 (g5 S'3333s\x91\xc2@' p4706 tp4707 Rp4708 g4148 F4350.0 tp4709 a(I9190000 g1 (g5 S'R\xb8\x1e\x85k\xc0\xc2@' p4710 tp4711 Rp4712 g4148 F19284.0 tp4713 a(I9200000 g1 (g5 S'\x85\xebQ\xb8\x1eu\xc2@' p4714 tp4715 Rp4716 g4148 F8002.0 tp4717 a(I9210000 g1 (g5 S'\x9a\x99\x99\x99\x99?\xc2@' p4718 tp4719 Rp4720 g4148 F8386.0 tp4721 a(I9220000 g1 (g5 S'\x85\xebQ\xb8^B\xc2@' p4722 tp4723 Rp4724 g4148 F8794.0 tp4725 a(I9230000 g1 (g5 S'\xecQ\xb8\x1eEP\xc2@' p4726 tp4727 Rp4728 g4148 F13892.0 tp4729 a(I9240000 g1 (g5 S'q=\n\xd7#\x7f\xc2@' p4730 tp4731 Rp4732 g4148 F6678.0 tp4733 a(I9250000 g1 (g5 S'\x14\xaeG\xe1\xba\x86\xc2@' p4734 tp4735 Rp4736 g4148 F7920.0 tp4737 a(I9260000 g1 (g5 S'\xa4p=\nW\x93\xc2@' p4738 tp4739 Rp4740 g4148 F7500.0 tp4741 a(I9270000 g1 (g5 S'ffff&\x95\xc2@' p4742 tp4743 Rp4744 g4148 F6724.0 tp4745 a(I9280000 g1 (g5 S'\xaeG\xe1zT\xc8\xc2@' p4746 tp4747 Rp4748 g4148 F17526.0 tp4749 a(I9290000 g1 (g5 S'\xb8\x1e\x85\xeb\x11\x9e\xc2@' p4750 tp4751 Rp4752 g4148 F10750.0 tp4753 a(I9300000 g1 (g5 S'R\xb8\x1e\x85k\xa8\xc2@' p4754 tp4755 Rp4756 g4148 F10920.0 tp4757 a(I9310000 g1 (g5 S'=\n\xd7\xa3\xb0\xb6\xc2@' p4758 tp4759 Rp4760 g4148 F7782.0 tp4761 a(I9320000 g1 (g5 S'ffff\xa6\xdb\xc2@' p4762 tp4763 Rp4764 g4148 F18642.0 tp4765 a(I9330000 g1 (g5 S'3333\xf3\xd3\xc2@' p4766 tp4767 Rp4768 g4148 F8346.0 tp4769 a(I9340000 g1 (g5 S'\\\x8f\xc2\xf5\xa8w\xc2@' p4770 tp4771 Rp4772 g4148 F8452.0 tp4773 a(I9350000 g1 (g5 S'\\\x8f\xc2\xf5\xe8n\xc2@' p4774 tp4775 Rp4776 g4148 F8122.0 tp4777 a(I9360000 g1 (g5 S'\xd7\xa3p=\xcat\xc2@' p4778 tp4779 Rp4780 g4148 F8592.0 tp4781 a(I9370000 g1 (g5 S'{\x14\xaeGa\x8f\xc2@' p4782 tp4783 Rp4784 g4148 F17118.0 tp4785 a(I9380000 g1 (g5 S'\x8f\xc2\xf5(\x9ch\xc2@' p4786 tp4787 Rp4788 g4148 F7264.0 tp4789 a(I9390000 g1 (g5 S'\n\xd7\xa3p=C\xc2@' p4790 tp4791 Rp4792 g4148 F6810.0 tp4793 a(I9400000 g1 (g5 S'\n\xd7\xa3p=@\xc2@' p4794 tp4795 Rp4796 g4148 F5952.0 tp4797 a(I9410000 g1 (g5 S'H\xe1z\x14.5\xc2@' p4798 tp4799 Rp4800 g4148 F8528.0 tp4801 a(I9420000 g1 (g5 S'ffff\xe6\x0c\xc2@' p4802 tp4803 Rp4804 g4148 F5854.0 tp4805 a(I9430000 g1 (g5 S'3333\xb3\x0b\xc2@' p4806 tp4807 Rp4808 g4148 F7912.0 tp4809 a(I9440000 g1 (g5 S'\xd7\xa3p=\x8a\xe2\xc1@' p4810 tp4811 Rp4812 g4148 F5440.0 tp4813 a(I9450000 g1 (g5 S'\x00\x00\x00\x00\x00\x17\xc2@' p4814 tp4815 Rp4816 g4148 F7668.0 tp4817 a(I9460000 g1 (g5 S'\xecQ\xb8\x1e\xc5\x19\xc2@' p4818 tp4819 Rp4820 g4148 F11120.0 tp4821 a(I9470000 g1 (g5 S'\xecQ\xb8\x1eE(\xc2@' p4822 tp4823 Rp4824 g4148 F5666.0 tp4825 a(I9480000 g1 (g5 S'\x1f\x85\xebQ\xf85\xc2@' p4826 tp4827 Rp4828 g4148 F5500.0 tp4829 a(I9490000 g1 (g5 S'\xecQ\xb8\x1e\x85/\xc2@' p4830 tp4831 Rp4832 g4148 F11130.0 tp4833 a(I9500000 g1 (g5 S"\n\xd7\xa3p\xfd'\xc2@" p4834 tp4835 Rp4836 g4148 F6504.0 tp4837 a(I9510000 g1 (g5 S'\x8f\xc2\xf5(\x9cc\xc2@' p4838 tp4839 Rp4840 g4148 F7176.0 tp4841 a(I9520000 g1 (g5 S'=\n\xd7\xa3p\xa4\xc2@' p4842 tp4843 Rp4844 g4148 F19240.0 tp4845 a(I9530000 g1 (g5 S'\xf6(\\\x8f\x02\xb1\xc2@' p4846 tp4847 Rp4848 g4148 F9840.0 tp4849 a(I9540000 g1 (g5 S'\xcd\xcc\xcc\xccL\xcd\xc2@' p4850 tp4851 Rp4852 g4148 F12618.0 tp4853 a(I9550000 g1 (g5 S'\xaeG\xe1z\xd4\xe5\xc2@' p4854 tp4855 Rp4856 g4148 F17970.0 tp4857 a(I9560000 g1 (g5 S'{\x14\xaeGa\x12\xc3@' p4858 tp4859 Rp4860 g4148 F16338.0 tp4861 a(I9570000 g1 (g5 S'\xa4p=\nWl\xc3@' p4862 tp4863 Rp4864 g4148 F23340.0 tp4865 a(I9580000 g1 (g5 S'\xc3\xf5(\\O\x84\xc3@' p4866 tp4867 Rp4868 g4148 F12180.0 tp4869 a(I9590000 g1 (g5 S'\x85\xebQ\xb8\xdeG\xc3@' p4870 tp4871 Rp4872 g4148 F3964.0 tp4873 a(I9600000 g1 (g5 S'\xecQ\xb8\x1e\x85e\xc3@' p4874 tp4875 Rp4876 g4148 F16890.0 tp4877 a(I9610000 g1 (g5 S'\x1f\x85\xebQ8p\xc3@' p4878 tp4879 Rp4880 g4148 F10830.0 tp4881 a(I9620000 g1 (g5 S'\x85\xebQ\xb8\x9eT\xc3@' p4882 tp4883 Rp4884 g4148 F8734.0 tp4885 a(I9630000 g1 (g5 S'\xaeG\xe1z\x94_\xc3@' p4886 tp4887 Rp4888 g4148 F4928.0 tp4889 a(I9640000 g1 (g5 S'R\xb8\x1e\x85\xebt\xc3@' p4890 tp4891 Rp4892 g4148 F10080.0 tp4893 a(I9650000 g1 (g5 S'=\n\xd7\xa30N\xc3@' p4894 tp4895 Rp4896 g4148 F11118.0 tp4897 a(I9660000 g1 (g5 S"q=\n\xd7c'\xc3@" p4898 tp4899 Rp4900 g4148 F11090.0 tp4901 a(I9670000 g1 (g5 S'\xb8\x1e\x85\xeb\x91,\xc3@' p4902 tp4903 Rp4904 g4148 F9494.0 tp4905 a(I9680000 g1 (g5 S'\\\x8f\xc2\xf5\xe8,\xc3@' p4906 tp4907 Rp4908 g4148 F17378.0 tp4909 a(I9690000 g1 (g5 S'\x8f\xc2\xf5(\x9c=\xc3@' p4910 tp4911 Rp4912 g4148 F12090.0 tp4913 a(I9700000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x80\xc3@' p4914 tp4915 Rp4916 g4148 F10848.0 tp4917 a(I9710000 g1 (g5 S'fffff\x96\xc3@' p4918 tp4919 Rp4920 g4148 F9888.0 tp4921 a(I9720000 g1 (g5 S'\n\xd7\xa3p\xfd\xb6\xc3@' p4922 tp4923 Rp4924 g4148 F8610.0 tp4925 a(I9730000 g1 (g5 S'\xb8\x1e\x85\xebQ\x8c\xc3@' p4926 tp4927 Rp4928 g4148 F7926.0 tp4929 a(I9740000 g1 (g5 S'\xa4p=\n\x17s\xc3@' p4930 tp4931 Rp4932 g4148 F14100.0 tp4933 a(I9750000 g1 (g5 S'\xd7\xa3p=\n0\xc3@' p4934 tp4935 Rp4936 g4148 F1380.0 tp4937 a(I9760000 g1 (g5 S'\xf6(\\\x8fBT\xc3@' p4938 tp4939 Rp4940 g4148 F12460.0 tp4941 a(I9770000 g1 (g5 S'\x00\x00\x00\x00\xc0\x9f\xc3@' p4942 tp4943 Rp4944 g4148 F23862.0 tp4945 a(I9780000 g1 (g5 S'\xaeG\xe1z\xd4\xae\xc3@' p4946 tp4947 Rp4948 g4148 F10018.0 tp4949 a(I9790000 g1 (g5 S'\xf6(\\\x8f\x82\x88\xc3@' p4950 tp4951 Rp4952 g4148 F6322.0 tp4953 a(I9800000 g1 (g5 S')\\\x8f\xc2\xf5x\xc3@' p4954 tp4955 Rp4956 g4148 F9890.0 tp4957 a(I9810000 g1 (g5 S')\\\x8f\xc2u~\xc3@' p4958 tp4959 Rp4960 g4148 F5608.0 tp4961 a(I9820000 g1 (g5 S'\n\xd7\xa3p\xfd\x8f\xc3@' p4962 tp4963 Rp4964 g4148 F9896.0 tp4965 a(I9830000 g1 (g5 S'{\x14\xaeG!O\xc3@' p4966 tp4967 Rp4968 g4148 F6444.0 tp4969 a(I9840000 g1 (g5 S'\n\xd7\xa3p\xfd|\xc3@' p4970 tp4971 Rp4972 g4148 F13584.0 tp4973 a(I9850000 g1 (g5 S'\xecQ\xb8\x1e\x85\xa9\xc3@' p4974 tp4975 Rp4976 g4148 F15116.0 tp4977 a(I9860000 g1 (g5 S')\\\x8f\xc2u\xc0\xc3@' p4978 tp4979 Rp4980 g4148 F12720.0 tp4981 a(I9870000 g1 (g5 S'\x8f\xc2\xf5(\xdc\xb2\xc3@' p4982 tp4983 Rp4984 g4148 F9220.0 tp4985 a(I9880000 g1 (g5 S'=\n\xd7\xa3\xf0\x85\xc3@' p4986 tp4987 Rp4988 g4148 F10300.0 tp4989 a(I9890000 g1 (g5 S'=\n\xd7\xa30\xaf\xc3@' p4990 tp4991 Rp4992 g4148 F10254.0 tp4993 a(I9900000 g1 (g5 S'\xaeG\xe1z\xd4\xbd\xc3@' p4994 tp4995 Rp4996 g4148 F5730.0 tp4997 a(I9910000 g1 (g5 S'\xcd\xcc\xcc\xcc\x8c\xb9\xc3@' p4998 tp4999 Rp5000 g4148 F8374.0 tp5001 a(I9920000 g1 (g5 S'\xecQ\xb8\x1e\x05\x99\xc3@' p5002 tp5003 Rp5004 g4148 F7386.0 tp5005 a(I9930000 g1 (g5 S'\n\xd7\xa3p}\x93\xc3@' p5006 tp5007 Rp5008 g4148 F6258.0 tp5009 a(I9940000 g1 (g5 S'\n\xd7\xa3p=\x91\xc3@' p5010 tp5011 Rp5012 g4148 F7470.0 tp5013 a(I9950000 g1 (g5 S'\xc3\xf5(\\\x0f\xae\xc3@' p5014 tp5015 Rp5016 g4148 F18198.0 tp5017 a(I9953926 g1 (g5 S')\\\x8f\xc2\xf5\xa2\xc3@' p5018 tp5019 Rp5020 g4148 F5280.0 tp5021 a.(lp0 F308.0 aF176.0 aF308.0 aF264.0 aF440.0 aF176.0 aF528.0 aF528.0 aF176.0 aF484.0 aF176.0 aF264.0 aF396.0 aF220.0 aF484.0 aF352.0 aF176.0 aF660.0 aF352.0 aF440.0 aF440.0 aF572.0 aF616.0 aF132.0 aF308.0 aF308.0 aF220.0 aF352.0 aF440.0 aF264.0 aF528.0 aF396.0 aF264.0 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S'\x1f\x85\xebQ\xb8\x9e!@' p336 tp337 Rp338 g334 F3.0 tp339 a(I650000 g1 (g5 S'\xc3\xf5(\\\x8fB!@' p340 tp341 Rp342 g1 (g5 S'\x9a\x99\x99\x99\x99\x19"@' p343 tp344 Rp345 F5.0 tp346 a(I660000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb!@' p347 tp348 Rp349 g345 F10.0 tp350 a(I670000 g1 (g5 S'fffff\xe6"@' p351 tp352 Rp353 g1 (g5 S'fffff\xe6"@' p354 tp355 Rp356 F19.0 tp357 a(I680000 g1 (g5 S'=\n\xd7\xa3p\xbd#@' p358 tp359 Rp360 g1 (g5 S'\n\xd7\xa3p=\n$@' p361 tp362 Rp363 F7.0 tp364 a(I690000 g1 (g5 S'\xf6(\\\x8f\xc2\xf5#@' p365 tp366 Rp367 g363 F9.0 tp368 a(I700000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8$@' p369 tp370 Rp371 g1 (g5 S'R\xb8\x1e\x85\xeb\xd1$@' p372 tp373 Rp374 F1.0 tp375 a(I710000 g1 (g5 S'\xecQ\xb8\x1e\x85k$@' p376 tp377 Rp378 g1 (g5 S'\xf6(\\\x8f\xc2\xf5$@' p379 tp380 Rp381 F15.0 tp382 a(I720000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99$@' p383 tp384 Rp385 g1 (g5 S'\n\xd7\xa3p=\n%@' p386 tp387 Rp388 F11.0 tp389 a(I730000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb$@' p390 tp391 Rp392 g1 (g5 S'H\xe1z\x14\xaeG%@' p393 tp394 Rp395 F9.0 tp396 a(I740000 g1 (g5 S'\x85\xebQ\xb8\x1e\x05%@' p397 tp398 Rp399 g395 F15.0 tp400 a(I750000 g1 (g5 S'ffffff&@' p401 tp402 Rp403 g1 (g5 S'\xf6(\\\x8f\xc2u&@' p404 tp405 Rp406 F11.0 tp407 a(I760000 g1 (g5 S'\xd7\xa3p=\nW(@' p408 tp409 Rp410 g1 (g5 S'\xd7\xa3p=\nW(@' p411 tp412 Rp413 F5.0 tp414 a(I770000 g1 (g5 S')\\\x8f\xc2\xf5(*@' p415 tp416 Rp417 g1 (g5 S'\xb8\x1e\x85\xebQ8*@' p418 tp419 Rp420 F16.0 tp421 a(I780000 g1 (g5 S'\x85\xebQ\xb8\x1e\x05+@' p422 tp423 Rp424 g1 (g5 S'\x85\xebQ\xb8\x1e\x05+@' p425 tp426 Rp427 F21.0 tp428 a(I790000 g1 (g5 S'\xf6(\\\x8f\xc2u,@' p429 tp430 Rp431 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e,@' p432 tp433 Rp434 F10.0 tp435 a(I800000 g1 (g5 S'\x85\xebQ\xb8\x1e\x85,@' p436 tp437 Rp438 g1 (g5 S'\x00\x00\x00\x00\x00\x00-@' p439 tp440 Rp441 F13.0 tp442 a(I810000 g1 (g5 S')\\\x8f\xc2\xf5\xa8,@' p443 tp444 Rp445 g1 (g5 S'333333-@' p446 tp447 Rp448 F13.0 tp449 a(I820000 g1 (g5 S')\\\x8f\xc2\xf5\xa8-@' p450 tp451 Rp452 g1 (g5 S'fffff\xe6-@' p453 tp454 Rp455 F18.0 tp456 a(I830000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc.@' p457 tp458 Rp459 g1 (g5 S'\x8f\xc2\xf5(\\\x0f/@' p460 tp461 Rp462 F15.0 tp463 a(I840000 g1 (g5 S'fffff\xa60@' p464 tp465 Rp466 g1 (g5 S'fffff\xa60@' p467 tp468 Rp469 F38.0 tp470 a(I850000 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e1@' p471 tp472 Rp473 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e1@' p474 tp475 Rp476 F28.0 tp477 a(I860000 g1 (g5 S'R\xb8\x1e\x85\xebQ2@' p478 tp479 Rp480 g1 (g5 S'R\xb8\x1e\x85\xebQ2@' p481 tp482 Rp483 F31.0 tp484 a(I870000 g1 (g5 S'\xd7\xa3p=\n\x173@' p485 tp486 Rp487 g1 (g5 S'\xd7\xa3p=\n\x173@' p488 tp489 Rp490 F39.0 tp491 a(I880000 g1 (g5 S'\xd7\xa3p=\n\x974@' p492 tp493 Rp494 g1 (g5 S'\xd7\xa3p=\n\x974@' p495 tp496 Rp497 F28.0 tp498 a(I890000 g1 (g5 S'q=\n\xd7\xa3p5@' p499 tp500 Rp501 g1 (g5 S'q=\n\xd7\xa3p5@' p502 tp503 Rp504 F33.0 tp505 a(I900000 g1 (g5 S'\\\x8f\xc2\xf5(\xdc5@' p506 tp507 Rp508 g1 (g5 S'H\xe1z\x14\xae\x076@' p509 tp510 Rp511 F23.0 tp512 a(I910000 g1 (g5 S'\x00\x00\x00\x00\x00\x806@' p513 tp514 Rp515 g1 (g5 S'\x00\x00\x00\x00\x00\x806@' p516 tp517 Rp518 F33.0 tp519 a(I920000 g1 (g5 S'\xc3\xf5(\\\x8f\x027@' p520 tp521 Rp522 g1 (g5 S'\xc3\xf5(\\\x8f\x027@' p523 tp524 Rp525 F32.0 tp526 a(I930000 g1 (g5 S'H\xe1z\x14\xae\x078@' p527 tp528 Rp529 g1 (g5 S'H\xe1z\x14\xae\x078@' p530 tp531 Rp532 F30.0 tp533 a(I940000 g1 (g5 S'=\n\xd7\xa3p=9@' p534 tp535 Rp536 g1 (g5 S'=\n\xd7\xa3p=9@' p537 tp538 Rp539 F42.0 tp540 a(I950000 g1 (g5 S'\xd7\xa3p=\nW:@' p541 tp542 Rp543 g1 (g5 S'\xd7\xa3p=\nW:@' p544 tp545 Rp546 F34.0 tp547 a(I960000 g1 (g5 S'\\\x8f\xc2\xf5(\\;@' p548 tp549 Rp550 g1 (g5 S'\xecQ\xb8\x1e\x85k;@' p551 tp552 Rp553 F42.0 tp554 a(I970000 g1 (g5 S'R\xb8\x1e\x85\xebQ<@' p555 tp556 Rp557 g1 (g5 S'R\xb8\x1e\x85\xebQ<@' p558 tp559 Rp560 F37.0 tp561 a(I980000 g1 (g5 S'R\xb8\x1e\x85\xeb\x11=@' p562 tp563 Rp564 g1 (g5 S'R\xb8\x1e\x85\xeb\x11=@' p565 tp566 Rp567 F34.0 tp568 a(I990000 g1 (g5 S')\\\x8f\xc2\xf5\xe8>@' p569 tp570 Rp571 g1 (g5 S'\xf6(\\\x8f\xc2\xf5>@' p572 tp573 Rp574 F11.0 tp575 a(I1000000 g1 (g5 S'\xe1z\x14\xaeGA@@' p576 tp577 Rp578 g1 (g5 S'\xe1z\x14\xaeGA@@' p579 tp580 Rp581 F25.0 tp582 a(I1010000 g1 (g5 S'=\n\xd7\xa3p\x1dA@' p583 tp584 Rp585 g1 (g5 S'=\n\xd7\xa3p\x1dA@' p586 tp587 Rp588 F19.0 tp589 a(I1020000 g1 (g5 S'\\\x8f\xc2\xf5(\xbcA@' p590 tp591 Rp592 g1 (g5 S'\\\x8f\xc2\xf5(\xbcA@' p593 tp594 Rp595 F42.0 tp596 a(I1030000 g1 (g5 S'\x85\xebQ\xb8\x1e\x85B@' p597 tp598 Rp599 g1 (g5 S'\x85\xebQ\xb8\x1e\x85B@' p600 tp601 Rp602 F78.0 tp603 a(I1040000 g1 (g5 S'q=\n\xd7\xa3\xf0B@' p604 tp605 Rp606 g1 (g5 S'q=\n\xd7\xa3\xf0B@' p607 tp608 Rp609 F58.0 tp610 a(I1050000 g1 (g5 S'fffff\xc6C@' p611 tp612 Rp613 g1 (g5 S'\xecQ\xb8\x1e\x85\xcbC@' p614 tp615 Rp616 F46.0 tp617 a(I1060000 g1 (g5 S'\x1f\x85\xebQ\xb8\x1eD@' p618 tp619 Rp620 g1 (g5 S'fffff&D@' p621 tp622 Rp623 F38.0 tp624 a(I1070000 g1 (g5 S'\x00\x00\x00\x00\x00`D@' p625 tp626 Rp627 g1 (g5 S'\x00\x00\x00\x00\x00`D@' p628 tp629 Rp630 F74.0 tp631 a(I1080000 g1 (g5 S'\xf6(\\\x8f\xc2\xb5D@' p632 tp633 Rp634 g1 (g5 S'\xf6(\\\x8f\xc2\xb5D@' p635 tp636 Rp637 F24.0 tp638 a(I1090000 g1 (g5 S')\\\x8f\xc2\xf5\x08E@' p639 tp640 Rp641 g1 (g5 S')\\\x8f\xc2\xf5\x08E@' p642 tp643 Rp644 F86.0 tp645 a(I1100000 g1 (g5 S'{\x14\xaeG\xe1\x9aE@' p646 tp647 Rp648 g1 (g5 S'{\x14\xaeG\xe1\x9aE@' p649 tp650 Rp651 F77.0 tp652 a(I1110000 g1 (g5 S'\n\xd7\xa3p=\xeaE@' p653 tp654 Rp655 g1 (g5 S'\n\xd7\xa3p=\xeaE@' p656 tp657 Rp658 F54.0 tp659 a(I1120000 g1 (g5 S'\xd7\xa3p=\nWF@' p660 tp661 Rp662 g1 (g5 S'\xd7\xa3p=\nWF@' p663 tp664 Rp665 F46.0 tp666 a(I1130000 g1 (g5 S'33333sF@' p667 tp668 Rp669 g1 (g5 S'33333sF@' p670 tp671 Rp672 F61.0 tp673 a(I1140000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8F@' p674 tp675 Rp676 g1 (g5 S'\xc3\xf5(\\\x8f\xe2F@' p677 tp678 Rp679 F45.0 tp680 a(I1150000 g1 (g5 S'\x8f\xc2\xf5(\\\xcfF@' p681 tp682 Rp683 g679 F40.0 tp684 a(I1160000 g1 (g5 S'\x1f\x85\xebQ\xb8\xbeF@' p685 tp686 Rp687 g1 (g5 S'fffffFG@' p688 tp689 Rp690 F33.0 tp691 a(I1170000 g1 (g5 S'\xc3\xf5(\\\x8f"G@' p692 tp693 Rp694 g1 (g5 S'\x8f\xc2\xf5(\\\x8fG@' p695 tp696 Rp697 F48.0 tp698 a(I1180000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcclG@' p699 tp700 Rp701 g697 F64.0 tp702 a(I1190000 g1 (g5 S'\xb8\x1e\x85\xebQ\xf8G@' p703 tp704 Rp705 g1 (g5 S'\xb8\x1e\x85\xebQ\xf8G@' p706 tp707 Rp708 F75.0 tp709 a(I1200000 g1 (g5 S'\x85\xebQ\xb8\x1e\x05H@' p710 tp711 Rp712 g1 (g5 S'33333\x13H@' p713 tp714 Rp715 F33.0 tp716 a(I1210000 g1 (g5 S"H\xe1z\x14\xae'H@" p717 tp718 Rp719 g1 (g5 S'\\\x8f\xc2\xf5([@' p1389 tp1390 Rp1391 g1 (g5 S'\x85\xebQ\xb8\x1e\xe5[@' p1392 tp1393 Rp1394 F115.0 tp1395 a(I2540000 g1 (g5 S'\xe1z\x14\xaeG\xd1[@' p1396 tp1397 Rp1398 g1394 F136.0 tp1399 a(I2550000 g1 (g5 S'\x85\xebQ\xb8\x1e\x05\\@' p1400 tp1401 Rp1402 g1 (g5 S'\x1f\x85\xebQ\xb8n\\@' p1403 tp1404 Rp1405 F101.0 tp1406 a(I2560000 g1 (g5 S'H\xe1z\x14\xaeG\\@' p1407 tp1408 Rp1409 g1 (g5 S'{\x14\xaeG\xe1\x8a\\@' p1410 tp1411 Rp1412 F78.0 tp1413 a(I2570000 g1 (g5 S'\x14\xaeG\xe1z\xe4\\@' p1414 tp1415 Rp1416 g1 (g5 S'\x14\xaeG\xe1z\xe4\\@' p1417 tp1418 Rp1419 F227.0 tp1420 a(I2580000 g1 (g5 S'\x14\xaeG\xe1zT]@' p1421 tp1422 Rp1423 g1 (g5 S'{\x14\xaeG\xe1\x9a]@' p1424 tp1425 Rp1426 F89.0 tp1427 a(I2590000 g1 (g5 S'R\xb8\x1e\x85\xeb\xe1]@' p1428 tp1429 Rp1430 g1 (g5 S'33333\x03^@' p1431 tp1432 Rp1433 F95.0 tp1434 a(I2600000 g1 (g5 S'R\xb8\x1e\x85\xebQ^@' p1435 tp1436 Rp1437 g1 (g5 S'R\xb8\x1e\x85\xebQ^@' p1438 tp1439 Rp1440 F165.0 tp1441 a(I2610000 g1 (g5 S'{\x14\xaeG\xe1j]@' p1442 tp1443 Rp1444 g1 (g5 S'q=\n\xd7\xa3`^@' p1445 tp1446 Rp1447 F73.0 tp1448 a(I2620000 g1 (g5 S'H\xe1z\x14\xae7]@' p1449 tp1450 Rp1451 g1447 F170.0 tp1452 a(I2630000 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e]@' p1453 tp1454 Rp1455 g1447 F91.0 tp1456 a(I2640000 g1 (g5 S'\x00\x00\x00\x00\x00\xd0^@' p1457 tp1458 Rp1459 g1 (g5 S'\xb8\x1e\x85\xebQ\xf8^@' p1460 tp1461 Rp1462 F170.0 tp1463 a(I2650000 g1 (g5 S'\xe1z\x14\xaeG\x11_@' p1464 tp1465 Rp1466 g1 (g5 S'\xc3\xf5(\\\x8fb_@' p1467 tp1468 Rp1469 F106.0 tp1470 a(I2660000 g1 (g5 S'\x14\xaeG\xe1z\xc4^@' p1471 tp1472 Rp1473 g1469 F49.0 tp1474 a(I2670000 g1 (g5 S'\n\xd7\xa3p=\xda^@' p1475 tp1476 Rp1477 g1469 F180.0 tp1478 a(I2680000 g1 (g5 S'\xaeG\xe1z\x14\x8e]@' p1479 tp1480 Rp1481 g1469 F53.0 tp1482 a(I2690000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc,]@' p1483 tp1484 Rp1485 g1469 F151.0 tp1486 a(I2700000 g1 (g5 S'\xb8\x1e\x85\xebQ\x18^@' p1487 tp1488 Rp1489 g1469 F135.0 tp1490 a(I2710000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x8c]@' p1491 tp1492 Rp1493 g1469 F54.0 tp1494 a(I2720000 g1 (g5 S'\xc3\xf5(\\\x8f\x12]@' p1495 tp1496 Rp1497 g1469 F57.0 tp1498 a(I2730000 g1 (g5 S'R\xb8\x1e\x85\xebA]@' p1499 tp1500 Rp1501 g1469 F276.0 tp1502 a(I2740000 g1 (g5 S'\xaeG\xe1z\x14\xae]@' p1503 tp1504 Rp1505 g1469 F126.0 tp1506 a(I2750000 g1 (g5 S'\x14\xaeG\xe1zD_@' p1507 tp1508 Rp1509 g1469 F270.0 tp1510 a(I2760000 g1 (g5 S'{\x14\xaeG\xe1\n`@' p1511 tp1512 Rp1513 g1 (g5 S'{\x14\xaeG\xe1\n`@' p1514 tp1515 Rp1516 F185.0 tp1517 a(I2770000 g1 (g5 S'q=\n\xd7\xa3\xa0_@' p1518 tp1519 Rp1520 g1516 F230.0 tp1521 a(I2780000 g1 (g5 S'\x14\xaeG\xe1z\xd4^@' p1522 tp1523 Rp1524 g1516 F154.0 tp1525 a(I2790000 g1 (g5 S'\xf6(\\\x8f\xc2%^@' p1526 tp1527 Rp1528 g1516 F87.0 tp1529 a(I2800000 g1 (g5 S'\x1f\x85\xebQ\xb8\xce]@' p1530 tp1531 Rp1532 g1516 F82.0 tp1533 a(I2810000 g1 (g5 S'\xb8\x1e\x85\xebQ(]@' p1534 tp1535 Rp1536 g1516 F106.0 tp1537 a(I2820000 g1 (g5 S'33333\x13]@' p1538 tp1539 Rp1540 g1516 F276.0 tp1541 a(I2830000 g1 (g5 S'\x85\xebQ\xb8\x1eE\\@' p1542 tp1543 Rp1544 g1516 F130.0 tp1545 a(I2840000 g1 (g5 S'\n\xd7\xa3p=\xcaZ@' p1546 tp1547 Rp1548 g1516 F39.0 tp1549 a(I2850000 g1 (g5 S'\xd7\xa3p=\n7Y@' p1550 tp1551 Rp1552 g1516 F32.0 tp1553 a(I2860000 g1 (g5 S'\x14\xaeG\xe1z\x04V@' p1554 tp1555 Rp1556 g1516 F21.0 tp1557 a(I2870000 g1 (g5 S'\x1f\x85\xebQ\xb8\xaeP@' p1558 tp1559 Rp1560 g1516 F15.0 tp1561 a(I2880000 g1 (g5 S'\\\x8f\xc2\xf5(\x9cF@' p1562 tp1563 Rp1564 g1516 F4.0 tp1565 a(I2890000 g1 (g5 S'\xf6(\\\x8f\xc257@' p1566 tp1567 Rp1568 g1516 F13.0 tp1569 a(I2900000 g1 (g5 S'\xf6(\\\x8f\xc2u @' p1570 tp1571 Rp1572 g1516 F3.0 tp1573 a(I2910000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\r@' p1574 tp1575 Rp1576 g1516 F1.0 tp1577 a(I2920000 g1 (g5 S'\xd7\xa3p=\n\xd7\x01@' p1578 tp1579 Rp1580 g1516 F6.0 tp1581 a(I2930000 g1 (g5 S'R\xb8\x1e\x85\xebQ\x00@' p1582 tp1583 Rp1584 g1516 F4.0 tp1585 a(I2940000 g1 (g5 S'=\n\xd7\xa3p=\x00@' p1586 tp1587 Rp1588 g1516 F1.0 tp1589 a(I2950000 g1 (g5 S'\xc3\xf5(\\\x8f\xc2\x03@' p1590 tp1591 Rp1592 g1516 F3.0 tp1593 a(I2960000 g1 (g5 S'q=\n\xd7\xa3p\x07@' p1594 tp1595 Rp1596 g1516 F4.0 tp1597 a(I2970000 g1 (g5 S'\x14\xaeG\xe1z\x14\x08@' p1598 tp1599 Rp1600 g1516 F6.0 tp1601 a(I2980000 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e\x07@' p1602 tp1603 Rp1604 g1516 F5.0 tp1605 a(I2990000 g1 (g5 S'333333\x03@' p1606 tp1607 Rp1608 g1516 F0.0 tp1609 a(I3000000 g1 (g5 S'\xd7\xa3p=\n\xd7\x01@' p1610 tp1611 Rp1612 g1516 F3.0 tp1613 a(I3010000 g1 (g5 S'{\x14\xaeG\xe1z\x02@' p1614 tp1615 Rp1616 g1516 F2.0 tp1617 a(I3020000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\x03@' p1618 tp1619 Rp1620 g1516 F2.0 tp1621 a(I3030000 g1 (g5 S'\\\x8f\xc2\xf5(\\\x03@' p1622 tp1623 Rp1624 g1516 F2.0 tp1625 a(I3040000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc\x04@' p1626 tp1627 Rp1628 g1516 F1.0 tp1629 a(I3050000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f\x02@' p1630 tp1631 Rp1632 g1516 F1.0 tp1633 a(I3060000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00@' p1634 tp1635 Rp1636 g1516 F3.0 tp1637 a(I3070000 g1 (g5 S'\x85\xebQ\xb8\x1e\x85\x03@' p1638 tp1639 Rp1640 g1516 F7.0 tp1641 a(I3080000 g1 (g5 S'\x85\xebQ\xb8\x1e\x85\x0b@' p1642 tp1643 Rp1644 g1516 F4.0 tp1645 a(I3090000 g1 (g5 S'\\\x8f\xc2\xf5(\\\x0f@' p1646 tp1647 Rp1648 g1516 F5.0 tp1649 a(I3100000 g1 (g5 S'\xaeG\xe1z\x14\xae\x12@' p1650 tp1651 Rp1652 g1516 F2.0 tp1653 a(I3110000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\x12@' p1654 tp1655 Rp1656 g1516 F6.0 tp1657 a(I3120000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\x12@' p1658 tp1659 Rp1660 g1516 F2.0 tp1661 a(I3130000 g1 (g5 S'\\\x8f\xc2\xf5(\\\x11@' p1662 tp1663 Rp1664 g1516 F3.0 tp1665 a(I3140000 g1 (g5 S'\xe1z\x14\xaeG\xe1\x10@' p1666 tp1667 Rp1668 g1516 F1.0 tp1669 a(I3150000 g1 (g5 S'\x85\xebQ\xb8\x1e\x85\x10@' p1670 tp1671 Rp1672 g1516 F1.0 tp1673 a(I3160000 g1 (g5 S'\xf6(\\\x8f\xc2\xf5\x10@' p1674 tp1675 Rp1676 g1516 F4.0 tp1677 a(I3170000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f\x12@' p1678 tp1679 Rp1680 g1516 F17.0 tp1681 a(I3180000 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e\x13@' p1682 tp1683 Rp1684 g1516 F3.0 tp1685 a(I3190000 g1 (g5 S'\n\xd7\xa3p=\n\x13@' p1686 tp1687 Rp1688 g1516 F0.0 tp1689 a(I3200000 g1 (g5 S'\\\x8f\xc2\xf5(\\\x11@' p1690 tp1691 Rp1692 g1516 F4.0 tp1693 a(I3210000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x10@' p1694 tp1695 Rp1696 g1516 F7.0 tp1697 a(I3220000 g1 (g5 S'{\x14\xaeG\xe1z\x12@' p1698 tp1699 Rp1700 g1516 F6.0 tp1701 a(I3230000 g1 (g5 S'q=\n\xd7\xa3p\x14@' p1702 tp1703 Rp1704 g1516 F8.0 tp1705 a(I3240000 g1 (g5 S'\xf6(\\\x8f\xc2\xf5\x15@' p1706 tp1707 Rp1708 g1516 F5.0 tp1709 a(I3250000 g1 (g5 S'\x14\xaeG\xe1z\x14\x18@' p1710 tp1711 Rp1712 g1516 F7.0 tp1713 a(I3260000 g1 (g5 S'ffffff\x16@' p1714 tp1715 Rp1716 g1516 F8.0 tp1717 a(I3270000 g1 (g5 S'R\xb8\x1e\x85\xebQ\x16@' p1718 tp1719 Rp1720 g1516 F1.0 tp1721 a(I3280000 g1 (g5 S'\n\xd7\xa3p=\n\x16@' p1722 tp1723 Rp1724 g1516 F5.0 tp1725 a(I3290000 g1 (g5 S'{\x14\xaeG\xe1z\x17@' p1726 tp1727 Rp1728 g1516 F5.0 tp1729 a(I3300000 g1 (g5 S'\xf6(\\\x8f\xc2\xf5\x16@' p1730 tp1731 Rp1732 g1516 F5.0 tp1733 a(I3310000 g1 (g5 S'\\\x8f\xc2\xf5(\\\x15@' p1734 tp1735 Rp1736 g1516 F2.0 tp1737 a(I3320000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x16@' p1738 tp1739 Rp1740 g1516 F6.0 tp1741 a(I3330000 g1 (g5 S'\n\xd7\xa3p=\n\x19@' p1742 tp1743 Rp1744 g1516 F2.0 tp1745 a(I3340000 g1 (g5 S'\xa4p=\n\xd7\xa3\x1b@' p1746 tp1747 Rp1748 g1516 F6.0 tp1749 a(I3350000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\x1d@' p1750 tp1751 Rp1752 g1516 F11.0 tp1753 a(I3360000 g1 (g5 S')\\\x8f\xc2\xf5(\x1f@' p1754 tp1755 Rp1756 g1516 F12.0 tp1757 a(I3370000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8 @' p1758 tp1759 Rp1760 g1516 F7.0 tp1761 a(I3380000 g1 (g5 S'\xe1z\x14\xaeGa!@' p1762 tp1763 Rp1764 g1516 F5.0 tp1765 a(I3390000 g1 (g5 S'\xcd\xcc\xcc\xcc\xccL#@' p1766 tp1767 Rp1768 g1516 F5.0 tp1769 a(I3400000 g1 (g5 S'\x00\x00\x00\x00\x00\x00%@' p1770 tp1771 Rp1772 g1516 F8.0 tp1773 a(I3410000 g1 (g5 S'H\xe1z\x14\xae\xc7$@' p1774 tp1775 Rp1776 g1516 F8.0 tp1777 a(I3420000 g1 (g5 S'{\x14\xaeG\xe1\xfa$@' p1778 tp1779 Rp1780 g1516 F13.0 tp1781 a(I3430000 g1 (g5 S'{\x14\xaeG\xe1\xfa%@' p1782 tp1783 Rp1784 g1516 F18.0 tp1785 a(I3440000 g1 (g5 S'\x14\xaeG\xe1z\x94&@' p1786 tp1787 Rp1788 g1516 F15.0 tp1789 a(I3450000 g1 (g5 S'\xc3\xf5(\\\x8f\xc2%@' p1790 tp1791 Rp1792 g1516 F9.0 tp1793 a(I3460000 g1 (g5 S"\xb8\x1e\x85\xebQ8'@" p1794 tp1795 Rp1796 g1516 F9.0 tp1797 a(I3470000 g1 (g5 S'fffff\xe6(@' p1798 tp1799 Rp1800 g1516 F13.0 tp1801 a(I3480000 g1 (g5 S'\x85\xebQ\xb8\x1e\x05+@' p1802 tp1803 Rp1804 g1516 F11.0 tp1805 a(I3490000 g1 (g5 S'\x14\xaeG\xe1z\x14*@' p1806 tp1807 Rp1808 g1516 F5.0 tp1809 a(I3500000 g1 (g5 S')\\\x8f\xc2\xf5\xa8*@' p1810 tp1811 Rp1812 g1516 F19.0 tp1813 a(I3510000 g1 (g5 S'\x14\xaeG\xe1z\x14,@' p1814 tp1815 Rp1816 g1516 F17.0 tp1817 a(I3520000 g1 (g5 S'\xc3\xf5(\\\x8fB-@' p1818 tp1819 Rp1820 g1516 F21.0 tp1821 a(I3530000 g1 (g5 S'\\\x8f\xc2\xf5(\xdc,@' p1822 tp1823 Rp1824 g1516 F12.0 tp1825 a(I3540000 g1 (g5 S'\xd7\xa3p=\n\xd7+@' p1826 tp1827 Rp1828 g1516 F7.0 tp1829 a(I3550000 g1 (g5 S'\xe1z\x14\xaeGa.@' p1830 tp1831 Rp1832 g1516 F30.0 tp1833 a(I3560000 g1 (g5 S'=\n\xd7\xa3p=/@' p1834 tp1835 Rp1836 g1516 F8.0 tp1837 a(I3570000 g1 (g5 S'\xecQ\xb8\x1e\x85k/@' p1838 tp1839 Rp1840 g1516 F18.0 tp1841 a(I3580000 g1 (g5 S'\xc3\xf5(\\\x8f\xc2.@' p1842 tp1843 Rp1844 g1516 F26.0 tp1845 a(I3590000 g1 (g5 S'\xe1z\x14\xaeGa0@' p1846 tp1847 Rp1848 g1516 F28.0 tp1849 a(I3600000 g1 (g5 S'{\x14\xaeG\xe1:1@' p1850 tp1851 Rp1852 g1516 F22.0 tp1853 a(I3610000 g1 (g5 S'ffffff1@' p1854 tp1855 Rp1856 g1516 F17.0 tp1857 a(I3620000 g1 (g5 S'{\x14\xaeG\xe1\xfa1@' p1858 tp1859 Rp1860 g1516 F18.0 tp1861 a(I3630000 g1 (g5 S'\xf6(\\\x8f\xc2\xf52@' p1862 tp1863 Rp1864 g1516 F12.0 tp1865 a(I3640000 g1 (g5 S'{\x14\xaeG\xe1\xba3@' p1866 tp1867 Rp1868 g1516 F30.0 tp1869 a(I3650000 g1 (g5 S'q=\n\xd7\xa3\xb04@' p1870 tp1871 Rp1872 g1516 F34.0 tp1873 a(I3660000 g1 (g5 S'\xa4p=\n\xd7\xe34@' p1874 tp1875 Rp1876 g1516 F20.0 tp1877 a(I3670000 g1 (g5 S'\xaeG\xe1z\x14n4@' p1878 tp1879 Rp1880 g1516 F19.0 tp1881 a(I3680000 g1 (g5 S'\xf6(\\\x8f\xc2\xb54@' p1882 tp1883 Rp1884 g1516 F17.0 tp1885 a(I3690000 g1 (g5 S'\xb8\x1e\x85\xebQ85@' p1886 tp1887 Rp1888 g1516 F30.0 tp1889 a(I3700000 g1 (g5 S'{\x14\xaeG\xe1:5@' p1890 tp1891 Rp1892 g1516 F27.0 tp1893 a(I3710000 g1 (g5 S'=\n\xd7\xa3p=5@' p1894 tp1895 Rp1896 g1516 F10.0 tp1897 a(I3720000 g1 (g5 S'\x14\xaeG\xe1z\x945@' p1898 tp1899 Rp1900 g1516 F19.0 tp1901 a(I3730000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab5@' p1902 tp1903 Rp1904 g1516 F17.0 tp1905 a(I3740000 g1 (g5 S'33333\xf35@' p1906 tp1907 Rp1908 g1516 F40.0 tp1909 a(I3750000 g1 (g5 S')\\\x8f\xc2\xf5h7@' p1910 tp1911 Rp1912 g1516 F36.0 tp1913 a(I3760000 g1 (g5 S'\xc3\xf5(\\\x8f\x828@' p1914 tp1915 Rp1916 g1516 F23.0 tp1917 a(I3770000 g1 (g5 S'\x00\x00\x00\x00\x00\xc08@' p1918 tp1919 Rp1920 g1516 F31.0 tp1921 a(I3780000 g1 (g5 S'\x14\xaeG\xe1z\x949@' p1922 tp1923 Rp1924 g1516 F22.0 tp1925 a(I3790000 g1 (g5 S'\x85\xebQ\xb8\x1e\x05:@' p1926 tp1927 Rp1928 g1516 F14.0 tp1929 a(I3800000 g1 (g5 S'q=\n\xd7\xa30:@' p1930 tp1931 Rp1932 g1516 F17.0 tp1933 a(I3810000 g1 (g5 S'33333s:@' p1934 tp1935 Rp1936 g1516 F19.0 tp1937 a(I3820000 g1 (g5 S'\x9a\x99\x99\x99\x99Y;@' p1938 tp1939 Rp1940 g1516 F34.0 tp1941 a(I3830000 g1 (g5 S')\\\x8f\xc2\xf5h;@' p1942 tp1943 Rp1944 g1516 F50.0 tp1945 a(I3840000 g1 (g5 S'R\xb8\x1e\x85\xeb\x91;@' p1946 tp1947 Rp1948 g1516 F21.0 tp1949 a(I3850000 g1 (g5 S'fffff\xe6;@' p1950 tp1951 Rp1952 g1516 F24.0 tp1953 a(I3860000 g1 (g5 S'H\xe1z\x14\xae\x07?@' p1954 tp1955 Rp1956 g1516 F56.0 tp1957 a(I3870000 g1 (g5 S'=\n\xd7\xa3p}?@' p1958 tp1959 Rp1960 g1516 F18.0 tp1961 a(I3880000 g1 (g5 S'\xaeG\xe1z\x14\x0e@@' p1962 tp1963 Rp1964 g1516 F36.0 tp1965 a(I3890000 g1 (g5 S'33333\xd3@@' p1966 tp1967 Rp1968 g1516 F73.0 tp1969 a(I3900000 g1 (g5 S'\x85\xebQ\xb8\x1e\xa5A@' p1970 tp1971 Rp1972 g1516 F45.0 tp1973 a(I3910000 g1 (g5 S'\n\xd7\xa3p=\nB@' p1974 tp1975 Rp1976 g1516 F42.0 tp1977 a(I3920000 g1 (g5 S'H\xe1z\x14\xae\x87B@' p1978 tp1979 Rp1980 g1516 F56.0 tp1981 a(I3930000 g1 (g5 S'\\\x8f\xc2\xf5(w@' p4015 tp4016 Rp4017 g3933 F416.0 tp4018 a(I8100000 g1 (g5 S'\x00\x00\x00\x00\x00\x98w@' p4019 tp4020 Rp4021 g3933 F830.0 tp4022 a(I8110000 g1 (g5 S'33333\x8bw@' p4023 tp4024 Rp4025 g3933 F424.0 tp4026 a(I8120000 g1 (g5 S'\n\xd7\xa3p=\x92w@' p4027 tp4028 Rp4029 g3933 F425.0 tp4030 a(I8130000 g1 (g5 S'\n\xd7\xa3p=\x9aw@' p4031 tp4032 Rp4033 g3933 F411.0 tp4034 a(I8140000 g1 (g5 S'R\xb8\x1e\x85\xeb\xa1w@' p4035 tp4036 Rp4037 g3933 F414.0 tp4038 a(I8150000 g1 (g5 S'H\xe1z\x14\xae\xafw@' p4039 tp4040 Rp4041 g3933 F408.0 tp4042 a(I8160000 g1 (g5 S'=\n\xd7\xa3p\xa5w@' p4043 tp4044 Rp4045 g3933 F413.0 tp4046 a(I8170000 g1 (g5 S'\x85\xebQ\xb8\x1e\x91w@' p4047 tp4048 Rp4049 g3933 F428.0 tp4050 a(I8180000 g1 (g5 S'\xe1z\x14\xaeG\xa9w@' p4051 tp4052 Rp4053 g3933 F435.0 tp4054 a(I8190000 g1 (g5 S'\x14\xaeG\xe1z\xb8w@' p4055 tp4056 Rp4057 g3933 F359.0 tp4058 a(I8200000 g1 (g5 S'H\xe1z\x14\xae\xf7w@' p4059 tp4060 Rp4061 g3933 F424.0 tp4062 a(I8210000 g1 (g5 S'\x1f\x85\xebQ\xb8\xeew@' p4063 tp4064 Rp4065 g3933 F310.0 tp4066 a(I8220000 g1 (g5 S'\xb8\x1e\x85\xebQ\xc0w@' p4067 tp4068 Rp4069 g3933 F90.0 tp4070 a(I8230000 g1 (g5 S'H\xe1z\x14\xaeww@' p4071 tp4072 Rp4073 g3933 F124.0 tp4074 a(I8240000 g1 (g5 S'\n\xd7\xa3p=^w@' p4075 tp4076 Rp4077 g3933 F275.0 tp4078 a(I8250000 g1 (g5 S'\\\x8f\xc2\xf5(hw@' p4079 tp4080 Rp4081 g3933 F428.0 tp4082 a(I8260000 g1 (g5 S'H\xe1z\x14\xae\xa3w@' p4083 tp4084 Rp4085 g3933 F220.0 tp4086 a(I8270000 g1 (g5 S'\xaeG\xe1z\x14\x82w@' p4087 tp4088 Rp4089 g3933 F381.0 tp4090 a(I8280000 g1 (g5 S'\n\xd7\xa3p=fw@' p4091 tp4092 Rp4093 g3933 F227.0 tp4094 a(I8290000 g1 (g5 S'\x14\xaeG\xe1z\x8cw@' p4095 tp4096 Rp4097 g3933 F435.0 tp4098 a(I8300000 g1 (g5 S'=\n\xd7\xa3p\x91w@' p4099 tp4100 Rp4101 g3933 F332.0 tp4102 a(I8310000 g1 (g5 S'\xb8\x1e\x85\xebQ\x98w@' p4103 tp4104 Rp4105 g3933 F334.0 tp4106 a(I8320000 g1 (g5 S'\xa4p=\n\xd7\xa7w@' p4107 tp4108 Rp4109 g3933 F426.0 tp4110 a(I8330000 g1 (g5 S'\\\x8f\xc2\xf5(\x9cw@' p4111 tp4112 Rp4113 g3933 F382.0 tp4114 a(I8340000 g1 (g5 S'q=\n\xd7\xa3\xb8w@' p4115 tp4116 Rp4117 g3933 F388.0 tp4118 a(I8350000 g1 (g5 S'33333\xa3w@' p4119 tp4120 Rp4121 g3933 F313.0 tp4122 a(I8360000 g1 (g5 S'q=\n\xd7\xa3\xa8w@' p4123 tp4124 Rp4125 g3933 F437.0 tp4126 a(I8370000 g1 (g5 S'\x9a\x99\x99\x99\x99\xcdw@' p4127 tp4128 Rp4129 g3933 F392.0 tp4130 a(I8380000 g1 (g5 S'\x9a\x99\x99\x99\x99\x05x@' p4131 tp4132 Rp4133 g3933 F315.0 tp4134 a(I8390000 g1 (g5 S'H\xe1z\x14\xae\x9fw@' p4135 tp4136 Rp4137 g3933 F216.0 tp4138 a(I8400000 g1 (g5 S'\xa4p=\n\xd7?w@' p4139 tp4140 Rp4141 g3933 F360.0 tp4142 a(I8410000 g1 (g5 S'\x1f\x85\xebQ\xb8:w@' p4143 tp4144 Rp4145 g3933 F321.0 tp4146 a(I8420000 g1 (g5 S'\xa4p=\n\xd7\x9fv@' p4147 tp4148 Rp4149 g3933 F107.0 tp4150 a(I8430000 g1 (g5 S'=\n\xd7\xa3p\xa5v@' p4151 tp4152 Rp4153 g3933 F422.0 tp4154 a(I8440000 g1 (g5 S'\xd7\xa3p=\n\x87v@' p4155 tp4156 Rp4157 g3933 F425.0 tp4158 a(I8450000 g1 (g5 S'fffffzv@' p4159 tp4160 Rp4161 g3933 F409.0 tp4162 a(I8460000 g1 (g5 S'\x85\xebQ\xb8\x1e}v@' p4163 tp4164 Rp4165 g3933 F418.0 tp4166 a(I8470000 g1 (g5 S'R\xb8\x1e\x85\xeb\x85v@' p4167 tp4168 Rp4169 g3933 F425.0 tp4170 a(I8480000 g1 (g5 S'\n\xd7\xa3p=~v@' p4171 tp4172 Rp4173 g3933 F391.0 tp4174 a(I8490000 g1 (g5 S'fffff\xbev@' p4175 tp4176 Rp4177 g3933 F395.0 tp4178 a(I8500000 g1 (g5 S'\xaeG\xe1z\x14\xe6v@' p4179 tp4180 Rp4181 g3933 F156.0 tp4182 a(I8510000 g1 (g5 S'q=\n\xd7\xa34w@' p4183 tp4184 Rp4185 g3933 F298.0 tp4186 a(I8520000 g1 (g5 S'\x85\xebQ\xb8\x1eaw@' p4187 tp4188 Rp4189 g3933 F402.0 tp4190 a(I8530000 g1 (g5 S'q=\n\xd7\xa3tw@' p4191 tp4192 Rp4193 g3933 F386.0 tp4194 a(I8540000 g1 (g5 S'\xe1z\x14\xaeG1w@' p4195 tp4196 Rp4197 g3933 F393.0 tp4198 a(I8550000 g1 (g5 S'fffffNw@' p4199 tp4200 Rp4201 g3933 F402.0 tp4202 a(I8560000 g1 (g5 S'\xaeG\xe1z\x14>w@' p4203 tp4204 Rp4205 g3933 F299.0 tp4206 a(I8570000 g1 (g5 S'33333Ow@' p4207 tp4208 Rp4209 g3933 F373.0 tp4210 a(I8580000 g1 (g5 S'\n\xd7\xa3p=Zw@' p4211 tp4212 Rp4213 g3933 F422.0 tp4214 a(I8590000 g1 (g5 S'\xaeG\xe1z\x14jw@' p4215 tp4216 Rp4217 g3933 F411.0 tp4218 a(I8600000 g1 (g5 S'33333Sw@' p4219 tp4220 Rp4221 g3933 F347.0 tp4222 a(I8610000 g1 (g5 S'=\n\xd7\xa3p]w@' p4223 tp4224 Rp4225 g3933 F327.0 tp4226 a(I8620000 g1 (g5 S'\xa4p=\n\xd7[w@' p4227 tp4228 Rp4229 g3933 F363.0 tp4230 a(I8630000 g1 (g5 S'33333Kw@' p4231 tp4232 Rp4233 g3933 F324.0 tp4234 a(I8640000 g1 (g5 S'R\xb8\x1e\x85\xeb]w@' p4235 tp4236 Rp4237 g3933 F398.0 tp4238 a(I8650000 g1 (g5 S'\xa4p=\n\xd7/w@' p4239 tp4240 Rp4241 g3933 F347.0 tp4242 a(I8660000 g1 (g5 S'R\xb8\x1e\x85\xeb1w@' p4243 tp4244 Rp4245 g3933 F427.0 tp4246 a(I8670000 g1 (g5 S"H\xe1z\x14\xae'w@" p4247 tp4248 Rp4249 g3933 F436.0 tp4250 a(I8680000 g1 (g5 S'\x8f\xc2\xf5(\\Ww@' p4251 tp4252 Rp4253 g3933 F408.0 tp4254 a(I8690000 g1 (g5 S'\xa4p=\n\xd7Sw@' p4255 tp4256 Rp4257 g3933 F377.0 tp4258 a(I8700000 g1 (g5 S'\xb8\x1e\x85\xebQ|w@' p4259 tp4260 Rp4261 g3933 F348.0 tp4262 a(I8710000 g1 (g5 S'\x9a\x99\x99\x99\x99Uw@' p4263 tp4264 Rp4265 g3933 F193.0 tp4266 a(I8720000 g1 (g5 S'\\\x8f\xc2\xf5(\x84w@' p4267 tp4268 Rp4269 g3933 F419.0 tp4270 a(I8730000 g1 (g5 S'\xf6(\\\x8f\xc2\x89w@' p4271 tp4272 Rp4273 g3933 F258.0 tp4274 a(I8740000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x9cw@' p4275 tp4276 Rp4277 g3933 F344.0 tp4278 a(I8750000 g1 (g5 S'\x85\xebQ\xb8\x1e\x99w@' p4279 tp4280 Rp4281 g3933 F379.0 tp4282 a(I8760000 g1 (g5 S'\xc3\xf5(\\\x8frw@' p4283 tp4284 Rp4285 g3933 F434.0 tp4286 a(I8770000 g1 (g5 S'33333sw@' p4287 tp4288 Rp4289 g3933 F342.0 tp4290 a(I8780000 g1 (g5 S'\xb8\x1e\x85\xebQXw@' p4291 tp4292 Rp4293 g3933 F427.0 tp4294 a(I8790000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc0w@' p4295 tp4296 Rp4297 g3933 F104.0 tp4298 a(I8800000 g1 (g5 S'\x00\x00\x00\x00\x00\xf8v@' p4299 tp4300 Rp4301 g3933 F380.0 tp4302 a(I8810000 g1 (g5 S'\xf6(\\\x8f\xc2\x15w@' p4303 tp4304 Rp4305 g3933 F428.0 tp4306 a(I8820000 g1 (g5 S'\xf6(\\\x8f\xc2\xfdv@' p4307 tp4308 Rp4309 g3933 F410.0 tp4310 a(I8830000 g1 (g5 S'\xaeG\xe1z\x14\x06w@' p4311 tp4312 Rp4313 g3933 F422.0 tp4314 a(I8840000 g1 (g5 S'fffff*w@' p4315 tp4316 Rp4317 g3933 F395.0 tp4318 a(I8850000 g1 (g5 S'\xd7\xa3p=\n\x0fw@' p4319 tp4320 Rp4321 g3933 F372.0 tp4322 a(I8860000 g1 (g5 S'\xa4p=\n\xd7\x17w@' p4323 tp4324 Rp4325 g3933 F338.0 tp4326 a(I8870000 g1 (g5 S'\xe1z\x14\xaeG\x11w@' p4327 tp4328 Rp4329 g3933 F390.0 tp4330 a(I8880000 g1 (g5 S'\n\xd7\xa3p=\xf6v@' p4331 tp4332 Rp4333 g3933 F268.0 tp4334 a(I8890000 g1 (g5 S'q=\n\xd7\xa3\x14w@' p4335 tp4336 Rp4337 g3933 F416.0 tp4338 a(I8900000 g1 (g5 S'=\n\xd7\xa3p!w@' p4339 tp4340 Rp4341 g3933 F441.0 tp4342 a(I8910000 g1 (g5 S'H\xe1z\x14\xae\x0fw@' p4343 tp4344 Rp4345 g3933 F423.0 tp4346 a(I8920000 g1 (g5 S'\n\xd7\xa3p=\xcav@' p4347 tp4348 Rp4349 g3933 F405.0 tp4350 a(I8930000 g1 (g5 S'\xf6(\\\x8f\xc2\tw@' p4351 tp4352 Rp4353 g3933 F406.0 tp4354 a(I8940000 g1 (g5 S'\x00\x00\x00\x00\x00\x1cw@' p4355 tp4356 Rp4357 g3933 F417.0 tp4358 a(I8950000 g1 (g5 S'=\n\xd7\xa3p\xf5v@' p4359 tp4360 Rp4361 g3933 F415.0 tp4362 a(I8960000 g1 (g5 S'\\\x8f\xc2\xf5( w@' p4363 tp4364 Rp4365 g3933 F418.0 tp4366 a(I8970000 g1 (g5 S'\xb8\x1e\x85\xebQ\x10w@' p4367 tp4368 Rp4369 g3933 F200.0 tp4370 a(I8980000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x04w@' p4371 tp4372 Rp4373 g3933 F428.0 tp4374 a(I8990000 g1 (g5 S'\x8f\xc2\xf5(\\#w@' p4375 tp4376 Rp4377 g3933 F431.0 tp4378 a(I9000000 g1 (g5 S'\xa4p=\n\xd7_w@' p4379 tp4380 Rp4381 g3933 F416.0 tp4382 a(I9010000 g1 (g5 S'\n\xd7\xa3p=fw@' p4383 tp4384 Rp4385 g3933 F417.0 tp4386 a(I9020000 g1 (g5 S'\x9a\x99\x99\x99\x99}w@' p4387 tp4388 Rp4389 g3933 F393.0 tp4390 a(I9030000 g1 (g5 S'\xa4p=\n\xd7\x83w@' p4391 tp4392 Rp4393 g3933 F428.0 tp4394 a(I9040000 g1 (g5 S'\x00\x00\x00\x00\x00xw@' p4395 tp4396 Rp4397 g3933 F399.0 tp4398 a(I9050000 g1 (g5 S'{\x14\xaeG\xe1\x9aw@' p4399 tp4400 Rp4401 g3933 F344.0 tp4402 a(I9060000 g1 (g5 S'\xc3\xf5(\\\x8f\x9aw@' p4403 tp4404 Rp4405 g3933 F422.0 tp4406 a(I9070000 g1 (g5 S'\xf6(\\\x8f\xc2\xa5w@' p4407 tp4408 Rp4409 g3933 F424.0 tp4410 a(I9080000 g1 (g5 S'\\\x8f\xc2\xf5(\xacw@' p4411 tp4412 Rp4413 g3933 F398.0 tp4414 a(I9090000 g1 (g5 S'\x8f\xc2\xf5(\\\x97w@' p4415 tp4416 Rp4417 g3933 F306.0 tp4418 a(I9100000 g1 (g5 S'\xe1z\x14\xaeG\xc1w@' p4419 tp4420 Rp4421 g3933 F791.0 tp4422 a(I9110000 g1 (g5 S'\xd7\xa3p=\n\x9bw@' p4423 tp4424 Rp4425 g3933 F424.0 tp4426 a(I9120000 g1 (g5 S'\x1f\x85\xebQ\xb8\x8ew@' p4427 tp4428 Rp4429 g3933 F459.0 tp4430 a(I9130000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb0w@' p4431 tp4432 Rp4433 g3933 F412.0 tp4434 a(I9140000 g1 (g5 S'q=\n\xd7\xa3\xb4w@' p4435 tp4436 Rp4437 g3933 F401.0 tp4438 a(I9150000 g1 (g5 S'{\x14\xaeG\xe1\xbew@' p4439 tp4440 Rp4441 g3933 F420.0 tp4442 a(I9160000 g1 (g5 S'\\\x8f\xc2\xf5(\xe8w@' p4443 tp4444 Rp4445 g3933 F423.0 tp4446 a(I9170000 g1 (g5 S')\\\x8f\xc2\xf5\xdcw@' p4447 tp4448 Rp4449 g3933 F357.0 tp4450 a(I9180000 g1 (g5 S'=\n\xd7\xa3p\x11x@' p4451 tp4452 Rp4453 g3933 F412.0 tp4454 a(I9190000 g1 (g5 S'\x00\x00\x00\x00\x00@x@' p4455 tp4456 Rp4457 g3933 F667.0 tp4458 a(I9200000 g1 (g5 S'fffff\xeaw@' p4459 tp4460 Rp4461 g3933 F92.0 tp4462 a(I9210000 g1 (g5 S'\x00\x00\x00\x00\x00\xf8w@' p4463 tp4464 Rp4465 g3933 F419.0 tp4466 a(I9220000 g1 (g5 S'\x14\xaeG\xe1z@x@' p4467 tp4468 Rp4469 g3933 F342.0 tp4470 a(I9230000 g1 (g5 S'\\\x8f\xc2\xf5(`x@' p4471 tp4472 Rp4473 g3933 F379.0 tp4474 a(I9240000 g1 (g5 S'\xc3\xf5(\\\x8frx@' p4475 tp4476 Rp4477 g1 (g5 S'\xc3\xf5(\\\x8frx@' p4478 tp4479 Rp4480 F434.0 tp4481 a(I9250000 g1 (g5 S'33333wx@' p4482 tp4483 Rp4484 g1 (g5 S'\xd7\xa3p=\n{x@' p4485 tp4486 Rp4487 F387.0 tp4488 a(I9260000 g1 (g5 S'\\\x8f\xc2\xf5(\xacx@' p4489 tp4490 Rp4491 g1 (g5 S'\\\x8f\xc2\xf5(\xacx@' p4492 tp4493 Rp4494 F328.0 tp4495 a(I9270000 g1 (g5 S'33333\xa7x@' p4496 tp4497 Rp4498 g1 (g5 S'\xd7\xa3p=\n\xafx@' p4499 tp4500 Rp4501 F393.0 tp4502 a(I9280000 g1 (g5 S'33333\xcbx@' p4503 tp4504 Rp4505 g1 (g5 S'\\\x8f\xc2\xf5(\xd8x@' p4506 tp4507 Rp4508 F402.0 tp4509 a(I9290000 g1 (g5 S'fffff\xcax@' p4510 tp4511 Rp4512 g4508 F418.0 tp4513 a(I9300000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xbcx@' p4514 tp4515 Rp4516 g4508 F380.0 tp4517 a(I9310000 g1 (g5 S'\n\xd7\xa3p=nx@' p4518 tp4519 Rp4520 g4508 F381.0 tp4521 a(I9320000 g1 (g5 S'33333\x93x@' p4522 tp4523 Rp4524 g4508 F411.0 tp4525 a(I9330000 g1 (g5 S'{\x14\xaeG\xe1\x92x@' p4526 tp4527 Rp4528 g4508 F369.0 tp4529 a(I9340000 g1 (g5 S'\x00\x00\x00\x00\x00\x94x@' p4530 tp4531 Rp4532 g4508 F425.0 tp4533 a(I9350000 g1 (g5 S'H\xe1z\x14\xae\x8bx@' p4534 tp4535 Rp4536 g4508 F398.0 tp4537 a(I9360000 g1 (g5 S'\xe1z\x14\xaeG\x8dx@' p4538 tp4539 Rp4540 g4508 F125.0 tp4541 a(I9370000 g1 (g5 S'{\x14\xaeG\xe1~x@' p4542 tp4543 Rp4544 g4508 F405.0 tp4545 a(I9380000 g1 (g5 S'fffff\x8ax@' p4546 tp4547 Rp4548 g4508 F410.0 tp4549 a(I9390000 g1 (g5 S'\xe1z\x14\xaeGax@' p4550 tp4551 Rp4552 g4508 F421.0 tp4553 a(I9400000 g1 (g5 S'\xe1z\x14\xaeGex@' p4554 tp4555 Rp4556 g4508 F428.0 tp4557 a(I9410000 g1 (g5 S'\n\xd7\xa3p=~x@' p4558 tp4559 Rp4560 g4508 F300.0 tp4561 a(I9420000 g1 (g5 S'\x85\xebQ\xb8\x1e\x81x@' p4562 tp4563 Rp4564 g4508 F420.0 tp4565 a(I9430000 g1 (g5 S'\x85\xebQ\xb8\x1eIx@' p4566 tp4567 Rp4568 g4508 F390.0 tp4569 a(I9440000 g1 (g5 S'\xc3\xf5(\\\x8f:x@' p4570 tp4571 Rp4572 g4508 F369.0 tp4573 a(I9450000 g1 (g5 S'\xd7\xa3p=\n3x@' p4574 tp4575 Rp4576 g4508 F428.0 tp4577 a(I9460000 g1 (g5 S'\xf6(\\\x8f\xc2\rx@' p4578 tp4579 Rp4580 g4508 F417.0 tp4581 a(I9470000 g1 (g5 S'\x1f\x85\xebQ\xb8*x@' p4582 tp4583 Rp4584 g4508 F424.0 tp4585 a(I9480000 g1 (g5 S'\n\xd7\xa3p=.x@' p4586 tp4587 Rp4588 g4508 F437.0 tp4589 a(I9490000 g1 (g5 S'\xa4p=\n\xd7\xa3x@' p4590 tp4591 Rp4592 g4508 F420.0 tp4593 a(I9500000 g1 (g5 S'33333\xb3x@' p4594 tp4595 Rp4596 g4508 F366.0 tp4597 a(I9510000 g1 (g5 S'\x14\xaeG\xe1zTx@' p4598 tp4599 Rp4600 g4508 F131.0 tp4601 a(I9520000 g1 (g5 S'\xaeG\xe1z\x14^x@' p4602 tp4603 Rp4604 g4508 F421.0 tp4605 a(I9530000 g1 (g5 S'\\\x8f\xc2\xf5(,x@' p4606 tp4607 Rp4608 g4508 F415.0 tp4609 a(I9540000 g1 (g5 S'\xa4p=\n\xd7;x@' p4610 tp4611 Rp4612 g4508 F412.0 tp4613 a(I9550000 g1 (g5 S'\xd7\xa3p=\n7x@' p4614 tp4615 Rp4616 g4508 F412.0 tp4617 a(I9560000 g1 (g5 S'\xaeG\xe1z\x14\x1ex@' p4618 tp4619 Rp4620 g4508 F428.0 tp4621 a(I9570000 g1 (g5 S'R\xb8\x1e\x85\xeb)x@' p4622 tp4623 Rp4624 g4508 F297.0 tp4625 a(I9580000 g1 (g5 S'33333gx@' p4626 tp4627 Rp4628 g4508 F810.0 tp4629 a(I9590000 g1 (g5 S'\x14\xaeG\xe1z\xc4x@' p4630 tp4631 Rp4632 g4508 F427.0 tp4633 a(I9600000 g1 (g5 S'\xb8\x1e\x85\xebQ\xc8x@' p4634 tp4635 Rp4636 g4508 F421.0 tp4637 a(I9610000 g1 (g5 S'\x8f\xc2\xf5(\\\xa3x@' p4638 tp4639 Rp4640 g4508 F194.0 tp4641 a(I9620000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xa4x@' p4642 tp4643 Rp4644 g4508 F283.0 tp4645 a(I9630000 g1 (g5 S'\xb8\x1e\x85\xebQ\xa4x@' p4646 tp4647 Rp4648 g4508 F406.0 tp4649 a(I9640000 g1 (g5 S'\\\x8f\xc2\xf5(\xb8x@' p4650 tp4651 Rp4652 g4508 F424.0 tp4653 a(I9650000 g1 (g5 S'33333\xbbx@' p4654 tp4655 Rp4656 g1 (g5 S'\x85\xebQ\xb8\x1e\xe1x@' p4657 tp4658 Rp4659 F375.0 tp4660 a(I9660000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xb4x@' p4661 tp4662 Rp4663 g4659 F331.0 tp4664 a(I9670000 g1 (g5 S'\x1f\x85\xebQ\xb8\xcax@' p4665 tp4666 Rp4667 g4659 F412.0 tp4668 a(I9680000 g1 (g5 S'\xa4p=\n\xd7\xd7x@' p4669 tp4670 Rp4671 g4659 F416.0 tp4672 a(I9690000 g1 (g5 S'\x9a\x99\x99\x99\x99\xcdx@' p4673 tp4674 Rp4675 g4659 F334.0 tp4676 a(I9700000 g1 (g5 S'\xd7\xa3p=\n\xafx@' p4677 tp4678 Rp4679 g4659 F401.0 tp4680 a(I9710000 g1 (g5 S'\xa4p=\n\xd7\xcfx@' p4681 tp4682 Rp4683 g4659 F626.0 tp4684 a(I9713708 g1 (g5 S'\x14\xaeG\xe1z\xb8x@' p4685 tp4686 Rp4687 g4659 F253.0 tp4688 a.(lp0 F4.0 aF3.0 aF0.0 aF3.0 aF1.0 aF1.0 aF0.0 aF0.0 aF3.0 aF1.0 aF0.0 aF4.0 aF2.0 aF3.0 aF1.0 aF1.0 aF3.0 aF2.0 aF2.0 aF1.0 aF7.0 aF2.0 aF4.0 aF2.0 aF0.0 aF4.0 aF1.0 aF1.0 aF2.0 aF1.0 aF1.0 aF1.0 aF2.0 aF5.0 aF1.0 aF2.0 aF1.0 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S'\x00\x00\x00\x00\x00\x00\xfc?' p78 tp79 Rp80 g60 F1.0 tp81 a(I200000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc\xfc?' p82 tp83 Rp84 g60 F6.0 tp85 a(I210000 g1 (g5 S'\n\xd7\xa3p=\n\x03@' p86 tp87 Rp88 g1 (g5 S'\n\xd7\xa3p=\n\x03@' p89 tp90 Rp91 F8.0 tp92 a(I220000 g1 (g5 S'ffffff\x04@' p93 tp94 Rp95 g1 (g5 S'\x8f\xc2\xf5(\\\x8f\x04@' p96 tp97 Rp98 F1.0 tp99 a(I230000 g1 (g5 S'R\xb8\x1e\x85\xebQ\x04@' p100 tp101 Rp102 g1 (g5 S'H\xe1z\x14\xaeG\x05@' p103 tp104 Rp105 F3.0 tp106 a(I240000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc\x04@' p107 tp108 Rp109 g105 F1.0 tp110 a(I250000 g1 (g5 S'333333\x07@' p111 tp112 Rp113 g1 (g5 S'q=\n\xd7\xa3p\x07@' p114 tp115 Rp116 F0.0 tp117 a(I260000 g1 (g5 S'\xc3\xf5(\\\x8f\xc2\t@' p118 tp119 Rp120 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\t@' p121 tp122 Rp123 F4.0 tp124 a(I270000 g1 (g5 S'\xaeG\xe1z\x14\xae\x0b@' p125 tp126 Rp127 g1 (g5 S'R\xb8\x1e\x85\xebQ\x0c@' p128 tp129 Rp130 F0.0 tp131 a(I280000 g1 (g5 S'\\\x8f\xc2\xf5(\\\t@' p132 tp133 Rp134 g130 F1.0 tp135 a(I290000 g1 (g5 S')\\\x8f\xc2\xf5(\x0e@' p136 tp137 Rp138 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc\x0e@' p139 tp140 Rp141 F2.0 tp142 a(I300000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f\x10@' p143 tp144 Rp145 g1 (g5 S'\x8f\xc2\xf5(\\\x8f\x10@' p146 tp147 Rp148 F2.0 tp149 a(I310000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x12@' p150 tp151 Rp152 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x12@' p153 tp154 Rp155 F7.0 tp156 a(I320000 g1 (g5 S'\x85\xebQ\xb8\x1e\x85\x11@' p157 tp158 Rp159 g1 (g5 S'\n\xd7\xa3p=\n\x13@' p160 tp161 Rp162 F2.0 tp163 a(I330000 g1 (g5 S'\\\x8f\xc2\xf5(\\\x13@' p164 tp165 Rp166 g1 (g5 S'\\\x8f\xc2\xf5(\\\x13@' p167 tp168 Rp169 F4.0 tp170 a(I340000 g1 (g5 S'\xd7\xa3p=\n\xd7\x13@' p171 tp172 Rp173 g1 (g5 S'\\\x8f\xc2\xf5(\\\x14@' p174 tp175 Rp176 F2.0 tp177 a(I350000 g1 (g5 S'\xa4p=\n\xd7\xa3\x13@' p178 tp179 Rp180 g176 F9.0 tp181 a(I360000 g1 (g5 S'\\\x8f\xc2\xf5(\\\x12@' p182 tp183 Rp184 g176 F8.0 tp185 a(I370000 g1 (g5 S'{\x14\xaeG\xe1z\x11@' p186 tp187 Rp188 g176 F6.0 tp189 a(I380000 g1 (g5 S'\xaeG\xe1z\x14\xae\x11@' p190 tp191 Rp192 g176 F6.0 tp193 a(I390000 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e\x11@' p194 tp195 Rp196 g176 F5.0 tp197 a(I400000 g1 (g5 S'=\n\xd7\xa3p=\x13@' p198 tp199 Rp200 g176 F2.0 tp201 a(I410000 g1 (g5 S')\\\x8f\xc2\xf5(\x14@' p202 tp203 Rp204 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\x14@' p205 tp206 Rp207 F3.0 tp208 a(I420000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x15@' p209 tp210 Rp211 g1 (g5 S'\xf6(\\\x8f\xc2\xf5\x15@' p212 tp213 Rp214 F8.0 tp215 a(I430000 g1 (g5 S'\xd7\xa3p=\n\xd7\x15@' p216 tp217 Rp218 g1 (g5 S'ffffff\x16@' p219 tp220 Rp221 F3.0 tp222 a(I440000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\x17@' p223 tp224 Rp225 g1 (g5 S'\xc3\xf5(\\\x8f\xc2\x17@' p226 tp227 Rp228 F5.0 tp229 a(I450000 g1 (g5 S'333333\x19@' p230 tp231 Rp232 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc\x19@' p233 tp234 Rp235 F4.0 tp236 a(I460000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc\x19@' p237 tp238 Rp239 g1 (g5 S'R\xb8\x1e\x85\xebQ\x1a@' p240 tp241 Rp242 F3.0 tp243 a(I470000 g1 (g5 S'\x14\xaeG\xe1z\x14\x1b@' p244 tp245 Rp246 g1 (g5 S'\xc3\xf5(\\\x8f\xc2\x1b@' p247 tp248 Rp249 F6.0 tp250 a(I480000 g1 (g5 S'ffffff\x1a@' p251 tp252 Rp253 g249 F9.0 tp254 a(I490000 g1 (g5 S')\\\x8f\xc2\xf5(\x1b@' p255 tp256 Rp257 g249 F7.0 tp258 a(I500000 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e\x1b@' p259 tp260 Rp261 g249 F7.0 tp262 a(I510000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x1a@' p263 tp264 Rp265 g249 F8.0 tp266 a(I520000 g1 (g5 S'\xd7\xa3p=\n\xd7\x19@' p267 tp268 Rp269 g249 F11.0 tp270 a(I530000 g1 (g5 S'q=\n\xd7\xa3p\x1a@' p271 tp272 Rp273 g249 F6.0 tp274 a(I540000 g1 (g5 S'R\xb8\x1e\x85\xebQ\x1b@' p275 tp276 Rp277 g249 F7.0 tp278 a(I550000 g1 (g5 S'R\xb8\x1e\x85\xebQ\x1c@' p279 tp280 Rp281 g1 (g5 S'\\\x8f\xc2\xf5(\\\x1c@' p282 tp283 Rp284 F4.0 tp285 a(I560000 g1 (g5 S'\xd7\xa3p=\n\xd7\x1e@' p286 tp287 Rp288 g1 (g5 S'\xf6(\\\x8f\xc2\xf5\x1e@' p289 tp290 Rp291 F3.0 tp292 a(I570000 g1 (g5 S'H\xe1z\x14\xaeG\x1f@' p293 tp294 Rp295 g1 (g5 S'R\xb8\x1e\x85\xebQ\x1f@' p296 tp297 Rp298 F10.0 tp299 a(I580000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x1f@' p300 tp301 Rp302 g1 (g5 S'\x8f\xc2\xf5(\\\x8f @' p303 tp304 Rp305 F10.0 tp306 a(I590000 g1 (g5 S'H\xe1z\x14\xaeG @' p307 tp308 Rp309 g1 (g5 S'\x9a\x99\x99\x99\x99\x99 @' p310 tp311 Rp312 F6.0 tp313 a(I600000 g1 (g5 S'\xf6(\\\x8f\xc2\xf5 @' p314 tp315 Rp316 g1 (g5 S'H\xe1z\x14\xaeG!@' p317 tp318 Rp319 F19.0 tp320 a(I610000 g1 (g5 S'{\x14\xaeG\xe1z!@' p321 tp322 Rp323 g1 (g5 S'{\x14\xaeG\xe1z!@' p324 tp325 Rp326 F8.0 tp327 a(I620000 g1 (g5 S'\x14\xaeG\xe1z\x14!@' p328 tp329 Rp330 g1 (g5 S'\x9a\x99\x99\x99\x99\x99!@' p331 tp332 Rp333 F17.0 tp334 a(I630000 g1 (g5 S'\xf6(\\\x8f\xc2u"@' p335 tp336 Rp337 g1 (g5 S'\xf6(\\\x8f\xc2u"@' p338 tp339 Rp340 F9.0 tp341 a(I640000 g1 (g5 S')\\\x8f\xc2\xf5("@' p342 tp343 Rp344 g1 (g5 S')\\\x8f\xc2\xf5\xa8"@' p345 tp346 Rp347 F5.0 tp348 a(I650000 g1 (g5 S'\x00\x00\x00\x00\x00\x00"@' p349 tp350 Rp351 g347 F10.0 tp352 a(I660000 g1 (g5 S'\n\xd7\xa3p=\n"@' p353 tp354 Rp355 g347 F7.0 tp356 a(I670000 g1 (g5 S'\\\x8f\xc2\xf5(\xdc!@' p357 tp358 Rp359 g347 F5.0 tp360 a(I680000 g1 (g5 S'\xcd\xcc\xcc\xcc\xccL"@' p361 tp362 Rp363 g347 F17.0 tp364 a(I690000 g1 (g5 S'\x14\xaeG\xe1z\x14#@' p365 tp366 Rp367 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e#@' p368 tp369 Rp370 F9.0 tp371 a(I700000 g1 (g5 S'\xaeG\xe1z\x14.$@' p372 tp373 Rp374 g1 (g5 S'\xb8\x1e\x85\xebQ8$@' p375 tp376 Rp377 F8.0 tp378 a(I710000 g1 (g5 S')\\\x8f\xc2\xf5(%@' p379 tp380 Rp381 g1 (g5 S')\\\x8f\xc2\xf5(%@' p382 tp383 Rp384 F20.0 tp385 a(I720000 g1 (g5 S'\x14\xaeG\xe1z\x94&@' p386 tp387 Rp388 g1 (g5 S'\x14\xaeG\xe1z\x94&@' p389 tp390 Rp391 F15.0 tp392 a(I730000 g1 (g5 S"\xa4p=\n\xd7\xa3'@" p393 tp394 Rp395 g1 (g5 S"\xa4p=\n\xd7\xa3'@" p396 tp397 Rp398 F12.0 tp399 a(I740000 g1 (g5 S'\xa4p=\n\xd7#)@' p400 tp401 Rp402 g1 (g5 S'\xa4p=\n\xd7#)@' p403 tp404 Rp405 F9.0 tp406 a(I750000 g1 (g5 S'\xd7\xa3p=\n\xd7)@' p407 tp408 Rp409 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb)@' p410 tp411 Rp412 F7.0 tp413 a(I760000 g1 (g5 S'\xb8\x1e\x85\xebQ8+@' p414 tp415 Rp416 g1 (g5 S'\xb8\x1e\x85\xebQ8+@' p417 tp418 Rp419 F17.0 tp420 a(I770000 g1 (g5 S'333333,@' p421 tp422 Rp423 g1 (g5 S'333333,@' p424 tp425 Rp426 F13.0 tp427 a(I780000 g1 (g5 S'\x85\xebQ\xb8\x1e\x05-@' p428 tp429 Rp430 g1 (g5 S'\x85\xebQ\xb8\x1e\x05-@' p431 tp432 Rp433 F17.0 tp434 a(I790000 g1 (g5 S'\xecQ\xb8\x1e\x85k-@' p435 tp436 Rp437 g1 (g5 S'\x00\x00\x00\x00\x00\x80-@' p438 tp439 Rp440 F12.0 tp441 a(I800000 g1 (g5 S'33333\xb3-@' p442 tp443 Rp444 g1 (g5 S'33333\xb3-@' p445 tp446 Rp447 F16.0 tp448 a(I810000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb.@' p449 tp450 Rp451 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb.@' p452 tp453 Rp454 F21.0 tp455 a(I820000 g1 (g5 S'\x8f\xc2\xf5(\\\x0f0@' p456 tp457 Rp458 g1 (g5 S'\x8f\xc2\xf5(\\\x0f0@' p459 tp460 Rp461 F16.0 tp462 a(I830000 g1 (g5 S'3333330@' p463 tp464 Rp465 g1 (g5 S'\n\xd7\xa3p=J0@' p466 tp467 Rp468 F16.0 tp469 a(I840000 g1 (g5 S'\x00\x00\x00\x00\x00\x001@' p470 tp471 Rp472 g1 (g5 S'\x00\x00\x00\x00\x00\x001@' p473 tp474 Rp475 F24.0 tp476 a(I850000 g1 (g5 S'\xaeG\xe1z\x14\xee1@' p477 tp478 Rp479 g1 (g5 S'\xaeG\xe1z\x14\xee1@' p480 tp481 Rp482 F32.0 tp483 a(I860000 g1 (g5 S'H\xe1z\x14\xaeG2@' p484 tp485 Rp486 g1 (g5 S'33333s2@' p487 tp488 Rp489 F19.0 tp490 a(I870000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f2@' p491 tp492 Rp493 g1 (g5 S'\x8f\xc2\xf5(\\\x8f2@' p494 tp495 Rp496 F30.0 tp497 a(I880000 g1 (g5 S'\\\x8f\xc2\xf5(\xdc2@' p498 tp499 Rp500 g1 (g5 S'\\\x8f\xc2\xf5(\xdc2@' p501 tp502 Rp503 F29.0 tp504 a(I890000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f3@' p505 tp506 Rp507 g1 (g5 S'\x8f\xc2\xf5(\\\x8f3@' p508 tp509 Rp510 F28.0 tp511 a(I900000 g1 (g5 S'\xecQ\xb8\x1e\x85+4@' p512 tp513 Rp514 g1 (g5 S'\x85\xebQ\xb8\x1eE4@' p515 tp516 Rp517 F6.0 tp518 a(I910000 g1 (g5 S'\xc3\xf5(\\\x8f\x025@' p519 tp520 Rp521 g1 (g5 S'\xc3\xf5(\\\x8f\x025@' p522 tp523 Rp524 F33.0 tp525 a(I920000 g1 (g5 S'\xd7\xa3p=\n\x975@' p526 tp527 Rp528 g1 (g5 S'\xd7\xa3p=\n\x975@' p529 tp530 Rp531 F18.0 tp532 a(I930000 g1 (g5 S'{\x14\xaeG\xe1\xfa5@' p533 tp534 Rp535 g1 (g5 S'\xd7\xa3p=\n\x176@' p536 tp537 Rp538 F17.0 tp539 a(I940000 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e7@' p540 tp541 Rp542 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e7@' p543 tp544 Rp545 F26.0 tp546 a(I950000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab9@' p547 tp548 Rp549 g1 (g5 S'\xecQ\xb8\x1e\x85\xab9@' p550 tp551 Rp552 F24.0 tp553 a(I960000 g1 (g5 S')\\\x8f\xc2\xf5(;@' p554 tp555 Rp556 g1 (g5 S')\\\x8f\xc2\xf5(;@' p557 tp558 Rp559 F77.0 tp560 a(I970000 g1 (g5 S'\xe1z\x14\xaeG\xe1;@' p561 tp562 Rp563 g1 (g5 S'\xe1z\x14\xaeG\xe1;@' p564 tp565 Rp566 F27.0 tp567 a(I980000 g1 (g5 S'\xd7\xa3p=\n\x97<@' p568 tp569 Rp570 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e<@' p571 tp572 Rp573 F23.0 tp574 a(I990000 g1 (g5 S'ffffff>@' p575 tp576 Rp577 g1 (g5 S'ffffff>@' p578 tp579 Rp580 F42.0 tp581 a(I1000000 g1 (g5 S'\xecQ\xb8\x1e\x85k?@' p582 tp583 Rp584 g1 (g5 S'\xecQ\xb8\x1e\x85k?@' p585 tp586 Rp587 F37.0 tp588 a(I1010000 g1 (g5 S'\xc3\xf5(\\\x8fB@@' p589 tp590 Rp591 g1 (g5 S'\x85\xebQ\xb8\x1eE@@' p592 tp593 Rp594 F20.0 tp595 a(I1020000 g1 (g5 S'\xaeG\xe1z\x14\x8e@@' p596 tp597 Rp598 g1 (g5 S'\xaeG\xe1z\x14\x8e@@' p599 tp600 Rp601 F37.0 tp602 a(I1030000 g1 (g5 S'\xb8\x1e\x85\xebQxA@' p603 tp604 Rp605 g1 (g5 S'\xb8\x1e\x85\xebQxA@' p606 tp607 Rp608 F58.0 tp609 a(I1040000 g1 (g5 S'H\xe1z\x14\xae\xe7A@' p610 tp611 Rp612 g1 (g5 S'\xaeG\xe1z\x14\xeeA@' p613 tp614 Rp615 F34.0 tp616 a(I1050000 g1 (g5 S'\x8f\xc2\xf5(\\\xefA@' p617 tp618 Rp619 g1 (g5 S'\xaeG\xe1z\x14\x0eB@' p620 tp621 Rp622 F36.0 tp623 a(I1060000 g1 (g5 S'33333\x93B@' p624 tp625 Rp626 g1 (g5 S'33333\x93B@' p627 tp628 Rp629 F25.0 tp630 a(I1070000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x0cC@' p631 tp632 Rp633 g1 (g5 S'\\\x8f\xc2\xf5(@' p968 tp969 Rp970 g914 F1.0 tp971 a(I1660000 g1 (g5 S'\\\x8f\xc2\xf5(\xdc-@' p972 tp973 Rp974 g914 F11.0 tp975 a(I1670000 g1 (g5 S"\xcd\xcc\xcc\xcc\xccL'@" p976 tp977 Rp978 g914 F49.0 tp979 a(I1680000 g1 (g5 S"\x14\xaeG\xe1z\x94'@" p980 tp981 Rp982 g914 F68.0 tp983 a(I1690000 g1 (g5 S'ffffff,@' p984 tp985 Rp986 g914 F3.0 tp987 a(I1700000 g1 (g5 S'\xecQ\xb8\x1e\x85k1@' p988 tp989 Rp990 g914 F69.0 tp991 a(I1710000 g1 (g5 S'=\n\xd7\xa3p\xbd4@' p992 tp993 Rp994 g914 F24.0 tp995 a(I1720000 g1 (g5 S'\n\xd7\xa3p=J9@' p996 tp997 Rp998 g914 F52.0 tp999 a(I1730000 g1 (g5 S'\xe1z\x14\xaeGa>@' p1000 tp1001 Rp1002 g914 F84.0 tp1003 a(I1740000 g1 (g5 S'\x1f\x85\xebQ\xb8~A@' p1004 tp1005 Rp1006 g914 F57.0 tp1007 a(I1750000 g1 (g5 S'fffff\xc6C@' p1008 tp1009 Rp1010 g914 F87.0 tp1011 a(I1760000 g1 (g5 S'\n\xd7\xa3p=\x8aE@' p1012 tp1013 Rp1014 g914 F32.0 tp1015 a(I1770000 g1 (g5 S'\xb8\x1e\x85\xebQ8A@' p1016 tp1017 Rp1018 g914 F59.0 tp1019 a(I1780000 g1 (g5 S'\x1f\x85\xebQ\xb8^C@' p1020 tp1021 Rp1022 g914 F59.0 tp1023 a(I1790000 g1 (g5 S'\xa4p=\n\xd7CD@' p1024 tp1025 Rp1026 g914 F71.0 tp1027 a(I1800000 g1 (g5 S'\x14\xaeG\xe1z\xf4@@' p1028 tp1029 Rp1030 g914 F2.0 tp1031 a(I1810000 g1 (g5 S')\\\x8f\xc2\xf5\xe85@' p1032 tp1033 Rp1034 g914 F46.0 tp1035 a(I1820000 g1 (g5 S'{\x14\xaeG\xe1z8@' p1036 tp1037 Rp1038 g914 F52.0 tp1039 a(I1830000 g1 (g5 S'ffffff<@' p1040 tp1041 Rp1042 g914 F52.0 tp1043 a(I1840000 g1 (g5 S'ffffff@@' p1044 tp1045 Rp1046 g914 F62.0 tp1047 a(I1850000 g1 (g5 S'\xe1z\x14\xaeGa?@' p1048 tp1049 Rp1050 g914 F73.0 tp1051 a(I1860000 g1 (g5 S'{\x14\xaeG\xe1:?@' p1052 tp1053 Rp1054 g914 F104.0 tp1055 a(I1870000 g1 (g5 S'R\xb8\x1e\x85\xebQ?@' p1056 tp1057 Rp1058 g914 F20.0 tp1059 a(I1880000 g1 (g5 S'\x00\x00\x00\x00\x00 B@' p1060 tp1061 Rp1062 g914 F89.0 tp1063 a(I1890000 g1 (g5 S'=\n\xd7\xa3p}D@' p1064 tp1065 Rp1066 g914 F55.0 tp1067 a(I1900000 g1 (g5 S')\\\x8f\xc2\xf5\x08G@' p1068 tp1069 Rp1070 g914 F48.0 tp1071 a(I1910000 g1 (g5 S')\\\x8f\xc2\xf5hI@' p1072 tp1073 Rp1074 g914 F41.0 tp1075 a(I1920000 g1 (g5 S'\x1f\x85\xebQ\xb8\xdeJ@' p1076 tp1077 Rp1078 g914 F51.0 tp1079 a(I1930000 g1 (g5 S'\xecQ\xb8\x1e\x85\xebH@' p1080 tp1081 Rp1082 g914 F5.0 tp1083 a(I1940000 g1 (g5 S'R\xb8\x1e\x85\xeb\xb1J@' p1084 tp1085 Rp1086 g914 F93.0 tp1087 a(I1950000 g1 (g5 S'{\x14\xaeG\xe1\xdaK@' p1088 tp1089 Rp1090 g914 F67.0 tp1091 a(I1960000 g1 (g5 S'q=\n\xd7\xa3\xd0K@' p1092 tp1093 Rp1094 g914 F4.0 tp1095 a(I1970000 g1 (g5 S'\x1f\x85\xebQ\xb8\xfeI@' p1096 tp1097 Rp1098 g914 F18.0 tp1099 a(I1980000 g1 (g5 S'=\n\xd7\xa3p=J@' p1100 tp1101 Rp1102 g914 F86.0 tp1103 a(I1990000 g1 (g5 S'=\n\xd7\xa3p\xbdG@' p1104 tp1105 Rp1106 g914 F49.0 tp1107 a(I2000000 g1 (g5 S'\xaeG\xe1z\x14.G@' p1108 tp1109 Rp1110 g914 F23.0 tp1111 a(I2010000 g1 (g5 S'\xecQ\xb8\x1e\x85kF@' p1112 tp1113 Rp1114 g914 F51.0 tp1115 a(I2020000 g1 (g5 S'H\xe1z\x14\xae\x07F@' p1116 tp1117 Rp1118 g914 F103.0 tp1119 a(I2030000 g1 (g5 S'\xa4p=\n\xd7CF@' p1120 tp1121 Rp1122 g914 F63.0 tp1123 a(I2040000 g1 (g5 S'\x00\x00\x00\x00\x00\x00E@' p1124 tp1125 Rp1126 g914 F10.0 tp1127 a(I2050000 g1 (g5 S'\xc3\xf5(\\\x8f"D@' p1128 tp1129 Rp1130 g914 F79.0 tp1131 a(I2060000 g1 (g5 S')\\\x8f\xc2\xf5hF@' p1132 tp1133 Rp1134 g914 F68.0 tp1135 a(I2070000 g1 (g5 S'\xf6(\\\x8f\xc2UG@' p1136 tp1137 Rp1138 g914 F1.0 tp1139 a(I2080000 g1 (g5 S'\xecQ\xb8\x1e\x85\xcbG@' p1140 tp1141 Rp1142 g914 F13.0 tp1143 a(I2090000 g1 (g5 S'{\x14\xaeG\xe1ZE@' p1144 tp1145 Rp1146 g914 F41.0 tp1147 a(I2100000 g1 (g5 S'H\xe1z\x14\xaegE@' p1148 tp1149 Rp1150 g914 F96.0 tp1151 a(I2110000 g1 (g5 S"H\xe1z\x14\xae'G@" p1152 tp1153 Rp1154 g914 F59.0 tp1155 a(I2120000 g1 (g5 S'{\x14\xaeG\xe1zG@' p1156 tp1157 Rp1158 g914 F47.0 tp1159 a(I2130000 g1 (g5 S'\x85\xebQ\xb8\x1e\xa5F@' p1160 tp1161 Rp1162 g914 F95.0 tp1163 a(I2140000 g1 (g5 S'\xa4p=\n\xd7cG@' p1164 tp1165 Rp1166 g914 F64.0 tp1167 a(I2150000 g1 (g5 S'\xf6(\\\x8f\xc2\x95F@' p1168 tp1169 Rp1170 g914 F126.0 tp1171 a(I2160000 g1 (g5 S'\x1f\x85\xebQ\xb8~E@' p1172 tp1173 Rp1174 g914 F63.0 tp1175 a(I2170000 g1 (g5 S'\xecQ\xb8\x1e\x85KE@' p1176 tp1177 Rp1178 g914 F56.0 tp1179 a(I2180000 g1 (g5 S'\xecQ\xb8\x1e\x85\x8bG@' p1180 tp1181 Rp1182 g914 F77.0 tp1183 a(I2190000 g1 (g5 S'H\xe1z\x14\xae\xc7J@' p1184 tp1185 Rp1186 g914 F50.0 tp1187 a(I2200000 g1 (g5 S'\xd7\xa3p=\nwL@' p1188 tp1189 Rp1190 g914 F94.0 tp1191 a(I2210000 g1 (g5 S'\x1f\x85\xebQ\xb8\x9eL@' p1192 tp1193 Rp1194 g914 F53.0 tp1195 a(I2220000 g1 (g5 S'H\xe1z\x14\xae\xc7K@' p1196 tp1197 Rp1198 g914 F47.0 tp1199 a(I2230000 g1 (g5 S'33333\x13K@' p1200 tp1201 Rp1202 g914 F62.0 tp1203 a(I2240000 g1 (g5 S'\xc3\xf5(\\\x8fBK@' p1204 tp1205 Rp1206 g914 F73.0 tp1207 a(I2250000 g1 (g5 S'\x1f\x85\xebQ\xb8^J@' p1208 tp1209 Rp1210 g914 F83.0 tp1211 a(I2260000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x0cK@' p1212 tp1213 Rp1214 g914 F53.0 tp1215 a(I2270000 g1 (g5 S'fffff\x86L@' p1216 tp1217 Rp1218 g914 F85.0 tp1219 a(I2280000 g1 (g5 S'\xe1z\x14\xaeG\xc1L@' p1220 tp1221 Rp1222 g914 F74.0 tp1223 a(I2290000 g1 (g5 S'\x14\xaeG\xe1z\x94I@' p1224 tp1225 Rp1226 g914 F47.0 tp1227 a(I2300000 g1 (g5 S'\xe1z\x14\xaeG!I@' p1228 tp1229 Rp1230 g914 F94.0 tp1231 a(I2310000 g1 (g5 S'\xe1z\x14\xaeGaJ@' p1232 tp1233 Rp1234 g914 F127.0 tp1235 a(I2320000 g1 (g5 S'q=\n\xd7\xa3\x10K@' p1236 tp1237 Rp1238 g914 F71.0 tp1239 a(I2330000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc,L@' p1240 tp1241 Rp1242 g914 F54.0 tp1243 a(I2340000 g1 (g5 S'=\n\xd7\xa3p\xbdN@' p1244 tp1245 Rp1246 g914 F67.0 tp1247 a(I2350000 g1 (g5 S'ffffffO@' p1248 tp1249 Rp1250 g914 F73.0 tp1251 a(I2360000 g1 (g5 S'\xecQ\xb8\x1e\x85{P@' p1252 tp1253 Rp1254 g914 F26.0 tp1255 a(I2370000 g1 (g5 S'\xe1z\x14\xaeGAP@' p1256 tp1257 Rp1258 g914 F88.0 tp1259 a(I2380000 g1 (g5 S'\xc3\xf5(\\\x8f2P@' p1260 tp1261 Rp1262 g914 F7.0 tp1263 a(I2390000 g1 (g5 S'\xb8\x1e\x85\xebQ8P@' p1264 tp1265 Rp1266 g914 F57.0 tp1267 a(I2400000 g1 (g5 S'\x14\xaeG\xe1z\xc4P@' p1268 tp1269 Rp1270 g914 F34.0 tp1271 a(I2410000 g1 (g5 S'\x8f\xc2\xf5(\\_Q@' p1272 tp1273 Rp1274 g1 (g5 S'{\x14\xaeG\xe1\x8aQ@' p1275 tp1276 Rp1277 F3.0 tp1278 a(I2420000 g1 (g5 S'H\xe1z\x14\xaegQ@' p1279 tp1280 Rp1281 g1277 F68.0 tp1282 a(I2430000 g1 (g5 S'\xecQ\xb8\x1e\x85\x0bR@' p1283 tp1284 Rp1285 g1 (g5 S'\xecQ\xb8\x1e\x85\x0bR@' p1286 tp1287 Rp1288 F283.0 tp1289 a(I2440000 g1 (g5 S'R\xb8\x1e\x85\xeb\xe1Q@' p1290 tp1291 Rp1292 g1288 F102.0 tp1293 a(I2450000 g1 (g5 S')\\\x8f\xc2\xf5\x88Q@' p1294 tp1295 Rp1296 g1288 F117.0 tp1297 a(I2460000 g1 (g5 S'\x1f\x85\xebQ\xb8\xdeP@' p1298 tp1299 Rp1300 g1288 F49.0 tp1301 a(I2470000 g1 (g5 S'q=\n\xd7\xa3\x90O@' p1302 tp1303 Rp1304 g1288 F40.0 tp1305 a(I2480000 g1 (g5 S'\xa4p=\n\xd7\x13P@' p1306 tp1307 Rp1308 g1288 F20.0 tp1309 a(I2490000 g1 (g5 S'\x14\xaeG\xe1zTQ@' p1310 tp1311 Rp1312 g1288 F114.0 tp1313 a(I2500000 g1 (g5 S'\xe1z\x14\xaeG1R@' p1314 tp1315 Rp1316 g1 (g5 S'\xe1z\x14\xaeG1R@' p1317 tp1318 Rp1319 F44.0 tp1320 a(I2510000 g1 (g5 S'=\n\xd7\xa3p}R@' p1321 tp1322 Rp1323 g1 (g5 S'\n\xd7\xa3p=\xbaR@' p1324 tp1325 Rp1326 F88.0 tp1327 a(I2520000 g1 (g5 S'\xa4p=\n\xd7\xd3R@' p1328 tp1329 Rp1330 g1 (g5 S'H\xe1z\x14\xae\xd7R@' p1331 tp1332 Rp1333 F65.0 tp1334 a(I2530000 g1 (g5 S'H\xe1z\x14\xae\xa7R@' p1335 tp1336 Rp1337 g1 (g5 S'\n\xd7\xa3p=\xdaR@' p1338 tp1339 Rp1340 F100.0 tp1341 a(I2540000 g1 (g5 S'\n\xd7\xa3p=jS@' p1342 tp1343 Rp1344 g1 (g5 S'\n\xd7\xa3p=jS@' p1345 tp1346 Rp1347 F87.0 tp1348 a(I2550000 g1 (g5 S'H\xe1z\x14\xae\x87S@' p1349 tp1350 Rp1351 g1 (g5 S'=\n\xd7\xa3p\x8dS@' p1352 tp1353 Rp1354 F71.0 tp1355 a(I2560000 g1 (g5 S'\x00\x00\x00\x00\x00\x00S@' p1356 tp1357 Rp1358 g1 (g5 S'\xc3\xf5(\\\x8f\x92S@' p1359 tp1360 Rp1361 F36.0 tp1362 a(I2570000 g1 (g5 S'H\xe1z\x14\xaeGR@' p1363 tp1364 Rp1365 g1361 F65.0 tp1366 a(I2580000 g1 (g5 S'\x14\xaeG\xe1z$R@' p1367 tp1368 Rp1369 g1361 F104.0 tp1370 a(I2590000 g1 (g5 S'\x00\x00\x00\x00\x00PR@' p1371 tp1372 Rp1373 g1361 F62.0 tp1374 a(I2600000 g1 (g5 S'\x00\x00\x00\x00\x00@S@' p1375 tp1376 Rp1377 g1361 F100.0 tp1378 a(I2610000 g1 (g5 S'\x00\x00\x00\x00\x00\xa0S@' p1379 tp1380 Rp1381 g1 (g5 S'\xc3\xf5(\\\x8f\xb2S@' p1382 tp1383 Rp1384 F51.0 tp1385 a(I2620000 g1 (g5 S'\x9a\x99\x99\x99\x99\xb9R@' p1386 tp1387 Rp1388 g1 (g5 S'\x9a\x99\x99\x99\x99\xb9S@' p1389 tp1390 Rp1391 F111.0 tp1392 a(I2630000 g1 (g5 S'\xaeG\xe1z\x14\xbeR@' p1393 tp1394 Rp1395 g1391 F35.0 tp1396 a(I2640000 g1 (g5 S'\xecQ\xb8\x1e\x85KR@' p1397 tp1398 Rp1399 g1391 F41.0 tp1400 a(I2650000 g1 (g5 S'=\n\xd7\xa3p\x8dR@' p1401 tp1402 Rp1403 g1391 F35.0 tp1404 a(I2660000 g1 (g5 S'\xc3\xf5(\\\x8f\xf2Q@' p1405 tp1406 Rp1407 g1391 F91.0 tp1408 a(I2670000 g1 (g5 S'q=\n\xd7\xa3\xc0R@' p1409 tp1410 Rp1411 g1391 F137.0 tp1412 a(I2680000 g1 (g5 S'\x9a\x99\x99\x99\x99\xd9S@' p1413 tp1414 Rp1415 g1 (g5 S'\\\x8f\xc2\xf5(\xecS@' p1416 tp1417 Rp1418 F82.0 tp1419 a(I2690000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\\T@' p1420 tp1421 Rp1422 g1 (g5 S'\xe1z\x14\xaeGqT@' p1423 tp1424 Rp1425 F37.0 tp1426 a(I2700000 g1 (g5 S'\xc3\xf5(\\\x8f\xc2T@' p1427 tp1428 Rp1429 g1 (g5 S'\x8f\xc2\xf5(\\\xffT@' p1430 tp1431 Rp1432 F54.0 tp1433 a(I2710000 g1 (g5 S'fffff\x16U@' p1434 tp1435 Rp1436 g1 (g5 S'\xb8\x1e\x85\xebQ8U@' p1437 tp1438 Rp1439 F45.0 tp1440 a(I2720000 g1 (g5 S'\xf6(\\\x8f\xc2\x05U@' p1441 tp1442 Rp1443 g1439 F72.0 tp1444 a(I2730000 g1 (g5 S'\xecQ\xb8\x1e\x85\x8bU@' p1445 tp1446 Rp1447 g1 (g5 S'\xaeG\xe1z\x14\x9eU@' p1448 tp1449 Rp1450 F124.0 tp1451 a(I2740000 g1 (g5 S'333333U@' p1452 tp1453 Rp1454 g1450 F126.0 tp1455 a(I2750000 g1 (g5 S'q=\n\xd7\xa3@U@' p1456 tp1457 Rp1458 g1 (g5 S'\xb8\x1e\x85\xebQ\xc8U@' p1459 tp1460 Rp1461 F91.0 tp1462 a(I2760000 g1 (g5 S'{\x14\xaeG\xe1:V@' p1463 tp1464 Rp1465 g1 (g5 S'{\x14\xaeG\xe1:V@' p1466 tp1467 Rp1468 F122.0 tp1469 a(I2770000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcclV@' p1470 tp1471 Rp1472 g1 (g5 S'\xc3\xf5(\\\x8frV@' p1473 tp1474 Rp1475 F82.0 tp1476 a(I2780000 g1 (g5 S'\xc3\xf5(\\\x8f\xc2V@' p1477 tp1478 Rp1479 g1 (g5 S'\xc3\xf5(\\\x8f\xc2V@' p1480 tp1481 Rp1482 F120.0 tp1483 a(I2790000 g1 (g5 S'H\xe1z\x14\xae\xb7W@' p1484 tp1485 Rp1486 g1 (g5 S'H\xe1z\x14\xae\xb7W@' p1487 tp1488 Rp1489 F86.0 tp1490 a(I2800000 g1 (g5 S'\xa4p=\n\xd7\xc3W@' p1491 tp1492 Rp1493 g1 (g5 S'\x1f\x85\xebQ\xb8\xceW@' p1494 tp1495 Rp1496 F137.0 tp1497 a(I2810000 g1 (g5 S'\xecQ\xb8\x1e\x85\x1bW@' p1498 tp1499 Rp1500 g1496 F227.0 tp1501 a(I2820000 g1 (g5 S'\n\xd7\xa3p=\xaaV@' p1502 tp1503 Rp1504 g1496 F35.0 tp1505 a(I2830000 g1 (g5 S'\\\x8f\xc2\xf5(,V@' p1506 tp1507 Rp1508 g1496 F115.0 tp1509 a(I2840000 g1 (g5 S'fffff\xf6V@' p1510 tp1511 Rp1512 g1496 F177.0 tp1513 a(I2850000 g1 (g5 S'\xb8\x1e\x85\xebQ\x08W@' p1514 tp1515 Rp1516 g1496 F235.0 tp1517 a(I2860000 g1 (g5 S'\xa4p=\n\xd7#V@' p1518 tp1519 Rp1520 g1496 F30.0 tp1521 a(I2870000 g1 (g5 S'\xd7\xa3p=\n\x17V@' p1522 tp1523 Rp1524 g1496 F110.0 tp1525 a(I2880000 g1 (g5 S'\n\xd7\xa3p=\x8aT@' p1526 tp1527 Rp1528 g1496 F72.0 tp1529 a(I2890000 g1 (g5 S'\xc3\xf5(\\\x8f\xf2T@' p1530 tp1531 Rp1532 g1496 F80.0 tp1533 a(I2900000 g1 (g5 S"H\xe1z\x14\xae'V@" p1534 tp1535 Rp1536 g1496 F139.0 tp1537 a(I2910000 g1 (g5 S'\x85\xebQ\xb8\x1e\x15V@' p1538 tp1539 Rp1540 g1496 F81.0 tp1541 a(I2920000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xdcU@' p1542 tp1543 Rp1544 g1496 F113.0 tp1545 a(I2930000 g1 (g5 S'\xe1z\x14\xaeGAV@' p1546 tp1547 Rp1548 g1496 F100.0 tp1549 a(I2940000 g1 (g5 S'\xa4p=\n\xd7\x03V@' p1550 tp1551 Rp1552 g1496 F116.0 tp1553 a(I2950000 g1 (g5 S'fffff&W@' p1554 tp1555 Rp1556 g1496 F177.0 tp1557 a(I2960000 g1 (g5 S'\xb8\x1e\x85\xebQxW@' p1558 tp1559 Rp1560 g1496 F88.0 tp1561 a(I2970000 g1 (g5 S'\xe1z\x14\xaeGQW@' p1562 tp1563 Rp1564 g1 (g5 S'\xc3\xf5(\\\x8f\xd2W@' p1565 tp1566 Rp1567 F116.0 tp1568 a(I2980000 g1 (g5 S'\n\xd7\xa3p=\xbaV@' p1569 tp1570 Rp1571 g1567 F148.0 tp1572 a(I2990000 g1 (g5 S'\xd7\xa3p=\ngW@' p1573 tp1574 Rp1575 g1567 F38.0 tp1576 a(I3000000 g1 (g5 S'\\\x8f\xc2\xf5(\x8cW@' p1577 tp1578 Rp1579 g1 (g5 S'\xecQ\xb8\x1e\x85\xebW@' p1580 tp1581 Rp1582 F73.0 tp1583 a(I3010000 g1 (g5 S'fffff\xa6W@' p1584 tp1585 Rp1586 g1582 F93.0 tp1587 a(I3020000 g1 (g5 S'33333sX@' p1588 tp1589 Rp1590 g1 (g5 S'\x1f\x85\xebQ\xb8\x9eX@' p1591 tp1592 Rp1593 F49.0 tp1594 a(I3030000 g1 (g5 S'\x00\x00\x00\x00\x00\xf0X@' p1595 tp1596 Rp1597 g1 (g5 S'\x00\x00\x00\x00\x00\xf0X@' p1598 tp1599 Rp1600 F81.0 tp1601 a(I3040000 g1 (g5 S'\x9a\x99\x99\x99\x99\xe9W@' p1602 tp1603 Rp1604 g1 (g5 S'33333SY@' p1605 tp1606 Rp1607 F10.0 tp1608 a(I3050000 g1 (g5 S'\x1f\x85\xebQ\xb8^X@' p1609 tp1610 Rp1611 g1607 F58.0 tp1612 a(I3060000 g1 (g5 S'\x1f\x85\xebQ\xb8\x9eW@' p1613 tp1614 Rp1615 g1607 F163.0 tp1616 a(I3070000 g1 (g5 S'\x8f\xc2\xf5(\\\x7fW@' p1617 tp1618 Rp1619 g1607 F9.0 tp1620 a(I3080000 g1 (g5 S'\xf6(\\\x8f\xc2\x95X@' p1621 tp1622 Rp1623 g1607 F153.0 tp1624 a(I3090000 g1 (g5 S'R\xb8\x1e\x85\xeb!Y@' p1625 tp1626 Rp1627 g1607 F122.0 tp1628 a(I3100000 g1 (g5 S'33333\xd3Y@' p1629 tp1630 Rp1631 g1 (g5 S'33333\xd3Y@' p1632 tp1633 Rp1634 F283.0 tp1635 a(I3110000 g1 (g5 S'H\xe1z\x14\xae\xe7Z@' p1636 tp1637 Rp1638 g1 (g5 S'q=\n\xd7\xa3\xf0Z@' p1639 tp1640 Rp1641 F83.0 tp1642 a(I3120000 g1 (g5 S'33333\xd3Z@' p1643 tp1644 Rp1645 g1 (g5 S'\x9a\x99\x99\x99\x99\t[@' p1646 tp1647 Rp1648 F61.0 tp1649 a(I3130000 g1 (g5 S'\xa4p=\n\xd7\xf3[@' p1650 tp1651 Rp1652 g1 (g5 S'\xa4p=\n\xd7\xf3[@' p1653 tp1654 Rp1655 F45.0 tp1656 a(I3140000 g1 (g5 S'\xaeG\xe1z\x14\xee[@' p1657 tp1658 Rp1659 g1 (g5 S'H\xe1z\x14\xaeW\\@' p1660 tp1661 Rp1662 F97.0 tp1663 a(I3150000 g1 (g5 S'\xecQ\xb8\x1e\x85\xdb\\@' p1664 tp1665 Rp1666 g1 (g5 S'\xecQ\xb8\x1e\x85\xdb\\@' p1667 tp1668 Rp1669 F187.0 tp1670 a(I3160000 g1 (g5 S'\xd7\xa3p=\nW^@' p1671 tp1672 Rp1673 g1 (g5 S'\xd7\xa3p=\nW^@' p1674 tp1675 Rp1676 F174.0 tp1677 a(I3170000 g1 (g5 S'fffff\xb6^@' p1678 tp1679 Rp1680 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8^@' p1681 tp1682 Rp1683 F109.0 tp1684 a(I3180000 g1 (g5 S'{\x14\xaeG\xe1\n_@' p1685 tp1686 Rp1687 g1 (g5 S'\xecQ\xb8\x1e\x85K_@' p1688 tp1689 Rp1690 F128.0 tp1691 a(I3190000 g1 (g5 S'=\n\xd7\xa3p\x9d^@' p1692 tp1693 Rp1694 g1 (g5 S'\xd7\xa3p=\n\x87_@' p1695 tp1696 Rp1697 F16.0 tp1698 a(I3200000 g1 (g5 S'\x85\xebQ\xb8\x1e\xa5^@' p1699 tp1700 Rp1701 g1697 F77.0 tp1702 a(I3210000 g1 (g5 S'\\\x8f\xc2\xf5(\xbc^@' p1703 tp1704 Rp1705 g1697 F126.0 tp1706 a(I3220000 g1 (g5 S')\\\x8f\xc2\xf5H_@' p1707 tp1708 Rp1709 g1697 F143.0 tp1710 a(I3230000 g1 (g5 S'\xe1z\x14\xaeGa_@' p1711 tp1712 Rp1713 g1697 F236.0 tp1714 a(I3240000 g1 (g5 S'q=\n\xd7\xa3\x90^@' p1715 tp1716 Rp1717 g1697 F193.0 tp1718 a(I3250000 g1 (g5 S'\x00\x00\x00\x00\x00@^@' p1719 tp1720 Rp1721 g1697 F63.0 tp1722 a(I3260000 g1 (g5 S'q=\n\xd7\xa3\x90^@' p1723 tp1724 Rp1725 g1697 F89.0 tp1726 a(I3270000 g1 (g5 S'\x9a\x99\x99\x99\x99y^@' p1727 tp1728 Rp1729 g1697 F204.0 tp1730 a(I3280000 g1 (g5 S'\xa4p=\n\xd7\xe3^@' p1731 tp1732 Rp1733 g1697 F103.0 tp1734 a(I3290000 g1 (g5 S'\x00\x00\x00\x00\x00\x90^@' p1735 tp1736 Rp1737 g1697 F3.0 tp1738 a(I3300000 g1 (g5 S'H\xe1z\x14\xae\xd7\\@' p1739 tp1740 Rp1741 g1697 F131.0 tp1742 a(I3310000 g1 (g5 S'33333\xc3[@' p1743 tp1744 Rp1745 g1697 F113.0 tp1746 a(I3320000 g1 (g5 S'\xa4p=\n\xd7\x83\\@' p1747 tp1748 Rp1749 g1697 F54.0 tp1750 a(I3330000 g1 (g5 S')\\\x8f\xc2\xf5\xa8]@' p1751 tp1752 Rp1753 g1697 F125.0 tp1754 a(I3340000 g1 (g5 S'\x85\xebQ\xb8\x1eE_@' p1755 tp1756 Rp1757 g1697 F128.0 tp1758 a(I3350000 g1 (g5 S'\xc3\xf5(\\\x8f*`@' p1759 tp1760 Rp1761 g1 (g5 S'\xc3\xf5(\\\x8f*`@' p1762 tp1763 Rp1764 F244.0 tp1765 a(I3360000 g1 (g5 S'\xc3\xf5(\\\x8f\xd2_@' p1766 tp1767 Rp1768 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc<`@' p1769 tp1770 Rp1771 F81.0 tp1772 a(I3370000 g1 (g5 S')\\\x8f\xc2\xf5\xc8_@' p1773 tp1774 Rp1775 g1771 F131.0 tp1776 a(I3380000 g1 (g5 S'\xb8\x1e\x85\xebQ\x88`@' p1777 tp1778 Rp1779 g1 (g5 S'\xb8\x1e\x85\xebQ\x88`@' p1780 tp1781 Rp1782 F87.0 tp1783 a(I3390000 g1 (g5 S'\n\xd7\xa3p=\xda`@' p1784 tp1785 Rp1786 g1 (g5 S'{\x14\xaeG\xe1\xe2`@' p1787 tp1788 Rp1789 F135.0 tp1790 a(I3400000 g1 (g5 S'\x9a\x99\x99\x99\x99Qa@' p1791 tp1792 Rp1793 g1 (g5 S'\x9a\x99\x99\x99\x99Qa@' p1794 tp1795 Rp1796 F279.0 tp1797 a(I3410000 g1 (g5 S'H\xe1z\x14\xae\x9fa@' p1798 tp1799 Rp1800 g1 (g5 S'R\xb8\x1e\x85\xeb\xb1a@' p1801 tp1802 Rp1803 F260.0 tp1804 a(I3420000 g1 (g5 S'\xf6(\\\x8f\xc2Ea@' p1805 tp1806 Rp1807 g1803 F106.0 tp1808 a(I3430000 g1 (g5 S'H\xe1z\x14\xaeWa@' p1809 tp1810 Rp1811 g1803 F140.0 tp1812 a(I3440000 g1 (g5 S'33333\x93b@' p1813 tp1814 Rp1815 g1 (g5 S'33333\x93b@' p1816 tp1817 Rp1818 F96.0 tp1819 a(I3450000 g1 (g5 S'\xb8\x1e\x85\xebQ\xd8b@' p1820 tp1821 Rp1822 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x04c@' p1823 tp1824 Rp1825 F20.0 tp1826 a(I3460000 g1 (g5 S')\\\x8f\xc2\xf5@b@' p1827 tp1828 Rp1829 g1825 F97.0 tp1830 a(I3470000 g1 (g5 S'\x9a\x99\x99\x99\x99Ab@' p1831 tp1832 Rp1833 g1825 F203.0 tp1834 a(I3480000 g1 (g5 S'\\\x8f\xc2\xf5(\xd4a@' p1835 tp1836 Rp1837 g1825 F100.0 tp1838 a(I3490000 g1 (g5 S'\xd7\xa3p=\nwa@' p1839 tp1840 Rp1841 g1825 F92.0 tp1842 a(I3500000 g1 (g5 S'\xecQ\xb8\x1e\x85sa@' p1843 tp1844 Rp1845 g1825 F113.0 tp1846 a(I3510000 g1 (g5 S'\x9a\x99\x99\x99\x99\xc1`@' p1847 tp1848 Rp1849 g1825 F76.0 tp1850 a(I3520000 g1 (g5 S'\xaeG\xe1z\x14\x8e`@' p1851 tp1852 Rp1853 g1825 F139.0 tp1854 a(I3530000 g1 (g5 S'\\\x8f\xc2\xf5(\\`@' p1855 tp1856 Rp1857 g1825 F175.0 tp1858 a(I3540000 g1 (g5 S'\xaeG\xe1z\x14\x1e`@' p1859 tp1860 Rp1861 g1825 F109.0 tp1862 a(I3550000 g1 (g5 S'\xd7\xa3p=\n\x97_@' p1863 tp1864 Rp1865 g1825 F20.0 tp1866 a(I3560000 g1 (g5 S'\n\xd7\xa3p=:^@' p1867 tp1868 Rp1869 g1825 F75.0 tp1870 a(I3570000 g1 (g5 S'\x14\xaeG\xe1z4\\@' p1871 tp1872 Rp1873 g1825 F23.0 tp1874 a(I3580000 g1 (g5 S'\\\x8f\xc2\xf5(\xccY@' p1875 tp1876 Rp1877 g1825 F105.0 tp1878 a(I3590000 g1 (g5 S'H\xe1z\x14\xae\xf7Y@' p1879 tp1880 Rp1881 g1825 F34.0 tp1882 a(I3600000 g1 (g5 S'\xe1z\x14\xaeG\xe1X@' p1883 tp1884 Rp1885 g1825 F112.0 tp1886 a(I3610000 g1 (g5 S'\x9a\x99\x99\x99\x99\xa9X@' p1887 tp1888 Rp1889 g1825 F79.0 tp1890 a(I3620000 g1 (g5 S'\x00\x00\x00\x00\x00\xd0W@' p1891 tp1892 Rp1893 g1825 F39.0 tp1894 a(I3630000 g1 (g5 S'\x14\xaeG\xe1z\x04T@' p1895 tp1896 Rp1897 g1825 F50.0 tp1898 a(I3640000 g1 (g5 S'\x85\xebQ\xb8\x1eeR@' p1899 tp1900 Rp1901 g1825 F47.0 tp1902 a(I3650000 g1 (g5 S'\xecQ\xb8\x1e\x85\x9bP@' p1903 tp1904 Rp1905 g1825 F34.0 tp1906 a(I3660000 g1 (g5 S'\xaeG\xe1z\x14\x0eL@' p1907 tp1908 Rp1909 g1825 F26.0 tp1910 a(I3670000 g1 (g5 S'\x8f\xc2\xf5(\\OE@' p1911 tp1912 Rp1913 g1825 F14.0 tp1914 a(I3680000 g1 (g5 S'\xe1z\x14\xaeG!@@' p1915 tp1916 Rp1917 g1825 F5.0 tp1918 a(I3690000 g1 (g5 S'\x14\xaeG\xe1zT7@' p1919 tp1920 Rp1921 g1825 F10.0 tp1922 a(I3700000 g1 (g5 S'\n\xd7\xa3p=\n%@' p1923 tp1924 Rp1925 g1825 F8.0 tp1926 a(I3710000 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e!@' p1927 tp1928 Rp1929 g1825 F10.0 tp1930 a(I3720000 g1 (g5 S'R\xb8\x1e\x85\xebQ\x18@' p1931 tp1932 Rp1933 g1825 F8.0 tp1934 a(I3730000 g1 (g5 S'\xaeG\xe1z\x14\xae\x12@' p1935 tp1936 Rp1937 g1825 F3.0 tp1938 a(I3740000 g1 (g5 S'\x14\xaeG\xe1z\x14\n@' p1939 tp1940 Rp1941 g1825 F2.0 tp1942 a(I3750000 g1 (g5 S'\xaeG\xe1z\x14\xae\x03@' p1943 tp1944 Rp1945 g1825 F4.0 tp1946 a(I3760000 g1 (g5 S'=\n\xd7\xa3p=\x00@' p1947 tp1948 Rp1949 g1825 F3.0 tp1950 a(I3770000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc\xfc?' p1951 tp1952 Rp1953 g1825 F0.0 tp1954 a(I3780000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f\xfa?' p1955 tp1956 Rp1957 g1825 F4.0 tp1958 a(I3790000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xfa?' p1959 tp1960 Rp1961 g1825 F3.0 tp1962 a(I3800000 g1 (g5 S'\xaeG\xe1z\x14\xae\xf7?' p1963 tp1964 Rp1965 g1825 F1.0 tp1966 a(I3810000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc\xfc?' p1967 tp1968 Rp1969 g1825 F0.0 tp1970 a(I3820000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\xfc?' p1971 tp1972 Rp1973 g1825 F1.0 tp1974 a(I3830000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc\xf8?' p1975 tp1976 Rp1977 g1825 F1.0 tp1978 a(I3840000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xfd?' p1979 tp1980 Rp1981 g1825 F2.0 tp1982 a(I3850000 g1 (g5 S'\n\xd7\xa3p=\n\xff?' p1983 tp1984 Rp1985 g1825 F4.0 tp1986 a(I3860000 g1 (g5 S'\x85\xebQ\xb8\x1e\x85\xff?' p1987 tp1988 Rp1989 g1825 F1.0 tp1990 a(I3870000 g1 (g5 S'\x14\xaeG\xe1z\x14\xfe?' p1991 tp1992 Rp1993 g1825 F5.0 tp1994 a(I3880000 g1 (g5 S'=\n\xd7\xa3p=\xfa?' p1995 tp1996 Rp1997 g1825 F1.0 tp1998 a(I3890000 g1 (g5 S'\xf6(\\\x8f\xc2\xf5\xf8?' p1999 tp2000 Rp2001 g1825 F0.0 tp2002 a(I3900000 g1 (g5 S'ffffff\xfa?' p2003 tp2004 Rp2005 g1825 F0.0 tp2006 a(I3910000 g1 (g5 S'\xc3\xf5(\\\x8f\xc2\xf9?' p2007 tp2008 Rp2009 g1825 F2.0 tp2010 a(I3920000 g1 (g5 S'\xe1z\x14\xaeG\xe1\xf2?' p2011 tp2012 Rp2013 g1825 F1.0 tp2014 a(I3930000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\xf4?' p2015 tp2016 Rp2017 g1825 F0.0 tp2018 a(I3940000 g1 (g5 S'R\xb8\x1e\x85\xebQ\xf8?' p2019 tp2020 Rp2021 g1825 F2.0 tp2022 a(I3950000 g1 (g5 S'{\x14\xaeG\xe1z\x00@' p2023 tp2024 Rp2025 g1825 F1.0 tp2026 a(I3960000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\x01@' p2027 tp2028 Rp2029 g1825 F3.0 tp2030 a(I3970000 g1 (g5 S'\x85\xebQ\xb8\x1e\x85\xff?' p2031 tp2032 Rp2033 g1825 F1.0 tp2034 a(I3980000 g1 (g5 S'\xe1z\x14\xaeG\xe1\xfe?' p2035 tp2036 Rp2037 g1825 F0.0 tp2038 a(I3990000 g1 (g5 S'\xe1z\x14\xaeG\xe1\x00@' p2039 tp2040 Rp2041 g1825 F3.0 tp2042 a(I4000000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x02@' p2043 tp2044 Rp2045 g1825 F3.0 tp2046 a(I4010000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc\x02@' p2047 tp2048 Rp2049 g1825 F4.0 tp2050 a(I4020000 g1 (g5 S'\\\x8f\xc2\xf5(\\\x01@' p2051 tp2052 Rp2053 g1825 F7.0 tp2054 a(I4030000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\x01@' p2055 tp2056 Rp2057 g1825 F4.0 tp2058 a(I4040000 g1 (g5 S'\\\x8f\xc2\xf5(\\\x01@' p2059 tp2060 Rp2061 g1825 F4.0 tp2062 a(I4050000 g1 (g5 S'\xd7\xa3p=\n\xd7\xff?' p2063 tp2064 Rp2065 g1825 F0.0 tp2066 a(I4060000 g1 (g5 S'ffffff\x00@' p2067 tp2068 Rp2069 g1825 F0.0 tp2070 a(I4070000 g1 (g5 S'\xa4p=\n\xd7\xa3\x00@' p2071 tp2072 Rp2073 g1825 F4.0 tp2074 a(I4080000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\x01@' p2075 tp2076 Rp2077 g1825 F4.0 tp2078 a(I4090000 g1 (g5 S'\xaeG\xe1z\x14\xae\x03@' p2079 tp2080 Rp2081 g1825 F1.0 tp2082 a(I4100000 g1 (g5 S'\xf6(\\\x8f\xc2\xf5\x04@' p2083 tp2084 Rp2085 g1825 F0.0 tp2086 a(I4110000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\x05@' p2087 tp2088 Rp2089 g1825 F3.0 tp2090 a(I4120000 g1 (g5 S'H\xe1z\x14\xaeG\x0b@' p2091 tp2092 Rp2093 g1825 F4.0 tp2094 a(I4130000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x10@' p2095 tp2096 Rp2097 g1825 F5.0 tp2098 a(I4140000 g1 (g5 S'ffffff\x11@' p2099 tp2100 Rp2101 g1825 F10.0 tp2102 a(I4150000 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e\x12@' p2103 tp2104 Rp2105 g1825 F3.0 tp2106 a(I4160000 g1 (g5 S'\n\xd7\xa3p=\n\x13@' p2107 tp2108 Rp2109 g1825 F3.0 tp2110 a(I4170000 g1 (g5 S'\xc3\xf5(\\\x8f\xc2\x13@' p2111 tp2112 Rp2113 g1825 F3.0 tp2114 a(I4180000 g1 (g5 S'\x14\xaeG\xe1z\x14\x14@' p2115 tp2116 Rp2117 g1825 F5.0 tp2118 a(I4190000 g1 (g5 S'=\n\xd7\xa3p=\x14@' p2119 tp2120 Rp2121 g1825 F2.0 tp2122 a(I4200000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\x15@' p2123 tp2124 Rp2125 g1825 F4.0 tp2126 a(I4210000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc\x17@' p2127 tp2128 Rp2129 g1825 F5.0 tp2130 a(I4220000 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e\x1a@' p2131 tp2132 Rp2133 g1825 F9.0 tp2134 a(I4230000 g1 (g5 S'{\x14\xaeG\xe1z\x1d@' p2135 tp2136 Rp2137 g1825 F9.0 tp2138 a(I4240000 g1 (g5 S'\xa4p=\n\xd7\xa3\x1f@' p2139 tp2140 Rp2141 g1825 F7.0 tp2142 a(I4250000 g1 (g5 S'\x9a\x99\x99\x99\x99\x19!@' p2143 tp2144 Rp2145 g1825 F6.0 tp2146 a(I4260000 g1 (g5 S'q=\n\xd7\xa3\xf0!@' p2147 tp2148 Rp2149 g1825 F6.0 tp2150 a(I4270000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99"@' p2151 tp2152 Rp2153 g1825 F10.0 tp2154 a(I4280000 g1 (g5 S')\\\x8f\xc2\xf5\xa8#@' p2155 tp2156 Rp2157 g1825 F15.0 tp2158 a(I4290000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f$@' p2159 tp2160 Rp2161 g1825 F11.0 tp2162 a(I4300000 g1 (g5 S'fffff\xe6$@' p2163 tp2164 Rp2165 g1825 F10.0 tp2166 a(I4310000 g1 (g5 S'H\xe1z\x14\xaeG&@' p2167 tp2168 Rp2169 g1825 F23.0 tp2170 a(I4320000 g1 (g5 S'{\x14\xaeG\xe1\xfa&@' p2171 tp2172 Rp2173 g1825 F9.0 tp2174 a(I4330000 g1 (g5 S"R\xb8\x1e\x85\xebQ'@" p2175 tp2176 Rp2177 g1825 F9.0 tp2178 a(I4340000 g1 (g5 S'\xe1z\x14\xaeG\xe1(@' p2179 tp2180 Rp2181 g1825 F11.0 tp2182 a(I4350000 g1 (g5 S'H\xe1z\x14\xae\xc7*@' p2183 tp2184 Rp2185 g1825 F13.0 tp2186 a(I4360000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc,@' p2187 tp2188 Rp2189 g1825 F15.0 tp2190 a(I4370000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8-@' p2191 tp2192 Rp2193 g1825 F12.0 tp2194 a(I4380000 g1 (g5 S'\xd7\xa3p=\nW/@' p2195 tp2196 Rp2197 g1825 F14.0 tp2198 a(I4390000 g1 (g5 S'{\x14\xaeG\xe1:0@' p2199 tp2200 Rp2201 g1825 F19.0 tp2202 a(I4400000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99/@' p2203 tp2204 Rp2205 g1825 F18.0 tp2206 a(I4410000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f.@' p2207 tp2208 Rp2209 g1825 F17.0 tp2210 a(I4420000 g1 (g5 S'\x14\xaeG\xe1z\x14/@' p2211 tp2212 Rp2213 g1825 F12.0 tp2214 a(I4430000 g1 (g5 S'q=\n\xd7\xa3p/@' p2215 tp2216 Rp2217 g1825 F16.0 tp2218 a(I4440000 g1 (g5 S'\x85\xebQ\xb8\x1e\x05/@' p2219 tp2220 Rp2221 g1825 F19.0 tp2222 a(I4450000 g1 (g5 S'\xc3\xf5(\\\x8f\xc2.@' p2223 tp2224 Rp2225 g1825 F13.0 tp2226 a(I4460000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99/@' p2227 tp2228 Rp2229 g1825 F14.0 tp2230 a(I4470000 g1 (g5 S'H\xe1z\x14\xaeG0@' p2231 tp2232 Rp2233 g1825 F11.0 tp2234 a(I4480000 g1 (g5 S'\xf6(\\\x8f\xc250@' p2235 tp2236 Rp2237 g1825 F12.0 tp2238 a(I4490000 g1 (g5 S'\x00\x00\x00\x00\x00\x000@' p2239 tp2240 Rp2241 g1825 F12.0 tp2242 a(I4500000 g1 (g5 S'\xc3\xf5(\\\x8f\x820@' p2243 tp2244 Rp2245 g1825 F15.0 tp2246 a(I4510000 g1 (g5 S'\\\x8f\xc2\xf5(\\1@' p2247 tp2248 Rp2249 g1825 F27.0 tp2250 a(I4520000 g1 (g5 S'\x8f\xc2\xf5(\\O2@' p2251 tp2252 Rp2253 g1825 F11.0 tp2254 a(I4530000 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e3@' p2255 tp2256 Rp2257 g1825 F35.0 tp2258 a(I4540000 g1 (g5 S'fffff&4@' p2259 tp2260 Rp2261 g1825 F27.0 tp2262 a(I4550000 g1 (g5 S'3333335@' p2263 tp2264 Rp2265 g1825 F20.0 tp2266 a(I4560000 g1 (g5 S'\xf6(\\\x8f\xc2u6@' p2267 tp2268 Rp2269 g1825 F32.0 tp2270 a(I4570000 g1 (g5 S'\xb8\x1e\x85\xebQx7@' p2271 tp2272 Rp2273 g1825 F40.0 tp2274 a(I4580000 g1 (g5 S'\xd7\xa3p=\n\x179@' p2275 tp2276 Rp2277 g1825 F33.0 tp2278 a(I4590000 g1 (g5 S'\x85\xebQ\xb8\x1e\x859@' p2279 tp2280 Rp2281 g1825 F22.0 tp2282 a(I4600000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f9@' p2283 tp2284 Rp2285 g1825 F32.0 tp2286 a(I4610000 g1 (g5 S'\\\x8f\xc2\xf5(\x1c:@' p2287 tp2288 Rp2289 g1825 F22.0 tp2290 a(I4620000 g1 (g5 S'{\x14\xaeG\xe1\xba:@' p2291 tp2292 Rp2293 g1825 F20.0 tp2294 a(I4630000 g1 (g5 S'\xf6(\\\x8f\xc2\xf5:@' p2295 tp2296 Rp2297 g1825 F18.0 tp2298 a(I4640000 g1 (g5 S'\x85\xebQ\xb8\x1e\x85;@' p2299 tp2300 Rp2301 g1825 F46.0 tp2302 a(I4650000 g1 (g5 S'\x14\xaeG\xe1z\xd4<@' p2303 tp2304 Rp2305 g1825 F29.0 tp2306 a(I4660000 g1 (g5 S'{\x14\xaeG\xe1\xba=@' p2307 tp2308 Rp2309 g1825 F54.0 tp2310 a(I4670000 g1 (g5 S'=\n\xd7\xa3p}?@' p2311 tp2312 Rp2313 g1825 F50.0 tp2314 a(I4680000 g1 (g5 S'\n\xd7\xa3p=\n@@' p2315 tp2316 Rp2317 g1825 F38.0 tp2318 a(I4690000 g1 (g5 S'fffff\x06A@' p2319 tp2320 Rp2321 g1825 F47.0 tp2322 a(I4700000 g1 (g5 S'\x85\xebQ\xb8\x1e\x05B@' p2323 tp2324 Rp2325 g1825 F31.0 tp2326 a(I4710000 g1 (g5 S'=\n\xd7\xa3p}B@' p2327 tp2328 Rp2329 g1825 F26.0 tp2330 a(I4720000 g1 (g5 S'\xa4p=\n\xd7\x03C@' p2331 tp2332 Rp2333 g1825 F70.0 tp2334 a(I4730000 g1 (g5 S'R\xb8\x1e\x85\xeb1D@' p2335 tp2336 Rp2337 g1825 F62.0 tp2338 a(I4740000 g1 (g5 S'R\xb8\x1e\x85\xeb\x11E@' p2339 tp2340 Rp2341 g1825 F39.0 tp2342 a(I4750000 g1 (g5 S'33333\xd3E@' p2343 tp2344 Rp2345 g1825 F55.0 tp2346 a(I4760000 g1 (g5 S'{\x14\xaeG\xe1\xbaF@' p2347 tp2348 Rp2349 g1825 F67.0 tp2350 a(I4770000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x0cG@' p2351 tp2352 Rp2353 g1825 F64.0 tp2354 a(I4780000 g1 (g5 S'\xaeG\xe1z\x14.G@' p2355 tp2356 Rp2357 g1825 F50.0 tp2358 a(I4790000 g1 (g5 S'H\xe1z\x14\xae\xc7G@' p2359 tp2360 Rp2361 g1825 F75.0 tp2362 a(I4800000 g1 (g5 S'H\xe1z\x14\xaeGH@' p2363 tp2364 Rp2365 g1825 F88.0 tp2366 a(I4810000 g1 (g5 S'\x8f\xc2\xf5(\\\x0fI@' p2367 tp2368 Rp2369 g1825 F78.0 tp2370 a(I4820000 g1 (g5 S'\x00\x00\x00\x00\x00\x80I@' p2371 tp2372 Rp2373 g1825 F59.0 tp2374 a(I4830000 g1 (g5 S'{\x14\xaeG\xe1ZJ@' p2375 tp2376 Rp2377 g1825 F75.0 tp2378 a(I4840000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8J@' p2379 tp2380 Rp2381 g1825 F37.0 tp2382 a(I4850000 g1 (g5 S'\x1f\x85\xebQ\xb8\xdeJ@' p2383 tp2384 Rp2385 g1825 F63.0 tp2386 a(I4860000 g1 (g5 S'\x8f\xc2\xf5(\\\x8fJ@' p2387 tp2388 Rp2389 g1825 F33.0 tp2390 a(I4870000 g1 (g5 S'R\xb8\x1e\x85\xebQK@' p2391 tp2392 Rp2393 g1825 F67.0 tp2394 a(I4880000 g1 (g5 S'\xaeG\xe1z\x14\x0eK@' p2395 tp2396 Rp2397 g1825 F43.0 tp2398 a(I4890000 g1 (g5 S'\x14\xaeG\xe1z\xb4K@' p2399 tp2400 Rp2401 g1825 F55.0 tp2402 a(I4900000 g1 (g5 S'{\x14\xaeG\xe1\x1aL@' p2403 tp2404 Rp2405 g1825 F33.0 tp2406 a(I4910000 g1 (g5 S'\\\x8f\xc2\xf5(|L@' p2407 tp2408 Rp2409 g1825 F74.0 tp2410 a(I4920000 g1 (g5 S')\\\x8f\xc2\xf5\xa8L@' p2411 tp2412 Rp2413 g1825 F68.0 tp2414 a(I4930000 g1 (g5 S'fffff\xa6M@' p2415 tp2416 Rp2417 g1825 F87.0 tp2418 a(I4940000 g1 (g5 S'\x8f\xc2\xf5(\\\xcfM@' p2419 tp2420 Rp2421 g1825 F86.0 tp2422 a(I4950000 g1 (g5 S'\xaeG\xe1z\x14nN@' p2423 tp2424 Rp2425 g1825 F73.0 tp2426 a(I4960000 g1 (g5 S'H\xe1z\x14\xae\x07O@' p2427 tp2428 Rp2429 g1825 F110.0 tp2430 a(I4970000 g1 (g5 S'{\x14\xaeG\xe1\xfaO@' p2431 tp2432 Rp2433 g1825 F72.0 tp2434 a(I4980000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x8cP@' p2435 tp2436 Rp2437 g1825 F74.0 tp2438 a(I4990000 g1 (g5 S'\xb8\x1e\x85\xebQ8Q@' p2439 tp2440 Rp2441 g1825 F61.0 tp2442 a(I5000000 g1 (g5 S')\\\x8f\xc2\xf5HQ@' p2443 tp2444 Rp2445 g1825 F83.0 tp2446 a(I5010000 g1 (g5 S')\\\x8f\xc2\xf5\x88Q@' p2447 tp2448 Rp2449 g1825 F82.0 tp2450 a(I5020000 g1 (g5 S'\\\x8f\xc2\xf5(\xfcQ@' p2451 tp2452 Rp2453 g1825 F53.0 tp2454 a(I5030000 g1 (g5 S'\xf6(\\\x8f\xc2\xb5R@' p2455 tp2456 Rp2457 g1825 F69.0 tp2458 a(I5040000 g1 (g5 S'\xf6(\\\x8f\xc2\x85S@' p2459 tp2460 Rp2461 g1825 F109.0 tp2462 a(I5050000 g1 (g5 S'\x9a\x99\x99\x99\x99\xd9S@' p2463 tp2464 Rp2465 g1825 F93.0 tp2466 a(I5060000 g1 (g5 S'\xecQ\xb8\x1e\x85\xabS@' p2467 tp2468 Rp2469 g1825 F63.0 tp2470 a(I5070000 g1 (g5 S'\\\x8f\xc2\xf5(\x9cS@' p2471 tp2472 Rp2473 g1825 F55.0 tp2474 a(I5080000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x8cS@' p2475 tp2476 Rp2477 g1825 F80.0 tp2478 a(I5090000 g1 (g5 S'{\x14\xaeG\xe1jS@' p2479 tp2480 Rp2481 g1825 F80.0 tp2482 a(I5100000 g1 (g5 S'\xe1z\x14\xaeG\x91S@' p2483 tp2484 Rp2485 g1825 F88.0 tp2486 a(I5110000 g1 (g5 S'33333\xc3S@' p2487 tp2488 Rp2489 g1825 F129.0 tp2490 a(I5120000 g1 (g5 S'\xb8\x1e\x85\xebQ\xe8S@' p2491 tp2492 Rp2493 g1825 F66.0 tp2494 a(I5130000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xecT@' p2495 tp2496 Rp2497 g1825 F111.0 tp2498 a(I5140000 g1 (g5 S'\xb8\x1e\x85\xebQ\x08U@' p2499 tp2500 Rp2501 g1825 F119.0 tp2502 a(I5150000 g1 (g5 S'H\xe1z\x14\xae7U@' p2503 tp2504 Rp2505 g1825 F61.0 tp2506 a(I5160000 g1 (g5 S'\xb8\x1e\x85\xebQhU@' p2507 tp2508 Rp2509 g1825 F88.0 tp2510 a(I5170000 g1 (g5 S'\xa4p=\n\xd7\x93T@' p2511 tp2512 Rp2513 g1825 F115.0 tp2514 a(I5180000 g1 (g5 S'\xaeG\xe1z\x14.U@' p2515 tp2516 Rp2517 g1825 F90.0 tp2518 a(I5190000 g1 (g5 S'\x14\xaeG\xe1z4U@' p2519 tp2520 Rp2521 g1825 F66.0 tp2522 a(I5200000 g1 (g5 S'\x85\xebQ\xb8\x1e\x85U@' p2523 tp2524 Rp2525 g1825 F77.0 tp2526 a(I5210000 g1 (g5 S'\xf6(\\\x8f\xc2%V@' p2527 tp2528 Rp2529 g1825 F132.0 tp2530 a(I5220000 g1 (g5 S'H\xe1z\x14\xae\xe7V@' p2531 tp2532 Rp2533 g1825 F86.0 tp2534 a(I5230000 g1 (g5 S')\\\x8f\xc2\xf5\x18W@' p2535 tp2536 Rp2537 g1825 F103.0 tp2538 a(I5240000 g1 (g5 S'33333#W@' p2539 tp2540 Rp2541 g1825 F122.0 tp2542 a(I5250000 g1 (g5 S'\\\x8f\xc2\xf5(\xacW@' p2543 tp2544 Rp2545 g1825 F112.0 tp2546 a(I5260000 g1 (g5 S'\xc3\xf5(\\\x8f"X@' p2547 tp2548 Rp2549 g1825 F235.0 tp2550 a(I5270000 g1 (g5 S'q=\n\xd7\xa3 W@' p2551 tp2552 Rp2553 g1825 F45.0 tp2554 a(I5280000 g1 (g5 S'H\xe1z\x14\xae\x97W@' p2555 tp2556 Rp2557 g1825 F75.0 tp2558 a(I5290000 g1 (g5 S'\x9a\x99\x99\x99\x99iW@' p2559 tp2560 Rp2561 g1825 F69.0 tp2562 a(I5300000 g1 (g5 S'\x9a\x99\x99\x99\x99yX@' p2563 tp2564 Rp2565 g1825 F180.0 tp2566 a(I5310000 g1 (g5 S'R\xb8\x1e\x85\xeb!Y@' p2567 tp2568 Rp2569 g1825 F93.0 tp2570 a(I5320000 g1 (g5 S'\x00\x00\x00\x00\x00\x10Y@' p2571 tp2572 Rp2573 g1825 F130.0 tp2574 a(I5330000 g1 (g5 S'\xb8\x1e\x85\xebQ\xa8Y@' p2575 tp2576 Rp2577 g1825 F126.0 tp2578 a(I5340000 g1 (g5 S'\xf6(\\\x8f\xc2\xb5Z@' p2579 tp2580 Rp2581 g1825 F171.0 tp2582 a(I5350000 g1 (g5 S'33333#[@' p2583 tp2584 Rp2585 g1825 F81.0 tp2586 a(I5360000 g1 (g5 S'\x00\x00\x00\x00\x00\x00[@' p2587 tp2588 Rp2589 g1825 F94.0 tp2590 a(I5370000 g1 (g5 S'fffff\xa6[@' p2591 tp2592 Rp2593 g1825 F46.0 tp2594 a(I5380000 g1 (g5 S'\xc3\xf5(\\\x8f\x82[@' p2595 tp2596 Rp2597 g1825 F77.0 tp2598 a(I5390000 g1 (g5 S'\n\xd7\xa3p=:[@' p2599 tp2600 Rp2601 g1825 F55.0 tp2602 a(I5400000 g1 (g5 S'33333\x13[@' p2603 tp2604 Rp2605 g1825 F192.0 tp2606 a(I5410000 g1 (g5 S'fffff\x06[@' p2607 tp2608 Rp2609 g1825 F109.0 tp2610 a(I5420000 g1 (g5 S'R\xb8\x1e\x85\xeba[@' p2611 tp2612 Rp2613 g1825 F113.0 tp2614 a(I5430000 g1 (g5 S'\xf6(\\\x8f\xc2\x85[@' p2615 tp2616 Rp2617 g1825 F22.0 tp2618 a(I5440000 g1 (g5 S'\xc3\xf5(\\\x8f\xd2Z@' p2619 tp2620 Rp2621 g1825 F104.0 tp2622 a(I5450000 g1 (g5 S'\xc3\xf5(\\\x8fRZ@' p2623 tp2624 Rp2625 g1825 F69.0 tp2626 a(I5460000 g1 (g5 S'\x8f\xc2\xf5(\\\x9fZ@' p2627 tp2628 Rp2629 g1825 F135.0 tp2630 a(I5470000 g1 (g5 S'\x85\xebQ\xb8\x1e\xd5Z@' p2631 tp2632 Rp2633 g1825 F141.0 tp2634 a(I5480000 g1 (g5 S'\xe1z\x14\xaeG\xe1Z@' p2635 tp2636 Rp2637 g1825 F96.0 tp2638 a(I5490000 g1 (g5 S'33333\x83Z@' p2639 tp2640 Rp2641 g1825 F98.0 tp2642 a(I5500000 g1 (g5 S'\x1f\x85\xebQ\xb8\xceZ@' p2643 tp2644 Rp2645 g1825 F107.0 tp2646 a(I5510000 g1 (g5 S'33333c[@' p2647 tp2648 Rp2649 g1825 F241.0 tp2650 a(I5520000 g1 (g5 S'{\x14\xaeG\xe1\xea\\@' p2651 tp2652 Rp2653 g1825 F189.0 tp2654 a(I5530000 g1 (g5 S'\xa4p=\n\xd7\x83]@' p2655 tp2656 Rp2657 g1825 F166.0 tp2658 a(I5540000 g1 (g5 S'q=\n\xd7\xa30^@' p2659 tp2660 Rp2661 g1825 F90.0 tp2662 a(I5550000 g1 (g5 S'\xb8\x1e\x85\xebQ\xc8_@' p2663 tp2664 Rp2665 g1825 F128.0 tp2666 a(I5560000 g1 (g5 S'\x14\xaeG\xe1zt`@' p2667 tp2668 Rp2669 g1825 F147.0 tp2670 a(I5570000 g1 (g5 S'\xecQ\xb8\x1e\x85C`@' p2671 tp2672 Rp2673 g1825 F74.0 tp2674 a(I5580000 g1 (g5 S'\\\x8f\xc2\xf5(\xd4`@' p2675 tp2676 Rp2677 g1825 F101.0 tp2678 a(I5590000 g1 (g5 S'\x85\xebQ\xb8\x1e\ra@' p2679 tp2680 Rp2681 g1825 F97.0 tp2682 a(I5600000 g1 (g5 S'\xf6(\\\x8f\xc2%a@' p2683 tp2684 Rp2685 g1825 F62.0 tp2686 a(I5610000 g1 (g5 S'\xcd\xcc\xcc\xcc\xccLa@' p2687 tp2688 Rp2689 g1825 F301.0 tp2690 a(I5620000 g1 (g5 S'33333\xfb`@' p2691 tp2692 Rp2693 g1825 F95.0 tp2694 a(I5630000 g1 (g5 S'\xb8\x1e\x85\xebQha@' p2695 tp2696 Rp2697 g1825 F124.0 tp2698 a(I5640000 g1 (g5 S'R\xb8\x1e\x85\xeb\xc9a@' p2699 tp2700 Rp2701 g1825 F150.0 tp2702 a(I5650000 g1 (g5 S'\xd7\xa3p=\nGa@' p2703 tp2704 Rp2705 g1825 F107.0 tp2706 a(I5660000 g1 (g5 S'\x00\x00\x00\x00\x00\x90`@' p2707 tp2708 Rp2709 g1825 F122.0 tp2710 a(I5670000 g1 (g5 S'H\xe1z\x14\xae\xcf`@' p2711 tp2712 Rp2713 g1825 F64.0 tp2714 a(I5680000 g1 (g5 S')\\\x8f\xc2\xf5\x18a@' p2715 tp2716 Rp2717 g1825 F130.0 tp2718 a(I5690000 g1 (g5 S'H\xe1z\x14\xae\x7f`@' p2719 tp2720 Rp2721 g1825 F147.0 tp2722 a(I5700000 g1 (g5 S'\x85\xebQ\xb8\x1eu`@' p2723 tp2724 Rp2725 g1825 F165.0 tp2726 a(I5710000 g1 (g5 S'\\\x8f\xc2\xf5(\x84`@' p2727 tp2728 Rp2729 g1825 F186.0 tp2730 a(I5720000 g1 (g5 S'\x9a\x99\x99\x99\x99!a@' p2731 tp2732 Rp2733 g1825 F168.0 tp2734 a(I5730000 g1 (g5 S'\x85\xebQ\xb8\x1e\x1da@' p2735 tp2736 Rp2737 g1825 F100.0 tp2738 a(I5740000 g1 (g5 S'\xc3\xf5(\\\x8f\xe2`@' p2739 tp2740 Rp2741 g1825 F144.0 tp2742 a(I5750000 g1 (g5 S'\x85\xebQ\xb8\x1e]a@' p2743 tp2744 Rp2745 g1825 F153.0 tp2746 a(I5760000 g1 (g5 S'=\n\xd7\xa3p\x8da@' p2747 tp2748 Rp2749 g1825 F61.0 tp2750 a(I5770000 g1 (g5 S'\xecQ\xb8\x1e\x85\xf3a@' p2751 tp2752 Rp2753 g1825 F148.0 tp2754 a(I5780000 g1 (g5 S'33333Sa@' p2755 tp2756 Rp2757 g1825 F197.0 tp2758 a(I5790000 g1 (g5 S'R\xb8\x1e\x85\xeb\x01b@' p2759 tp2760 Rp2761 g1825 F269.0 tp2762 a(I5800000 g1 (g5 S'\x8f\xc2\xf5(\\_b@' p2763 tp2764 Rp2765 g1825 F176.0 tp2766 a(I5810000 g1 (g5 S'{\x14\xaeG\xe1zb@' p2767 tp2768 Rp2769 g1825 F136.0 tp2770 a(I5820000 g1 (g5 S')\\\x8f\xc2\xf5Hb@' p2771 tp2772 Rp2773 g1825 F246.0 tp2774 a(I5830000 g1 (g5 S'\x14\xaeG\xe1z\x8cb@' p2775 tp2776 Rp2777 g1825 F120.0 tp2778 a(I5840000 g1 (g5 S'\x9a\x99\x99\x99\x99Ic@' p2779 tp2780 Rp2781 g1 (g5 S'\x9a\x99\x99\x99\x99Ic@' p2782 tp2783 Rp2784 F324.0 tp2785 a(I5850000 g1 (g5 S'\x9a\x99\x99\x99\x99Yc@' p2786 tp2787 Rp2788 g1 (g5 S'\xb8\x1e\x85\xebQpc@' p2789 tp2790 Rp2791 F103.0 tp2792 a(I5860000 g1 (g5 S'\x00\x00\x00\x00\x00\xa0c@' p2793 tp2794 Rp2795 g1 (g5 S'\x00\x00\x00\x00\x00\xa0c@' p2796 tp2797 Rp2798 F307.0 tp2799 a(I5870000 g1 (g5 S'\n\xd7\xa3p=\x1ac@' p2800 tp2801 Rp2802 g2798 F259.0 tp2803 a(I5880000 g1 (g5 S')\\\x8f\xc2\xf5\x80c@' p2804 tp2805 Rp2806 g2798 F102.0 tp2807 a(I5890000 g1 (g5 S'{\x14\xaeG\xe1\xd2c@' p2808 tp2809 Rp2810 g1 (g5 S'q=\n\xd7\xa3\xf8c@' p2811 tp2812 Rp2813 F61.0 tp2814 a(I5900000 g1 (g5 S'\x00\x00\x00\x00\x00\xf8c@' p2815 tp2816 Rp2817 g1 (g5 S'q=\n\xd7\xa3\x18d@' p2818 tp2819 Rp2820 F95.0 tp2821 a(I5910000 g1 (g5 S'q=\n\xd7\xa3pc@' p2822 tp2823 Rp2824 g2820 F174.0 tp2825 a(I5920000 g1 (g5 S'\x8f\xc2\xf5(\\Oc@' p2826 tp2827 Rp2828 g2820 F204.0 tp2829 a(I5930000 g1 (g5 S'\x8f\xc2\xf5(\\\xf7c@' p2830 tp2831 Rp2832 g2820 F267.0 tp2833 a(I5940000 g1 (g5 S'H\xe1z\x14\xae\xffc@' p2834 tp2835 Rp2836 g2820 F305.0 tp2837 a(I5950000 g1 (g5 S')\\\x8f\xc2\xf5\x10e@' p2838 tp2839 Rp2840 g1 (g5 S')\\\x8f\xc2\xf5\x10e@' p2841 tp2842 Rp2843 F219.0 tp2844 a(I5960000 g1 (g5 S'\xb8\x1e\x85\xebQ\x08f@' p2845 tp2846 Rp2847 g1 (g5 S'\xb8\x1e\x85\xebQ\x08f@' p2848 tp2849 Rp2850 F300.0 tp2851 a(I5970000 g1 (g5 S'=\n\xd7\xa3p\xf5e@' p2852 tp2853 Rp2854 g1 (g5 S'=\n\xd7\xa3pUf@' p2855 tp2856 Rp2857 F303.0 tp2858 a(I5980000 g1 (g5 S'H\xe1z\x14\xae\x9fe@' p2859 tp2860 Rp2861 g2857 F101.0 tp2862 a(I5990000 g1 (g5 S'\xf6(\\\x8f\xc2\xd5e@' p2863 tp2864 Rp2865 g2857 F312.0 tp2866 a(I6000000 g1 (g5 S'=\n\xd7\xa3pEf@' p2867 tp2868 Rp2869 g2857 F255.0 tp2870 a(I6010000 g1 (g5 S'\x00\x00\x00\x00\x00Hf@' p2871 tp2872 Rp2873 g1 (g5 S'33333\x93f@' p2874 tp2875 Rp2876 F152.0 tp2877 a(I6020000 g1 (g5 S'\x1f\x85\xebQ\xb8\xfef@' p2878 tp2879 Rp2880 g1 (g5 S'\n\xd7\xa3p=\ng@' p2881 tp2882 Rp2883 F190.0 tp2884 a(I6030000 g1 (g5 S'\x9a\x99\x99\x99\x99\x01g@' p2885 tp2886 Rp2887 g1 (g5 S'fffffVg@' p2888 tp2889 Rp2890 F184.0 tp2891 a(I6040000 g1 (g5 S'\x00\x00\x00\x00\x00Pg@' p2892 tp2893 Rp2894 g1 (g5 S'\x8f\xc2\xf5(\\og@' p2895 tp2896 Rp2897 F128.0 tp2898 a(I6050000 g1 (g5 S'\xf6(\\\x8f\xc2\xfdg@' p2899 tp2900 Rp2901 g1 (g5 S'{\x14\xaeG\xe12h@' p2902 tp2903 Rp2904 F105.0 tp2905 a(I6060000 g1 (g5 S'\xb8\x1e\x85\xebQ\xd0h@' p2906 tp2907 Rp2908 g1 (g5 S'\xb8\x1e\x85\xebQ\xd0h@' p2909 tp2910 Rp2911 F147.0 tp2912 a(I6070000 g1 (g5 S'\xd7\xa3p=\nGi@' p2913 tp2914 Rp2915 g1 (g5 S'\xd7\xa3p=\nGi@' p2916 tp2917 Rp2918 F264.0 tp2919 a(I6080000 g1 (g5 S'\xe1z\x14\xaeGai@' p2920 tp2921 Rp2922 g1 (g5 S'\xf6(\\\x8f\xc2ei@' p2923 tp2924 Rp2925 F315.0 tp2926 a(I6090000 g1 (g5 S'fffff\x86i@' p2927 tp2928 Rp2929 g1 (g5 S'\xecQ\xb8\x1e\x85\x9bi@' p2930 tp2931 Rp2932 F275.0 tp2933 a(I6100000 g1 (g5 S'q=\n\xd7\xa3pi@' p2934 tp2935 Rp2936 g1 (g5 S'\xd7\xa3p=\n\xa7i@' p2937 tp2938 Rp2939 F79.0 tp2940 a(I6110000 g1 (g5 S'\xf6(\\\x8f\xc2ei@' p2941 tp2942 Rp2943 g2939 F162.0 tp2944 a(I6120000 g1 (g5 S'\x1f\x85\xebQ\xb8\xc6i@' p2945 tp2946 Rp2947 g1 (g5 S'\x1f\x85\xebQ\xb8\xc6i@' p2948 tp2949 Rp2950 F180.0 tp2951 a(I6130000 g1 (g5 S'\x9a\x99\x99\x99\x99\x89j@' p2952 tp2953 Rp2954 g1 (g5 S'\x9a\x99\x99\x99\x99\x89j@' p2955 tp2956 Rp2957 F350.0 tp2958 a(I6140000 g1 (g5 S'\x8f\xc2\xf5(\\gk@' p2959 tp2960 Rp2961 g1 (g5 S'\x8f\xc2\xf5(\\gk@' p2962 tp2963 Rp2964 F319.0 tp2965 a(I6150000 g1 (g5 S'33333\xd3j@' p2966 tp2967 Rp2968 g1 (g5 S'\xaeG\xe1z\x14~k@' p2969 tp2970 Rp2971 F177.0 tp2972 a(I6160000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x04k@' p2973 tp2974 Rp2975 g2971 F192.0 tp2976 a(I6170000 g1 (g5 S'\\\x8f\xc2\xf5(|k@' p2977 tp2978 Rp2979 g1 (g5 S')\\\x8f\xc2\xf5\x80k@' p2980 tp2981 Rp2982 F236.0 tp2983 a(I6180000 g1 (g5 S'\xd7\xa3p=\n/k@' p2984 tp2985 Rp2986 g2982 F68.0 tp2987 a(I6190000 g1 (g5 S'\xd7\xa3p=\n\xffk@' p2988 tp2989 Rp2990 g1 (g5 S'\xd7\xa3p=\n\xffk@' p2991 tp2992 Rp2993 F328.0 tp2994 a(I6200000 g1 (g5 S'\xe1z\x14\xaeG\xa9l@' p2995 tp2996 Rp2997 g1 (g5 S'\xe1z\x14\xaeG\xa9l@' p2998 tp2999 Rp3000 F319.0 tp3001 a(I6210000 g1 (g5 S'\xaeG\xe1z\x14\xd6l@' p3002 tp3003 Rp3004 g1 (g5 S'\xaeG\xe1z\x14\xf6l@' p3005 tp3006 Rp3007 F232.0 tp3008 a(I6220000 g1 (g5 S'R\xb8\x1e\x85\xeb\xa9l@' p3009 tp3010 Rp3011 g3007 F47.0 tp3012 a(I6230000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x1cm@' p3013 tp3014 Rp3015 g1 (g5 S'\xd7\xa3p=\nWm@' p3016 tp3017 Rp3018 F245.0 tp3019 a(I6240000 g1 (g5 S'\xaeG\xe1z\x14\xa6m@' p3020 tp3021 Rp3022 g1 (g5 S'\xaeG\xe1z\x14\xa6m@' p3023 tp3024 Rp3025 F374.0 tp3026 a(I6250000 g1 (g5 S'\\\x8f\xc2\xf5(\x8cm@' p3027 tp3028 Rp3029 g3025 F315.0 tp3030 a(I6260000 g1 (g5 S'\xf6(\\\x8f\xc2\xd5m@' p3031 tp3032 Rp3033 g1 (g5 S'\x14\xaeG\xe1z\xe4m@' p3034 tp3035 Rp3036 F280.0 tp3037 a(I6270000 g1 (g5 S'\x14\xaeG\xe1z\xbcm@' p3038 tp3039 Rp3040 g3036 F307.0 tp3041 a(I6280000 g1 (g5 S'R\xb8\x1e\x85\xeb\x89n@' p3042 tp3043 Rp3044 g1 (g5 S'R\xb8\x1e\x85\xeb\x89n@' p3045 tp3046 Rp3047 F382.0 tp3048 a(I6290000 g1 (g5 S'33333\x13o@' p3049 tp3050 Rp3051 g1 (g5 S'{\x14\xaeG\xe1"o@' p3052 tp3053 Rp3054 F245.0 tp3055 a(I6300000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xccn@' p3056 tp3057 Rp3058 g3054 F113.0 tp3059 a(I6310000 g1 (g5 S'\xf6(\\\x8f\xc2\xc5n@' p3060 tp3061 Rp3062 g3054 F264.0 tp3063 a(I6320000 g1 (g5 S'\xe1z\x14\xaeG!o@' p3064 tp3065 Rp3066 g1 (g5 S'\xaeG\xe1z\x14~o@' p3067 tp3068 Rp3069 F98.0 tp3070 a(I6330000 g1 (g5 S'\xecQ\xb8\x1e\x85\xa3o@' p3071 tp3072 Rp3073 g1 (g5 S'\xecQ\xb8\x1e\x85\xa3o@' p3074 tp3075 Rp3076 F385.0 tp3077 a(I6340000 g1 (g5 S'\x1f\x85\xebQ\xb8\x86o@' p3078 tp3079 Rp3080 g1 (g5 S'\xa4p=\n\xd7\xabo@' p3081 tp3082 Rp3083 F237.0 tp3084 a(I6350000 g1 (g5 S'fffff\x8eo@' p3085 tp3086 Rp3087 g1 (g5 S'fffff\x06p@' p3088 tp3089 Rp3090 F164.0 tp3091 a(I6360000 g1 (g5 S'\x00\x00\x00\x00\x00\xc0o@' p3092 tp3093 Rp3094 g3090 F312.0 tp3095 a(I6370000 g1 (g5 S'\x85\xebQ\xb8\x1e-o@' p3096 tp3097 Rp3098 g3090 F316.0 tp3099 a(I6380000 g1 (g5 S'\x85\xebQ\xb8\x1e\xd5o@' p3100 tp3101 Rp3102 g3090 F339.0 tp3103 a(I6390000 g1 (g5 S'R\xb8\x1e\x85\xeb\xf9o@' p3104 tp3105 Rp3106 g3090 F255.0 tp3107 a(I6400000 g1 (g5 S'\x9a\x99\x99\x99\x99ap@' p3108 tp3109 Rp3110 g1 (g5 S'\x9a\x99\x99\x99\x99ap@' p3111 tp3112 Rp3113 F336.0 tp3114 a(I6410000 g1 (g5 S'\x8f\xc2\xf5(\\\x83p@' p3115 tp3116 Rp3117 g1 (g5 S'\x8f\xc2\xf5(\\\x83p@' p3118 tp3119 Rp3120 F327.0 tp3121 a(I6420000 g1 (g5 S'q=\n\xd7\xa3\xb4p@' p3122 tp3123 Rp3124 g1 (g5 S'q=\n\xd7\xa3\xb4p@' p3125 tp3126 Rp3127 F241.0 tp3128 a(I6430000 g1 (g5 S'=\n\xd7\xa3p\x95p@' p3129 tp3130 Rp3131 g3127 F254.0 tp3132 a(I6440000 g1 (g5 S'R\xb8\x1e\x85\xeb\xc5p@' p3133 tp3134 Rp3135 g1 (g5 S'R\xb8\x1e\x85\xeb\xc5p@' p3136 tp3137 Rp3138 F204.0 tp3139 a(I6450000 g1 (g5 S'\xc3\xf5(\\\x8f\xbap@' p3140 tp3141 Rp3142 g1 (g5 S'\xe1z\x14\xaeG\xe9p@' p3143 tp3144 Rp3145 F213.0 tp3146 a(I6460000 g1 (g5 S')\\\x8f\xc2\xf5\xb4p@' p3147 tp3148 Rp3149 g3145 F367.0 tp3150 a(I6470000 g1 (g5 S'fffff\xfep@' p3151 tp3152 Rp3153 g1 (g5 S'fffff\xfep@' p3154 tp3155 Rp3156 F369.0 tp3157 a(I6480000 g1 (g5 S'\x8f\xc2\xf5(\\\xffp@' p3158 tp3159 Rp3160 g1 (g5 S'\xe1z\x14\xaeG\x1dq@' p3161 tp3162 Rp3163 F354.0 tp3164 a(I6490000 g1 (g5 S'\x00\x00\x00\x00\x00\xf8p@' p3165 tp3166 Rp3167 g3163 F371.0 tp3168 a(I6500000 g1 (g5 S'33333\x07q@' p3169 tp3170 Rp3171 g1 (g5 S'q=\n\xd7\xa30q@' p3172 tp3173 Rp3174 F74.0 tp3175 a(I6510000 g1 (g5 S'\xb8\x1e\x85\xebQ\x10q@' p3176 tp3177 Rp3178 g3174 F337.0 tp3179 a(I6520000 g1 (g5 S'\n\xd7\xa3p=6q@' p3180 tp3181 Rp3182 g1 (g5 S'\xb8\x1e\x85\xebQ8q@' p3183 tp3184 Rp3185 F364.0 tp3186 a(I6530000 g1 (g5 S'\x00\x00\x00\x00\x00\xa0q@' p3187 tp3188 Rp3189 g1 (g5 S'\x00\x00\x00\x00\x00\xa0q@' p3190 tp3191 Rp3192 F431.0 tp3193 a(I6540000 g1 (g5 S'\x1f\x85\xebQ\xb8\x86q@' p3194 tp3195 Rp3196 g1 (g5 S'\x1f\x85\xebQ\xb8\xaaq@' p3197 tp3198 Rp3199 F317.0 tp3200 a(I6550000 g1 (g5 S'\x85\xebQ\xb8\x1e\x89q@' p3201 tp3202 Rp3203 g1 (g5 S')\\\x8f\xc2\xf5\xb4q@' p3204 tp3205 Rp3206 F128.0 tp3207 a(I6560000 g1 (g5 S'\xaeG\xe1z\x14~q@' p3208 tp3209 Rp3210 g3206 F250.0 tp3211 a(I6570000 g1 (g5 S'\xf6(\\\x8f\xc2\x81q@' p3212 tp3213 Rp3214 g3206 F285.0 tp3215 a(I6580000 g1 (g5 S'\xf6(\\\x8f\xc2\x85q@' p3216 tp3217 Rp3218 g3206 F259.0 tp3219 a(I6590000 g1 (g5 S'H\xe1z\x14\xaesq@' p3220 tp3221 Rp3222 g3206 F275.0 tp3223 a(I6600000 g1 (g5 S'\xaeG\xe1z\x14\xa2q@' p3224 tp3225 Rp3226 g3206 F213.0 tp3227 a(I6610000 g1 (g5 S'R\xb8\x1e\x85\xeb\xadq@' p3228 tp3229 Rp3230 g1 (g5 S')\\\x8f\xc2\xf5\xbcq@' p3231 tp3232 Rp3233 F315.0 tp3234 a(I6620000 g1 (g5 S'\x8f\xc2\xf5(\\\xdbq@' p3235 tp3236 Rp3237 g1 (g5 S'\x8f\xc2\xf5(\\\xdbq@' p3238 tp3239 Rp3240 F320.0 tp3241 a(I6630000 g1 (g5 S'\x85\xebQ\xb8\x1e\xe1q@' p3242 tp3243 Rp3244 g1 (g5 S'\xa4p=\n\xd7\xe3q@' p3245 tp3246 Rp3247 F358.0 tp3248 a(I6640000 g1 (g5 S'R\xb8\x1e\x85\xeb\xd1q@' p3249 tp3250 Rp3251 g1 (g5 S'q=\n\xd7\xa3\x04r@' p3252 tp3253 Rp3254 F299.0 tp3255 a(I6650000 g1 (g5 S'\x14\xaeG\xe1z\x88q@' p3256 tp3257 Rp3258 g3254 F152.0 tp3259 a(I6660000 g1 (g5 S'\n\xd7\xa3p=\xf6q@' p3260 tp3261 Rp3262 g3254 F198.0 tp3263 a(I6670000 g1 (g5 S'\xc3\xf5(\\\x8f\xfaq@' p3264 tp3265 Rp3266 g3254 F269.0 tp3267 a(I6680000 g1 (g5 S'\x1f\x85\xebQ\xb8\xf6q@' p3268 tp3269 Rp3270 g3254 F331.0 tp3271 a(I6690000 g1 (g5 S'\x85\xebQ\xb8\x1e%r@' p3272 tp3273 Rp3274 g1 (g5 S'\x85\xebQ\xb8\x1e%r@' p3275 tp3276 Rp3277 F404.0 tp3278 a(I6700000 g1 (g5 S'fffff\x1er@' p3279 tp3280 Rp3281 g3277 F407.0 tp3282 a(I6710000 g1 (g5 S'\xf6(\\\x8f\xc2\rr@' p3283 tp3284 Rp3285 g3277 F301.0 tp3286 a(I6720000 g1 (g5 S'\xd7\xa3p=\n7r@' p3287 tp3288 Rp3289 g1 (g5 S'\x85\xebQ\xb8\x1e9r@' p3290 tp3291 Rp3292 F359.0 tp3293 a(I6730000 g1 (g5 S'H\xe1z\x14\xaeGr@' p3294 tp3295 Rp3296 g1 (g5 S'\n\xd7\xa3p=Rr@' p3297 tp3298 Rp3299 F62.0 tp3300 a(I6740000 g1 (g5 S')\\\x8f\xc2\xf5w@' p4472 tp4473 Rp4474 g4402 F374.0 tp4475 a(I9310000 g1 (g5 S'H\xe1z\x14\xae\x1fw@' p4476 tp4477 Rp4478 g4402 F383.0 tp4479 a(I9320000 g1 (g5 S'q=\n\xd7\xa3\x08w@' p4480 tp4481 Rp4482 g4402 F382.0 tp4483 a(I9330000 g1 (g5 S'fffff*w@' p4484 tp4485 Rp4486 g4402 F341.0 tp4487 a(I9340000 g1 (g5 S'\x1f\x85\xebQ\xb8*w@' p4488 tp4489 Rp4490 g4402 F403.0 tp4491 a(I9350000 g1 (g5 S'\xd7\xa3p=\nSw@' p4492 tp4493 Rp4494 g4402 F399.0 tp4495 a(I9360000 g1 (g5 S'q=\n\xd7\xa3\xf0v@' p4496 tp4497 Rp4498 g4402 F84.0 tp4499 a(I9370000 g1 (g5 S'R\xb8\x1e\x85\xeb\xf9v@' p4500 tp4501 Rp4502 g4402 F422.0 tp4503 a(I9380000 g1 (g5 S'\xecQ\xb8\x1e\x85Kw@' p4504 tp4505 Rp4506 g4402 F413.0 tp4507 a(I9390000 g1 (g5 S'H\xe1z\x14\xae\x17w@' p4508 tp4509 Rp4510 g4402 F408.0 tp4511 a(I9400000 g1 (g5 S'\xd7\xa3p=\nkw@' p4512 tp4513 Rp4514 g4402 F406.0 tp4515 a(I9410000 g1 (g5 S'=\n\xd7\xa3p]w@' p4516 tp4517 Rp4518 g4402 F423.0 tp4519 a(I9420000 g1 (g5 S'\xcd\xcc\xcc\xcc\xccdw@' p4520 tp4521 Rp4522 g4402 F425.0 tp4523 a(I9430000 g1 (g5 S'H\xe1z\x14\xaeKw@' p4524 tp4525 Rp4526 g4402 F224.0 tp4527 a(I9440000 g1 (g5 S'\xc3\xf5(\\\x8fJw@' p4528 tp4529 Rp4530 g4402 F406.0 tp4531 a(I9450000 g1 (g5 S'\xecQ\xb8\x1e\x85Ww@' p4532 tp4533 Rp4534 g4402 F411.0 tp4535 a(I9460000 g1 (g5 S'\x9a\x99\x99\x99\x99Aw@' p4536 tp4537 Rp4538 g4402 F406.0 tp4539 a(I9470000 g1 (g5 S'{\x14\xaeG\xe1Jw@' p4540 tp4541 Rp4542 g4402 F232.0 tp4543 a(I9480000 g1 (g5 S'\xcd\xcc\xcc\xcc\xccDw@' p4544 tp4545 Rp4546 g4402 F382.0 tp4547 a(I9490000 g1 (g5 S'R\xb8\x1e\x85\xebqw@' p4548 tp4549 Rp4550 g4402 F346.0 tp4551 a(I9500000 g1 (g5 S'\xa4p=\n\xd7\x9fw@' p4552 tp4553 Rp4554 g4402 F421.0 tp4555 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S'\x00\x00\x00\x00\x00\x00\x00\x00' p36 tp37 Rp38 F-inf F0.0 tp39 a(I140000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p40 tp41 Rp42 F-inf F0.0 tp43 a(I150000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p44 tp45 Rp46 F-inf F0.0 tp47 a(I160000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p48 tp49 Rp50 F-inf F0.0 tp51 a(I170000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p52 tp53 Rp54 F-inf F0.0 tp55 a(I180000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p56 tp57 Rp58 F-inf F0.0 tp59 a(I190000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p60 tp61 Rp62 F-inf F0.0 tp63 a(I200000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p64 tp65 Rp66 F-inf F0.0 tp67 a(I210000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p68 tp69 Rp70 F-inf F0.0 tp71 a(I220000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p72 tp73 Rp74 F-inf F0.0 tp75 a(I230000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p76 tp77 Rp78 F-inf F0.0 tp79 a(I240000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p80 tp81 Rp82 F-inf F0.0 tp83 a(I250000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p84 tp85 Rp86 F-inf F0.0 tp87 a(I260000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p88 tp89 Rp90 F-inf F0.0 tp91 a(I270000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p92 tp93 Rp94 F-inf F0.0 tp95 a(I280000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p96 tp97 Rp98 F-inf F0.0 tp99 a(I290000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p100 tp101 Rp102 F-inf F0.0 tp103 a(I300000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p104 tp105 Rp106 F-inf F0.0 tp107 a(I310000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p108 tp109 Rp110 F-inf F0.0 tp111 a(I320000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p112 tp113 Rp114 F-inf F0.0 tp115 a(I330000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p116 tp117 Rp118 F-inf F0.0 tp119 a(I340000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p120 tp121 Rp122 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p123 tp124 Rp125 F0.0 tp126 a(I350000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p127 tp128 Rp129 g125 F0.0 tp130 a(I360000 g1 (g5 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F0.0 tp181 a(I480000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p182 tp183 Rp184 g168 F0.0 tp185 a(I490000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p186 tp187 Rp188 g168 F0.0 tp189 a(I500000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p190 tp191 Rp192 g168 F0.0 tp193 a(I510000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p194 tp195 Rp196 g168 F0.0 tp197 a(I520000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p198 tp199 Rp200 g168 F0.0 tp201 a(I530000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p202 tp203 Rp204 g168 F0.0 tp205 a(I540000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p206 tp207 Rp208 g168 F0.0 tp209 a(I550000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p210 tp211 Rp212 g168 F0.0 tp213 a(I560000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p214 tp215 Rp216 g168 F0.0 tp217 a(I570000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p218 tp219 Rp220 g168 F0.0 tp221 a(I580000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p222 tp223 Rp224 g168 F0.0 tp225 a(I590000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p226 tp227 Rp228 g168 F0.0 tp229 a(I600000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p230 tp231 Rp232 g168 F0.0 tp233 a(I610000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p234 tp235 Rp236 g168 F0.0 tp237 a(I620000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p238 tp239 Rp240 g168 F0.0 tp241 a(I630000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p242 tp243 Rp244 g168 F0.0 tp245 a(I640000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p246 tp247 Rp248 g168 F0.0 tp249 a(I650000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p250 tp251 Rp252 g168 F0.0 tp253 a(I660000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p254 tp255 Rp256 g168 F0.0 tp257 a(I670000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p258 tp259 Rp260 g168 F0.0 tp261 a(I680000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p262 tp263 Rp264 g168 F0.0 tp265 a(I690000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p266 tp267 Rp268 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p269 tp270 Rp271 F0.0 tp272 a(I700000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p273 tp274 Rp275 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p276 tp277 Rp278 F1.0 tp279 a(I710000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p280 tp281 Rp282 g278 F0.0 tp283 a(I720000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p284 tp285 Rp286 g278 F0.0 tp287 a(I730000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p288 tp289 Rp290 g278 F0.0 tp291 a(I740000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p292 tp293 Rp294 g278 F0.0 tp295 a(I750000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p296 tp297 Rp298 g278 F0.0 tp299 a(I760000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p300 tp301 Rp302 g278 F0.0 tp303 a(I770000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p304 tp305 Rp306 g278 F0.0 tp307 a(I780000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p308 tp309 Rp310 g278 F0.0 tp311 a(I790000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p312 tp313 Rp314 g278 F0.0 tp315 a(I800000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p316 tp317 Rp318 g278 F0.0 tp319 a(I810000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p320 tp321 Rp322 g278 F0.0 tp323 a(I820000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p324 tp325 Rp326 g278 F0.0 tp327 a(I830000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p328 tp329 Rp330 g278 F0.0 tp331 a(I840000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p332 tp333 Rp334 g278 F0.0 tp335 a(I850000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p336 tp337 Rp338 g278 F1.0 tp339 a(I860000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p340 tp341 Rp342 g278 F0.0 tp343 a(I870000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p344 tp345 Rp346 g278 F0.0 tp347 a(I880000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p348 tp349 Rp350 g278 F0.0 tp351 a(I890000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p352 tp353 Rp354 g278 F0.0 tp355 a(I900000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p356 tp357 Rp358 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p359 tp360 Rp361 F0.0 tp362 a(I910000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p363 tp364 Rp365 g361 F0.0 tp366 a(I920000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p367 tp368 Rp369 g361 F0.0 tp370 a(I930000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p371 tp372 Rp373 g361 F0.0 tp374 a(I940000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p375 tp376 Rp377 g361 F0.0 tp378 a(I950000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p379 tp380 Rp381 g361 F0.0 tp382 a(I960000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p383 tp384 Rp385 g361 F0.0 tp386 a(I970000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p387 tp388 Rp389 g361 F0.0 tp390 a(I980000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p391 tp392 Rp393 g361 F0.0 tp394 a(I990000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p395 tp396 Rp397 g361 F0.0 tp398 a(I1000000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p399 tp400 Rp401 g361 F0.0 tp402 a(I1010000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p403 tp404 Rp405 g361 F0.0 tp406 a(I1020000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p407 tp408 Rp409 g361 F0.0 tp410 a(I1030000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p411 tp412 Rp413 g361 F0.0 tp414 a(I1040000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p415 tp416 Rp417 g361 F0.0 tp418 a(I1050000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p419 tp420 Rp421 g361 F0.0 tp422 a(I1060000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p423 tp424 Rp425 g361 F0.0 tp426 a(I1070000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p427 tp428 Rp429 g361 F0.0 tp430 a(I1080000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p431 tp432 Rp433 g361 F0.0 tp434 a(I1090000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p435 tp436 Rp437 g361 F0.0 tp438 a(I1100000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p439 tp440 Rp441 g361 F0.0 tp442 a(I1110000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p443 tp444 Rp445 g361 F0.0 tp446 a(I1120000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p447 tp448 Rp449 g361 F0.0 tp450 a(I1130000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p451 tp452 Rp453 g361 F0.0 tp454 a(I1140000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p455 tp456 Rp457 g361 F0.0 tp458 a(I1150000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p459 tp460 Rp461 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p462 tp463 Rp464 F0.0 tp465 a(I1160000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p466 tp467 Rp468 g464 F0.0 tp469 a(I1170000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p470 tp471 Rp472 g464 F0.0 tp473 a(I1180000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p474 tp475 Rp476 g464 F0.0 tp477 a(I1190000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p478 tp479 Rp480 g464 F0.0 tp481 a(I1200000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p482 tp483 Rp484 g464 F0.0 tp485 a(I1210000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p486 tp487 Rp488 g464 F0.0 tp489 a(I1220000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p490 tp491 Rp492 g464 F1.0 tp493 a(I1230000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p494 tp495 Rp496 g464 F0.0 tp497 a(I1240000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p498 tp499 Rp500 g464 F0.0 tp501 a(I1250000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p502 tp503 Rp504 g464 F0.0 tp505 a(I1260000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p506 tp507 Rp508 g464 F0.0 tp509 a(I1270000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p510 tp511 Rp512 g464 F0.0 tp513 a(I1280000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p514 tp515 Rp516 g464 F0.0 tp517 a(I1290000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p518 tp519 Rp520 g464 F0.0 tp521 a(I1300000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p522 tp523 Rp524 g464 F0.0 tp525 a(I1310000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p526 tp527 Rp528 g464 F0.0 tp529 a(I1320000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p530 tp531 Rp532 g464 F0.0 tp533 a(I1330000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p534 tp535 Rp536 g464 F0.0 tp537 a(I1340000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p538 tp539 Rp540 g464 F0.0 tp541 a(I1350000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p542 tp543 Rp544 g464 F0.0 tp545 a(I1360000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p546 tp547 Rp548 g464 F0.0 tp549 a(I1370000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p550 tp551 Rp552 g464 F0.0 tp553 a(I1380000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p554 tp555 Rp556 g464 F0.0 tp557 a(I1390000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p558 tp559 Rp560 g464 F0.0 tp561 a(I1400000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p562 tp563 Rp564 g464 F0.0 tp565 a(I1410000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p566 tp567 Rp568 g464 F0.0 tp569 a(I1420000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p570 tp571 Rp572 g464 F1.0 tp573 a(I1430000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p574 tp575 Rp576 g464 F0.0 tp577 a(I1440000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p578 tp579 Rp580 g464 F0.0 tp581 a(I1450000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p582 tp583 Rp584 g464 F0.0 tp585 a(I1460000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p586 tp587 Rp588 g464 F0.0 tp589 a(I1470000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p590 tp591 Rp592 g464 F0.0 tp593 a(I1480000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p594 tp595 Rp596 g464 F0.0 tp597 a(I1490000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p598 tp599 Rp600 g464 F0.0 tp601 a(I1500000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p602 tp603 Rp604 g464 F0.0 tp605 a(I1510000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p606 tp607 Rp608 g464 F0.0 tp609 a(I1520000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p610 tp611 Rp612 g464 F0.0 tp613 a(I1530000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p614 tp615 Rp616 g464 F0.0 tp617 a(I1540000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p618 tp619 Rp620 g464 F0.0 tp621 a(I1550000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p622 tp623 Rp624 g464 F0.0 tp625 a(I1560000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p626 tp627 Rp628 g464 F0.0 tp629 a(I1570000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p630 tp631 Rp632 g464 F0.0 tp633 a(I1580000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p634 tp635 Rp636 g464 F0.0 tp637 a(I1590000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p638 tp639 Rp640 g464 F0.0 tp641 a(I1600000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p642 tp643 Rp644 g464 F0.0 tp645 a(I1610000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p646 tp647 Rp648 g464 F0.0 tp649 a(I1620000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p650 tp651 Rp652 g464 F0.0 tp653 a(I1630000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p654 tp655 Rp656 g464 F0.0 tp657 a(I1640000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p658 tp659 Rp660 g464 F0.0 tp661 a(I1650000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p662 tp663 Rp664 g464 F0.0 tp665 a(I1660000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p666 tp667 Rp668 g464 F0.0 tp669 a(I1670000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p670 tp671 Rp672 g464 F0.0 tp673 a(I1680000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p674 tp675 Rp676 g464 F0.0 tp677 a(I1690000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p678 tp679 Rp680 g464 F0.0 tp681 a(I1700000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p682 tp683 Rp684 g464 F0.0 tp685 a(I1710000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p686 tp687 Rp688 g464 F0.0 tp689 a(I1720000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p690 tp691 Rp692 g464 F0.0 tp693 a(I1730000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p694 tp695 Rp696 g464 F0.0 tp697 a(I1740000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p698 tp699 Rp700 g464 F0.0 tp701 a(I1750000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p702 tp703 Rp704 g464 F0.0 tp705 a(I1760000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p706 tp707 Rp708 g464 F0.0 tp709 a(I1770000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p710 tp711 Rp712 g464 F0.0 tp713 a(I1780000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p714 tp715 Rp716 g464 F0.0 tp717 a(I1790000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p718 tp719 Rp720 g464 F0.0 tp721 a(I1800000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p722 tp723 Rp724 g464 F0.0 tp725 a(I1810000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p726 tp727 Rp728 g464 F0.0 tp729 a(I1820000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p730 tp731 Rp732 g464 F0.0 tp733 a(I1830000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p734 tp735 Rp736 g464 F0.0 tp737 a(I1840000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p738 tp739 Rp740 g464 F0.0 tp741 a(I1850000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p742 tp743 Rp744 g464 F0.0 tp745 a(I1860000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p746 tp747 Rp748 g464 F0.0 tp749 a(I1870000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p750 tp751 Rp752 g464 F0.0 tp753 a(I1880000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p754 tp755 Rp756 g464 F0.0 tp757 a(I1890000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p758 tp759 Rp760 g464 F0.0 tp761 a(I1900000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p762 tp763 Rp764 g464 F0.0 tp765 a(I1910000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p766 tp767 Rp768 g464 F0.0 tp769 a(I1920000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p770 tp771 Rp772 g464 F0.0 tp773 a(I1930000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p774 tp775 Rp776 g464 F0.0 tp777 a(I1940000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p778 tp779 Rp780 g464 F0.0 tp781 a(I1950000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p782 tp783 Rp784 g464 F0.0 tp785 a(I1960000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p786 tp787 Rp788 g464 F0.0 tp789 a(I1970000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p790 tp791 Rp792 g464 F0.0 tp793 a(I1980000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p794 tp795 Rp796 g464 F0.0 tp797 a(I1990000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p798 tp799 Rp800 g464 F0.0 tp801 a(I2000000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p802 tp803 Rp804 g464 F0.0 tp805 a(I2010000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p806 tp807 Rp808 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p809 tp810 Rp811 F2.0 tp812 a(I2020000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p813 tp814 Rp815 g811 F0.0 tp816 a(I2030000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p817 tp818 Rp819 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p820 tp821 Rp822 F1.0 tp823 a(I2040000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p824 tp825 Rp826 g822 F0.0 tp827 a(I2050000 g1 (g5 S'\n\xd7\xa3p=\n\xb7?' p828 tp829 Rp830 g1 (g5 S'\n\xd7\xa3p=\n\xb7?' p831 tp832 Rp833 F1.0 tp834 a(I2060000 g1 (g5 S'\n\xd7\xa3p=\n\xb7?' p835 tp836 Rp837 g833 F0.0 tp838 a(I2070000 g1 (g5 S'\n\xd7\xa3p=\n\xb7?' p839 tp840 Rp841 g833 F0.0 tp842 a(I2080000 g1 (g5 S'\n\xd7\xa3p=\n\xb7?' p843 tp844 Rp845 g833 F0.0 tp846 a(I2090000 g1 (g5 S'\n\xd7\xa3p=\n\xb7?' p847 tp848 Rp849 g833 F0.0 tp850 a(I2100000 g1 (g5 S'\n\xd7\xa3p=\n\xb7?' p851 tp852 Rp853 g833 F0.0 tp854 a(I2110000 g1 (g5 S'\n\xd7\xa3p=\n\xb7?' p855 tp856 Rp857 g833 F0.0 tp858 a(I2120000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p859 tp860 Rp861 g833 F0.0 tp862 a(I2130000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p863 tp864 Rp865 g833 F0.0 tp866 a(I2140000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p867 tp868 Rp869 g833 F0.0 tp870 a(I2150000 g1 (g5 S'\n\xd7\xa3p=\n\xb7?' p871 tp872 Rp873 g833 F1.0 tp874 a(I2160000 g1 (g5 S'\n\xd7\xa3p=\n\xb7?' p875 tp876 Rp877 g833 F0.0 tp878 a(I2170000 g1 (g5 S'\n\xd7\xa3p=\n\xb7?' p879 tp880 Rp881 g833 F0.0 tp882 a(I2180000 g1 (g5 S'\n\xd7\xa3p=\n\xb7?' p883 tp884 Rp885 g833 F0.0 tp886 a(I2190000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p887 tp888 Rp889 g833 F0.0 tp890 a(I2200000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p891 tp892 Rp893 g833 F0.0 tp894 a(I2210000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p895 tp896 Rp897 g833 F0.0 tp898 a(I2220000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p899 tp900 Rp901 g833 F0.0 tp902 a(I2230000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p903 tp904 Rp905 g833 F0.0 tp906 a(I2240000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p907 tp908 Rp909 g833 F0.0 tp910 a(I2250000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p911 tp912 Rp913 g833 F0.0 tp914 a(I2260000 g1 (g5 S'\n\xd7\xa3p=\n\xb7?' p915 tp916 Rp917 g833 F0.0 tp918 a(I2270000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xb9?' p919 tp920 Rp921 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xb9?' p922 tp923 Rp924 F0.0 tp925 a(I2280000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xb9?' p926 tp927 Rp928 g924 F0.0 tp929 a(I2290000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xb9?' p930 tp931 Rp932 g924 F0.0 tp933 a(I2300000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p934 tp935 Rp936 g924 F0.0 tp937 a(I2310000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p938 tp939 Rp940 g924 F0.0 tp941 a(I2320000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p942 tp943 Rp944 g924 F0.0 tp945 a(I2330000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p946 tp947 Rp948 g924 F1.0 tp949 a(I2340000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p950 tp951 Rp952 g924 F0.0 tp953 a(I2350000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p954 tp955 Rp956 g924 F0.0 tp957 a(I2360000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p958 tp959 Rp960 g924 F0.0 tp961 a(I2370000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p962 tp963 Rp964 g924 F0.0 tp965 a(I2380000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p966 tp967 Rp968 g924 F0.0 tp969 a(I2390000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p970 tp971 Rp972 g924 F0.0 tp973 a(I2400000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p974 tp975 Rp976 g924 F0.0 tp977 a(I2410000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p978 tp979 Rp980 g924 F0.0 tp981 a(I2420000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p982 tp983 Rp984 g924 F0.0 tp985 a(I2430000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p986 tp987 Rp988 g924 F0.0 tp989 a(I2440000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p990 tp991 Rp992 g924 F0.0 tp993 a(I2450000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p994 tp995 Rp996 g924 F0.0 tp997 a(I2460000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p998 tp999 Rp1000 g924 F0.0 tp1001 a(I2470000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1002 tp1003 Rp1004 g924 F0.0 tp1005 a(I2480000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p1006 tp1007 Rp1008 g924 F0.0 tp1009 a(I2490000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p1010 tp1011 Rp1012 g924 F1.0 tp1013 a(I2500000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p1014 tp1015 Rp1016 g924 F0.0 tp1017 a(I2510000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p1018 tp1019 Rp1020 g924 F0.0 tp1021 a(I2520000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p1022 tp1023 Rp1024 g924 F0.0 tp1025 a(I2530000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p1026 tp1027 Rp1028 g924 F0.0 tp1029 a(I2540000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p1030 tp1031 Rp1032 g924 F0.0 tp1033 a(I2550000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p1034 tp1035 Rp1036 g924 F0.0 tp1037 a(I2560000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p1038 tp1039 Rp1040 g924 F0.0 tp1041 a(I2570000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p1042 tp1043 Rp1044 g924 F0.0 tp1045 a(I2580000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p1046 tp1047 Rp1048 g924 F0.0 tp1049 a(I2590000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1050 tp1051 Rp1052 g924 F0.0 tp1053 a(I2600000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1054 tp1055 Rp1056 g924 F0.0 tp1057 a(I2610000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1058 tp1059 Rp1060 g924 F0.0 tp1061 a(I2620000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1062 tp1063 Rp1064 g924 F0.0 tp1065 a(I2630000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1066 tp1067 Rp1068 g924 F0.0 tp1069 a(I2640000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1070 tp1071 Rp1072 g924 F0.0 tp1073 a(I2650000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p1074 tp1075 Rp1076 g924 F2.0 tp1077 a(I2660000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p1078 tp1079 Rp1080 g924 F0.0 tp1081 a(I2670000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p1082 tp1083 Rp1084 g924 F1.0 tp1085 a(I2680000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p1086 tp1087 Rp1088 g924 F0.0 tp1089 a(I2690000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p1090 tp1091 Rp1092 g924 F0.0 tp1093 a(I2700000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p1094 tp1095 Rp1096 g924 F0.0 tp1097 a(I2710000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p1098 tp1099 Rp1100 g924 F0.0 tp1101 a(I2720000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p1102 tp1103 Rp1104 g924 F0.0 tp1105 a(I2730000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p1106 tp1107 Rp1108 g924 F0.0 tp1109 a(I2740000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p1110 tp1111 Rp1112 g924 F0.0 tp1113 a(I2750000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p1114 tp1115 Rp1116 g924 F0.0 tp1117 a(I2760000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p1118 tp1119 Rp1120 g924 F0.0 tp1121 a(I2770000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p1122 tp1123 Rp1124 g924 F0.0 tp1125 a(I2780000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p1126 tp1127 Rp1128 g924 F0.0 tp1129 a(I2790000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p1130 tp1131 Rp1132 g924 F0.0 tp1133 a(I2800000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p1134 tp1135 Rp1136 g924 F0.0 tp1137 a(I2810000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p1138 tp1139 Rp1140 g924 F0.0 tp1141 a(I2820000 g1 (g5 S'\n\xd7\xa3p=\n\xb7?' p1142 tp1143 Rp1144 g924 F3.0 tp1145 a(I2830000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p1146 tp1147 Rp1148 g924 F0.0 tp1149 a(I2840000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p1150 tp1151 Rp1152 g924 F0.0 tp1153 a(I2850000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p1154 tp1155 Rp1156 g924 F0.0 tp1157 a(I2860000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p1158 tp1159 Rp1160 g924 F0.0 tp1161 a(I2870000 g1 (g5 S'\n\xd7\xa3p=\n\xb7?' p1162 tp1163 Rp1164 g924 F0.0 tp1165 a(I2880000 g1 (g5 S'\n\xd7\xa3p=\n\xb7?' p1166 tp1167 Rp1168 g924 F0.0 tp1169 a(I2890000 g1 (g5 S'\n\xd7\xa3p=\n\xb7?' p1170 tp1171 Rp1172 g924 F0.0 tp1173 a(I2900000 g1 (g5 S'\n\xd7\xa3p=\n\xb7?' p1174 tp1175 Rp1176 g924 F0.0 tp1177 a(I2910000 g1 (g5 S'\n\xd7\xa3p=\n\xb7?' p1178 tp1179 Rp1180 g924 F0.0 tp1181 a(I2920000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xbe?' p1182 tp1183 Rp1184 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xbe?' p1185 tp1186 Rp1187 F0.0 tp1188 a(I2930000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xbe?' p1189 tp1190 Rp1191 g1187 F0.0 tp1192 a(I2940000 g1 (g5 S'\xa4p=\n\xd7\xa3\xc0?' p1193 tp1194 Rp1195 g1 (g5 S'\xa4p=\n\xd7\xa3\xc0?' p1196 tp1197 Rp1198 F1.0 tp1199 a(I2950000 g1 (g5 S'\xa4p=\n\xd7\xa3\xc0?' p1200 tp1201 Rp1202 g1198 F0.0 tp1203 a(I2960000 g1 (g5 S'\xa4p=\n\xd7\xa3\xc0?' p1204 tp1205 Rp1206 g1198 F0.0 tp1207 a(I2970000 g1 (g5 S'\xa4p=\n\xd7\xa3\xc0?' p1208 tp1209 Rp1210 g1198 F0.0 tp1211 a(I2980000 g1 (g5 S'\xa4p=\n\xd7\xa3\xc0?' p1212 tp1213 Rp1214 g1198 F0.0 tp1215 a(I2990000 g1 (g5 S'\xa4p=\n\xd7\xa3\xc0?' p1216 tp1217 Rp1218 g1198 F1.0 tp1219 a(I3000000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xbe?' p1220 tp1221 Rp1222 g1198 F0.0 tp1223 a(I3010000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p1224 tp1225 Rp1226 g1198 F0.0 tp1227 a(I3020000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p1228 tp1229 Rp1230 g1198 F0.0 tp1231 a(I3030000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p1232 tp1233 Rp1234 g1198 F0.0 tp1235 a(I3040000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p1236 tp1237 Rp1238 g1198 F0.0 tp1239 a(I3050000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p1240 tp1241 Rp1242 g1198 F0.0 tp1243 a(I3060000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p1244 tp1245 Rp1246 g1198 F0.0 tp1247 a(I3070000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p1248 tp1249 Rp1250 g1198 F0.0 tp1251 a(I3080000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xbe?' p1252 tp1253 Rp1254 g1198 F1.0 tp1255 a(I3090000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xbe?' p1256 tp1257 Rp1258 g1198 F0.0 tp1259 a(I3100000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xbe?' p1260 tp1261 Rp1262 g1198 F0.0 tp1263 a(I3110000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xbe?' p1264 tp1265 Rp1266 g1198 F0.0 tp1267 a(I3120000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xbe?' p1268 tp1269 Rp1270 g1198 F0.0 tp1271 a(I3130000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xbe?' p1272 tp1273 Rp1274 g1198 F0.0 tp1275 a(I3140000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xbe?' p1276 tp1277 Rp1278 g1198 F0.0 tp1279 a(I3150000 g1 (g5 S'\n\xd7\xa3p=\n\xb7?' p1280 tp1281 Rp1282 g1198 F0.0 tp1283 a(I3160000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xb9?' p1284 tp1285 Rp1286 g1198 F1.0 tp1287 a(I3170000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xb9?' p1288 tp1289 Rp1290 g1198 F0.0 tp1291 a(I3180000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xbe?' p1292 tp1293 Rp1294 g1198 F0.0 tp1295 a(I3190000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xbe?' p1296 tp1297 Rp1298 g1198 F0.0 tp1299 a(I3200000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p1300 tp1301 Rp1302 g1198 F0.0 tp1303 a(I3210000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p1304 tp1305 Rp1306 g1198 F0.0 tp1307 a(I3220000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p1308 tp1309 Rp1310 g1198 F0.0 tp1311 a(I3230000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p1312 tp1313 Rp1314 g1198 F0.0 tp1315 a(I3240000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p1316 tp1317 Rp1318 g1198 F0.0 tp1319 a(I3250000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p1320 tp1321 Rp1322 g1198 F0.0 tp1323 a(I3260000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p1324 tp1325 Rp1326 g1198 F0.0 tp1327 a(I3270000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p1328 tp1329 Rp1330 g1198 F0.0 tp1331 a(I3280000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p1332 tp1333 Rp1334 g1198 F0.0 tp1335 a(I3290000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p1336 tp1337 Rp1338 g1198 F0.0 tp1339 a(I3300000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p1340 tp1341 Rp1342 g1198 F0.0 tp1343 a(I3310000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p1344 tp1345 Rp1346 g1198 F0.0 tp1347 a(I3320000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1348 tp1349 Rp1350 g1198 F0.0 tp1351 a(I3330000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1352 tp1353 Rp1354 g1198 F0.0 tp1355 a(I3340000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1356 tp1357 Rp1358 g1198 F0.0 tp1359 a(I3350000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1360 tp1361 Rp1362 g1198 F0.0 tp1363 a(I3360000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1364 tp1365 Rp1366 g1198 F0.0 tp1367 a(I3370000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1368 tp1369 Rp1370 g1198 F0.0 tp1371 a(I3380000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p1372 tp1373 Rp1374 g1198 F1.0 tp1375 a(I3390000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p1376 tp1377 Rp1378 g1198 F0.0 tp1379 a(I3400000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1380 tp1381 Rp1382 g1198 F0.0 tp1383 a(I3410000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1384 tp1385 Rp1386 g1198 F0.0 tp1387 a(I3420000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1388 tp1389 Rp1390 g1198 F0.0 tp1391 a(I3430000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1392 tp1393 Rp1394 g1198 F0.0 tp1395 a(I3440000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1396 tp1397 Rp1398 g1198 F1.0 tp1399 a(I3450000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p1400 tp1401 Rp1402 g1198 F0.0 tp1403 a(I3460000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p1404 tp1405 Rp1406 g1198 F0.0 tp1407 a(I3470000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p1408 tp1409 Rp1410 g1198 F0.0 tp1411 a(I3480000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p1412 tp1413 Rp1414 g1198 F0.0 tp1415 a(I3490000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xc1?' p1416 tp1417 Rp1418 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xc1?' p1419 tp1420 Rp1421 F0.0 tp1422 a(I3500000 g1 (g5 S'\xa4p=\n\xd7\xa3\xc0?' p1423 tp1424 Rp1425 g1421 F0.0 tp1426 a(I3510000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xbe?' p1427 tp1428 Rp1429 g1421 F0.0 tp1430 a(I3520000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xbe?' p1431 tp1432 Rp1433 g1421 F0.0 tp1434 a(I3530000 g1 (g5 S'{\x14\xaeG\xe1z\xc4?' p1435 tp1436 Rp1437 g1 (g5 S'{\x14\xaeG\xe1z\xc4?' p1438 tp1439 Rp1440 F0.0 tp1441 a(I3540000 g1 (g5 S'{\x14\xaeG\xe1z\xc4?' p1442 tp1443 Rp1444 g1440 F0.0 tp1445 a(I3550000 g1 (g5 S'{\x14\xaeG\xe1z\xc4?' p1446 tp1447 Rp1448 g1440 F0.0 tp1449 a(I3560000 g1 (g5 S'{\x14\xaeG\xe1z\xc4?' p1450 tp1451 Rp1452 g1440 F0.0 tp1453 a(I3570000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xc9?' p1454 tp1455 Rp1456 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xc9?' p1457 tp1458 Rp1459 F2.0 tp1460 a(I3580000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xc9?' p1461 tp1462 Rp1463 g1459 F0.0 tp1464 a(I3590000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xc9?' p1465 tp1466 Rp1467 g1459 F0.0 tp1468 a(I3600000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xc9?' p1469 tp1470 Rp1471 g1459 F0.0 tp1472 a(I3610000 g1 (g5 S'\xe1z\x14\xaeG\xe1\xca?' p1473 tp1474 Rp1475 g1 (g5 S'\xe1z\x14\xaeG\xe1\xca?' p1476 tp1477 Rp1478 F0.0 tp1479 a(I3620000 g1 (g5 S'\xe1z\x14\xaeG\xe1\xca?' p1480 tp1481 Rp1482 g1478 F0.0 tp1483 a(I3630000 g1 (g5 S'\xe1z\x14\xaeG\xe1\xca?' p1484 tp1485 Rp1486 g1478 F0.0 tp1487 a(I3640000 g1 (g5 S'\xe1z\x14\xaeG\xe1\xca?' p1488 tp1489 Rp1490 g1478 F0.0 tp1491 a(I3650000 g1 (g5 S'\xe1z\x14\xaeG\xe1\xca?' p1492 tp1493 Rp1494 g1478 F0.0 tp1495 a(I3660000 g1 (g5 S'\xe1z\x14\xaeG\xe1\xca?' p1496 tp1497 Rp1498 g1478 F0.0 tp1499 a(I3670000 g1 (g5 S'q=\n\xd7\xa3p\xcd?' p1500 tp1501 Rp1502 g1 (g5 S'q=\n\xd7\xa3p\xcd?' p1503 tp1504 Rp1505 F0.0 tp1506 a(I3680000 g1 (g5 S'q=\n\xd7\xa3p\xcd?' p1507 tp1508 Rp1509 g1505 F0.0 tp1510 a(I3690000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xce?' p1511 tp1512 Rp1513 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xce?' p1514 tp1515 Rp1516 F1.0 tp1517 a(I3700000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xce?' p1518 tp1519 Rp1520 g1516 F0.0 tp1521 a(I3710000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xce?' p1522 tp1523 Rp1524 g1516 F0.0 tp1525 a(I3720000 g1 (g5 S'q=\n\xd7\xa3p\xcd?' p1526 tp1527 Rp1528 g1516 F0.0 tp1529 a(I3730000 g1 (g5 S'q=\n\xd7\xa3p\xcd?' p1530 tp1531 Rp1532 g1516 F0.0 tp1533 a(I3740000 g1 (g5 S'q=\n\xd7\xa3p\xcd?' p1534 tp1535 Rp1536 g1516 F0.0 tp1537 a(I3750000 g1 (g5 S'\xa4p=\n\xd7\xa3\xd0?' p1538 tp1539 Rp1540 g1 (g5 S'\xa4p=\n\xd7\xa3\xd0?' p1541 tp1542 Rp1543 F3.0 tp1544 a(I3760000 g1 (g5 S'\xa4p=\n\xd7\xa3\xd0?' p1545 tp1546 Rp1547 g1543 F0.0 tp1548 a(I3770000 g1 (g5 S'\xa4p=\n\xd7\xa3\xd0?' p1549 tp1550 Rp1551 g1543 F0.0 tp1552 a(I3780000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xce?' p1553 tp1554 Rp1555 g1543 F0.0 tp1556 a(I3790000 g1 (g5 S')\\\x8f\xc2\xf5(\xcc?' p1557 tp1558 Rp1559 g1543 F0.0 tp1560 a(I3800000 g1 (g5 S'R\xb8\x1e\x85\xebQ\xc8?' p1561 tp1562 Rp1563 g1543 F0.0 tp1564 a(I3810000 g1 (g5 S'\xe1z\x14\xaeG\xe1\xca?' p1565 tp1566 Rp1567 g1543 F2.0 tp1568 a(I3820000 g1 (g5 S'\n\xd7\xa3p=\n\xc7?' p1569 tp1570 Rp1571 g1543 F0.0 tp1572 a(I3830000 g1 (g5 S'\xe1z\x14\xaeG\xe1\xca?' p1573 tp1574 Rp1575 g1543 F3.0 tp1576 a(I3840000 g1 (g5 S'\xe1z\x14\xaeG\xe1\xca?' p1577 tp1578 Rp1579 g1543 F0.0 tp1580 a(I3850000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xce?' p1581 tp1582 Rp1583 g1543 F0.0 tp1584 a(I3860000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xc9?' p1585 tp1586 Rp1587 g1543 F0.0 tp1588 a(I3870000 g1 (g5 S'\xe1z\x14\xaeG\xe1\xca?' p1589 tp1590 Rp1591 g1543 F0.0 tp1592 a(I3880000 g1 (g5 S')\\\x8f\xc2\xf5(\xcc?' p1593 tp1594 Rp1595 g1543 F0.0 tp1596 a(I3890000 g1 (g5 S'q=\n\xd7\xa3p\xcd?' p1597 tp1598 Rp1599 g1543 F1.0 tp1600 a(I3900000 g1 (g5 S')\\\x8f\xc2\xf5(\xcc?' p1601 tp1602 Rp1603 g1543 F1.0 tp1604 a(I3910000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xc9?' p1605 tp1606 Rp1607 g1543 F0.0 tp1608 a(I3920000 g1 (g5 S'\xe1z\x14\xaeG\xe1\xca?' p1609 tp1610 Rp1611 g1543 F1.0 tp1612 a(I3930000 g1 (g5 S'\xe1z\x14\xaeG\xe1\xca?' p1613 tp1614 Rp1615 g1543 F0.0 tp1616 a(I3940000 g1 (g5 S')\\\x8f\xc2\xf5(\xcc?' p1617 tp1618 Rp1619 g1543 F2.0 tp1620 a(I3950000 g1 (g5 S'q=\n\xd7\xa3p\xcd?' p1621 tp1622 Rp1623 g1543 F1.0 tp1624 a(I3960000 g1 (g5 S'q=\n\xd7\xa3p\xcd?' p1625 tp1626 Rp1627 g1543 F0.0 tp1628 a(I3970000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\xd0?' p1629 tp1630 Rp1631 g1543 F2.0 tp1632 a(I3980000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\xd0?' p1633 tp1634 Rp1635 g1543 F0.0 tp1636 a(I3990000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\xd0?' p1637 tp1638 Rp1639 g1543 F0.0 tp1640 a(I4000000 g1 (g5 S'q=\n\xd7\xa3p\xcd?' p1641 tp1642 Rp1643 g1543 F0.0 tp1644 a(I4010000 g1 (g5 S'q=\n\xd7\xa3p\xcd?' p1645 tp1646 Rp1647 g1543 F0.0 tp1648 a(I4020000 g1 (g5 S'q=\n\xd7\xa3p\xcd?' p1649 tp1650 Rp1651 g1543 F0.0 tp1652 a(I4030000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xce?' p1653 tp1654 Rp1655 g1543 F0.0 tp1656 a(I4040000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xce?' p1657 tp1658 Rp1659 g1543 F0.0 tp1660 a(I4050000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xce?' p1661 tp1662 Rp1663 g1543 F0.0 tp1664 a(I4060000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xce?' p1665 tp1666 Rp1667 g1543 F0.0 tp1668 a(I4070000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xce?' p1669 tp1670 Rp1671 g1543 F0.0 tp1672 a(I4080000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xce?' p1673 tp1674 Rp1675 g1543 F0.0 tp1676 a(I4090000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\xd0?' p1677 tp1678 Rp1679 g1543 F4.0 tp1680 a(I4100000 g1 (g5 S'H\xe1z\x14\xaeG\xd1?' p1681 tp1682 Rp1683 g1 (g5 S'H\xe1z\x14\xaeG\xd1?' p1684 tp1685 Rp1686 F0.0 tp1687 a(I4110000 g1 (g5 S'H\xe1z\x14\xaeG\xd1?' p1688 tp1689 Rp1690 g1686 F0.0 tp1691 a(I4120000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f\xd2?' p1692 tp1693 Rp1694 g1 (g5 S'\x8f\xc2\xf5(\\\x8f\xd2?' p1695 tp1696 Rp1697 F2.0 tp1698 a(I4130000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f\xd2?' p1699 tp1700 Rp1701 g1697 F0.0 tp1702 a(I4140000 g1 (g5 S'H\xe1z\x14\xaeG\xd1?' p1703 tp1704 Rp1705 g1697 F0.0 tp1706 a(I4150000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xd1?' p1707 tp1708 Rp1709 g1697 F0.0 tp1710 a(I4160000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\xd0?' p1711 tp1712 Rp1713 g1697 F0.0 tp1714 a(I4170000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xd1?' p1715 tp1716 Rp1717 g1697 F4.0 tp1718 a(I4180000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\xd0?' p1719 tp1720 Rp1721 g1697 F0.0 tp1722 a(I4190000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\xd0?' p1723 tp1724 Rp1725 g1697 F0.0 tp1726 a(I4200000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xce?' p1727 tp1728 Rp1729 g1697 F0.0 tp1730 a(I4210000 g1 (g5 S'q=\n\xd7\xa3p\xcd?' p1731 tp1732 Rp1733 g1697 F0.0 tp1734 a(I4220000 g1 (g5 S')\\\x8f\xc2\xf5(\xcc?' p1735 tp1736 Rp1737 g1697 F0.0 tp1738 a(I4230000 g1 (g5 S'q=\n\xd7\xa3p\xcd?' p1739 tp1740 Rp1741 g1697 F0.0 tp1742 a(I4240000 g1 (g5 S'H\xe1z\x14\xaeG\xd1?' p1743 tp1744 Rp1745 g1697 F4.0 tp1746 a(I4250000 g1 (g5 S'\xa4p=\n\xd7\xa3\xd0?' p1747 tp1748 Rp1749 g1697 F0.0 tp1750 a(I4260000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xd1?' p1751 tp1752 Rp1753 g1697 F0.0 tp1754 a(I4270000 g1 (g5 S'\xa4p=\n\xd7\xa3\xd0?' p1755 tp1756 Rp1757 g1697 F0.0 tp1758 a(I4280000 g1 (g5 S'333333\xd3?' p1759 tp1760 Rp1761 g1 (g5 S'333333\xd3?' p1762 tp1763 Rp1764 F3.0 tp1765 a(I4290000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xe1?' p1766 tp1767 Rp1768 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xe1?' p1769 tp1770 Rp1771 F3.0 tp1772 a(I4300000 g1 (g5 S'\xf6(\\\x8f\xc2\xf5\xe0?' p1773 tp1774 Rp1775 g1771 F0.0 tp1776 a(I4310000 g1 (g5 S'\xf6(\\\x8f\xc2\xf5\xe0?' p1777 tp1778 Rp1779 g1771 F0.0 tp1780 a(I4320000 g1 (g5 S'H\xe1z\x14\xaeG\xe1?' p1781 tp1782 Rp1783 g1771 F0.0 tp1784 a(I4330000 g1 (g5 S'H\xe1z\x14\xaeG\xe1?' p1785 tp1786 Rp1787 g1771 F0.0 tp1788 a(I4340000 g1 (g5 S'\xd7\xa3p=\n\xd7\xe3?' p1789 tp1790 Rp1791 g1 (g5 S'\xd7\xa3p=\n\xd7\xe3?' p1792 tp1793 Rp1794 F3.0 tp1795 a(I4350000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc\xe4?' p1796 tp1797 Rp1798 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc\xe4?' p1799 tp1800 Rp1801 F3.0 tp1802 a(I4360000 g1 (g5 S'{\x14\xaeG\xe1z\xe4?' p1803 tp1804 Rp1805 g1801 F1.0 tp1806 a(I4370000 g1 (g5 S'q=\n\xd7\xa3p\xe5?' p1807 tp1808 Rp1809 g1 (g5 S'q=\n\xd7\xa3p\xe5?' p1810 tp1811 Rp1812 F0.0 tp1813 a(I4380000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\xf0?' p1814 tp1815 Rp1816 g1 (g5 S'\x00\x00\x00\x00\x00\x00\xf0?' p1817 tp1818 Rp1819 F21.0 tp1820 a(I4390000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xf1?' p1821 tp1822 Rp1823 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xf1?' p1824 tp1825 Rp1826 F0.0 tp1827 a(I4400000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xf1?' p1828 tp1829 Rp1830 g1826 F0.0 tp1831 a(I4410000 g1 (g5 S'\n\xd7\xa3p=\n\xf3?' p1832 tp1833 Rp1834 g1 (g5 S'\n\xd7\xa3p=\n\xf3?' p1835 tp1836 Rp1837 F3.0 tp1838 a(I4420000 g1 (g5 S'\xe1z\x14\xaeG\xe1\xf2?' p1839 tp1840 Rp1841 g1 (g5 S'\x85\xebQ\xb8\x1e\x85\xf3?' p1842 tp1843 Rp1844 F0.0 tp1845 a(I4430000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc\xf4?' p1846 tp1847 Rp1848 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc\xf4?' p1849 tp1850 Rp1851 F5.0 tp1852 a(I4440000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xf5?' p1853 tp1854 Rp1855 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xf5?' p1856 tp1857 Rp1858 F0.0 tp1859 a(I4450000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xf5?' p1860 tp1861 Rp1862 g1858 F0.0 tp1863 a(I4460000 g1 (g5 S'q=\n\xd7\xa3p\xf5?' p1864 tp1865 Rp1866 g1858 F1.0 tp1867 a(I4470000 g1 (g5 S'\x14\xaeG\xe1z\x14\xf6?' p1868 tp1869 Rp1870 g1 (g5 S'\x14\xaeG\xe1z\x14\xf6?' p1871 tp1872 Rp1873 F1.0 tp1874 a(I4480000 g1 (g5 S'\n\xd7\xa3p=\n\xf7?' p1875 tp1876 Rp1877 g1 (g5 S'\n\xd7\xa3p=\n\xf7?' p1878 tp1879 Rp1880 F1.0 tp1881 a(I4490000 g1 (g5 S'\x14\xaeG\xe1z\x14\xfa?' p1882 tp1883 Rp1884 g1 (g5 S'\x14\xaeG\xe1z\x14\xfa?' p1885 tp1886 Rp1887 F3.0 tp1888 a(I4500000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f\xfa?' p1889 tp1890 Rp1891 g1 (g5 S'\x8f\xc2\xf5(\\\x8f\xfa?' p1892 tp1893 Rp1894 F0.0 tp1895 a(I4510000 g1 (g5 S'=\n\xd7\xa3p=\xfa?' p1896 tp1897 Rp1898 g1894 F1.0 tp1899 a(I4520000 g1 (g5 S'\xaeG\xe1z\x14\xae\xfb?' p1900 tp1901 Rp1902 g1 (g5 S'\xaeG\xe1z\x14\xae\xfb?' p1903 tp1904 Rp1905 F7.0 tp1906 a(I4530000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xfe?' p1907 tp1908 Rp1909 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xfe?' p1910 tp1911 Rp1912 F0.0 tp1913 a(I4540000 g1 (g5 S'333333\xff?' p1914 tp1915 Rp1916 g1 (g5 S'333333\xff?' p1917 tp1918 Rp1919 F2.0 tp1920 a(I4550000 g1 (g5 S'\xc3\xf5(\\\x8f\xc2\x01@' p1921 tp1922 Rp1923 g1 (g5 S'\xc3\xf5(\\\x8f\xc2\x01@' p1924 tp1925 Rp1926 F9.0 tp1927 a(I4560000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x02@' p1928 tp1929 Rp1930 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x02@' p1931 tp1932 Rp1933 F3.0 tp1934 a(I4570000 g1 (g5 S')\\\x8f\xc2\xf5(\x02@' p1935 tp1936 Rp1937 g1 (g5 S')\\\x8f\xc2\xf5(\x02@' p1938 tp1939 Rp1940 F0.0 tp1941 a(I4580000 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e\x03@' p1942 tp1943 Rp1944 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e\x03@' p1945 tp1946 Rp1947 F0.0 tp1948 a(I4590000 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e\x03@' p1949 tp1950 Rp1951 g1 (g5 S'333333\x03@' p1952 tp1953 Rp1954 F1.0 tp1955 a(I4600000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\x03@' p1956 tp1957 Rp1958 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\x03@' p1959 tp1960 Rp1961 F0.0 tp1962 a(I4610000 g1 (g5 S'q=\n\xd7\xa3p\x03@' p1963 tp1964 Rp1965 g1961 F0.0 tp1966 a(I4620000 g1 (g5 S'\xaeG\xe1z\x14\xae\x01@' p1967 tp1968 Rp1969 g1961 F1.0 tp1970 a(I4630000 g1 (g5 S'\xaeG\xe1z\x14\xae\x01@' p1971 tp1972 Rp1973 g1961 F3.0 tp1974 a(I4640000 g1 (g5 S'R\xb8\x1e\x85\xebQ\x02@' p1975 tp1976 Rp1977 g1961 F1.0 tp1978 a(I4650000 g1 (g5 S'R\xb8\x1e\x85\xebQ\x02@' p1979 tp1980 Rp1981 g1961 F0.0 tp1982 a(I4660000 g1 (g5 S'ffffff\x02@' p1983 tp1984 Rp1985 g1961 F0.0 tp1986 a(I4670000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x02@' p1987 tp1988 Rp1989 g1961 F0.0 tp1990 a(I4680000 g1 (g5 S')\\\x8f\xc2\xf5(\x02@' p1991 tp1992 Rp1993 g1961 F0.0 tp1994 a(I4690000 g1 (g5 S'=\n\xd7\xa3p=\x02@' p1995 tp1996 Rp1997 g1961 F4.0 tp1998 a(I4700000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\x01@' p1999 tp2000 Rp2001 g1961 F0.0 tp2002 a(I4710000 g1 (g5 S'\n\xd7\xa3p=\n\x01@' p2003 tp2004 Rp2005 g1961 F0.0 tp2006 a(I4720000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xfd?' p2007 tp2008 Rp2009 g1961 F0.0 tp2010 a(I4730000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00@' p2011 tp2012 Rp2013 g1961 F14.0 tp2014 a(I4740000 g1 (g5 S'\n\xd7\xa3p=\n\x01@' p2015 tp2016 Rp2017 g1961 F6.0 tp2018 a(I4750000 g1 (g5 S'\xf6(\\\x8f\xc2\xf5\x02@' p2019 tp2020 Rp2021 g1961 F0.0 tp2022 a(I4760000 g1 (g5 S'\xe1z\x14\xaeG\xe1\x02@' p2023 tp2024 Rp2025 g1961 F7.0 tp2026 a(I4770000 g1 (g5 S'\xf6(\\\x8f\xc2\xf5\x02@' p2027 tp2028 Rp2029 g1961 F1.0 tp2030 a(I4780000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f\x04@' p2031 tp2032 Rp2033 g1 (g5 S'\x8f\xc2\xf5(\\\x8f\x04@' p2034 tp2035 Rp2036 F5.0 tp2037 a(I4790000 g1 (g5 S'ffffff\x06@' p2038 tp2039 Rp2040 g1 (g5 S'ffffff\x06@' p2041 tp2042 Rp2043 F23.0 tp2044 a(I4800000 g1 (g5 S'\n\xd7\xa3p=\n\x07@' p2045 tp2046 Rp2047 g1 (g5 S'\n\xd7\xa3p=\n\x07@' p2048 tp2049 Rp2050 F12.0 tp2051 a(I4810000 g1 (g5 S'\xaeG\xe1z\x14\xae\x07@' p2052 tp2053 Rp2054 g1 (g5 S'\xaeG\xe1z\x14\xae\x07@' p2055 tp2056 Rp2057 F1.0 tp2058 a(I4820000 g1 (g5 S'ffffff\x06@' p2059 tp2060 Rp2061 g2057 F0.0 tp2062 a(I4830000 g1 (g5 S')\\\x8f\xc2\xf5(\x08@' p2063 tp2064 Rp2065 g1 (g5 S')\\\x8f\xc2\xf5(\x08@' p2066 tp2067 Rp2068 F0.0 tp2069 a(I4840000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x0c@' p2070 tp2071 Rp2072 g1 (g5 S'\x14\xaeG\xe1z\x14\x0c@' p2073 tp2074 Rp2075 F0.0 tp2076 a(I4850000 g1 (g5 S'\x85\xebQ\xb8\x1e\x85\x0f@' p2077 tp2078 Rp2079 g1 (g5 S'\x85\xebQ\xb8\x1e\x85\x0f@' p2080 tp2081 Rp2082 F15.0 tp2083 a(I4860000 g1 (g5 S')\\\x8f\xc2\xf5(\x0e@' p2084 tp2085 Rp2086 g1 (g5 S'\xaeG\xe1z\x14\xae\x0f@' p2087 tp2088 Rp2089 F0.0 tp2090 a(I4870000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc\x0e@' p2091 tp2092 Rp2093 g2089 F0.0 tp2094 a(I4880000 g1 (g5 S'\x85\xebQ\xb8\x1e\x85\r@' p2095 tp2096 Rp2097 g2089 F2.0 tp2098 a(I4890000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x0e@' p2099 tp2100 Rp2101 g2089 F17.0 tp2102 a(I4900000 g1 (g5 S'\xa4p=\n\xd7\xa3\x0e@' p2103 tp2104 Rp2105 g2089 F0.0 tp2106 a(I4910000 g1 (g5 S'\n\xd7\xa3p=\n\x0f@' p2107 tp2108 Rp2109 g2089 F8.0 tp2110 a(I4920000 g1 (g5 S'333333\x0f@' p2111 tp2112 Rp2113 g2089 F0.0 tp2114 a(I4930000 g1 (g5 S'\xf6(\\\x8f\xc2\xf5\x0e@' p2115 tp2116 Rp2117 g2089 F4.0 tp2118 a(I4940000 g1 (g5 S'H\xe1z\x14\xaeG\x0f@' p2119 tp2120 Rp2121 g2089 F0.0 tp2122 a(I4950000 g1 (g5 S'\x14\xaeG\xe1z\x14\x11@' p2123 tp2124 Rp2125 g1 (g5 S'\x14\xaeG\xe1z\x14\x11@' p2126 tp2127 Rp2128 F0.0 tp2129 a(I4960000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x13@' p2130 tp2131 Rp2132 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x13@' p2133 tp2134 Rp2135 F9.0 tp2136 a(I4970000 g1 (g5 S'\x85\xebQ\xb8\x1e\x85\x14@' p2137 tp2138 Rp2139 g1 (g5 S'\x85\xebQ\xb8\x1e\x85\x14@' p2140 tp2141 Rp2142 F13.0 tp2143 a(I4980000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\x15@' p2144 tp2145 Rp2146 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\x15@' p2147 tp2148 Rp2149 F0.0 tp2150 a(I4990000 g1 (g5 S'{\x14\xaeG\xe1z\x16@' p2151 tp2152 Rp2153 g1 (g5 S'{\x14\xaeG\xe1z\x16@' p2154 tp2155 Rp2156 F13.0 tp2157 a(I5000000 g1 (g5 S'\xa4p=\n\xd7\xa3\x1a@' p2158 tp2159 Rp2160 g1 (g5 S'\xa4p=\n\xd7\xa3\x1a@' p2161 tp2162 Rp2163 F67.0 tp2164 a(I5010000 g1 (g5 S'\xa4p=\n\xd7\xa3\x1e@' p2165 tp2166 Rp2167 g1 (g5 S'\xa4p=\n\xd7\xa3\x1e@' p2168 tp2169 Rp2170 F28.0 tp2171 a(I5020000 g1 (g5 S'\xe1z\x14\xaeG\xe1 @' p2172 tp2173 Rp2174 g1 (g5 S'\xe1z\x14\xaeG\xe1 @' p2175 tp2176 Rp2177 F33.0 tp2178 a(I5030000 g1 (g5 S'\xecQ\xb8\x1e\x85k!@' p2179 tp2180 Rp2181 g1 (g5 S'\xecQ\xb8\x1e\x85k!@' p2182 tp2183 Rp2184 F0.0 tp2185 a(I5040000 g1 (g5 S'\n\xd7\xa3p=\n#@' p2186 tp2187 Rp2188 g1 (g5 S'\n\xd7\xa3p=\n#@' p2189 tp2190 Rp2191 F2.0 tp2192 a(I5050000 g1 (g5 S'\xe1z\x14\xaeG\xe1$@' p2193 tp2194 Rp2195 g1 (g5 S'\xe1z\x14\xaeG\xe1$@' p2196 tp2197 Rp2198 F59.0 tp2199 a(I5060000 g1 (g5 S'33333\xb3&@' p2200 tp2201 Rp2202 g1 (g5 S'33333\xb3&@' p2203 tp2204 Rp2205 F13.0 tp2206 a(I5070000 g1 (g5 S"\xa4p=\n\xd7\xa3'@" p2207 tp2208 Rp2209 g1 (g5 S"\xa4p=\n\xd7\xa3'@" p2210 tp2211 Rp2212 F15.0 tp2213 a(I5080000 g1 (g5 S'\xb8\x1e\x85\xebQ8*@' p2214 tp2215 Rp2216 g1 (g5 S'\xb8\x1e\x85\xebQ8*@' p2217 tp2218 Rp2219 F40.0 tp2220 a(I5090000 g1 (g5 S'\xe1z\x14\xaeGa+@' p2221 tp2222 Rp2223 g1 (g5 S'\xe1z\x14\xaeGa+@' p2224 tp2225 Rp2226 F34.0 tp2227 a(I5100000 g1 (g5 S'\xc3\xf5(\\\x8f\xc2-@' p2228 tp2229 Rp2230 g1 (g5 S'\xc3\xf5(\\\x8f\xc2-@' p2231 tp2232 Rp2233 F72.0 tp2234 a(I5110000 g1 (g5 S'\x14\xaeG\xe1z\x140@' p2235 tp2236 Rp2237 g1 (g5 S'\x14\xaeG\xe1z\x140@' p2238 tp2239 Rp2240 F42.0 tp2241 a(I5120000 g1 (g5 S'\\\x8f\xc2\xf5(\\1@' p2242 tp2243 Rp2244 g1 (g5 S'\\\x8f\xc2\xf5(\\1@' p2245 tp2246 Rp2247 F87.0 tp2248 a(I5130000 g1 (g5 S')\\\x8f\xc2\xf5(3@' p2249 tp2250 Rp2251 g1 (g5 S')\\\x8f\xc2\xf5(3@' p2252 tp2253 Rp2254 F63.0 tp2255 a(I5140000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc3@' p2256 tp2257 Rp2258 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc3@' p2259 tp2260 Rp2261 F30.0 tp2262 a(I5150000 g1 (g5 S'=\n\xd7\xa3p=6@' p2263 tp2264 Rp2265 g1 (g5 S'=\n\xd7\xa3p=6@' p2266 tp2267 Rp2268 F77.0 tp2269 a(I5160000 g1 (g5 S'fffff\xa67@' p2270 tp2271 Rp2272 g1 (g5 S'fffff\xa67@' p2273 tp2274 Rp2275 F44.0 tp2276 a(I5170000 g1 (g5 S'\\\x8f\xc2\xf5(\\9@' p2277 tp2278 Rp2279 g1 (g5 S'\\\x8f\xc2\xf5(\\9@' p2280 tp2281 Rp2282 F51.0 tp2283 a(I5180000 g1 (g5 S'\x8f\xc2\xf5(\\\xcf:@' p2284 tp2285 Rp2286 g1 (g5 S'\x8f\xc2\xf5(\\\xcf:@' p2287 tp2288 Rp2289 F115.0 tp2290 a(I5190000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x0c<@' p2291 tp2292 Rp2293 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x0c<@' p2294 tp2295 Rp2296 F33.0 tp2297 a(I5200000 g1 (g5 S'\x1f\x85\xebQ\xb8\xde=@' p2298 tp2299 Rp2300 g1 (g5 S'\x1f\x85\xebQ\xb8\xde=@' p2301 tp2302 Rp2303 F27.0 tp2304 a(I5210000 g1 (g5 S'q=\n\xd7\xa3\xf0?@' p2305 tp2306 Rp2307 g1 (g5 S'q=\n\xd7\xa3\xf0?@' p2308 tp2309 Rp2310 F84.0 tp2311 a(I5220000 g1 (g5 S'fffff\xc6@@' p2312 tp2313 Rp2314 g1 (g5 S'fffff\xc6@@' p2315 tp2316 Rp2317 F76.0 tp2318 a(I5230000 g1 (g5 S'\\\x8f\xc2\xf5(\xdcA@' p2319 tp2320 Rp2321 g1 (g5 S'\\\x8f\xc2\xf5(\xdcA@' p2322 tp2323 Rp2324 F75.0 tp2325 a(I5240000 g1 (g5 S'q=\n\xd7\xa3PB@' p2326 tp2327 Rp2328 g1 (g5 S'q=\n\xd7\xa3PB@' p2329 tp2330 Rp2331 F30.0 tp2332 a(I5250000 g1 (g5 S'\x00\x00\x00\x00\x00@C@' p2333 tp2334 Rp2335 g1 (g5 S'\x00\x00\x00\x00\x00@C@' p2336 tp2337 Rp2338 F93.0 tp2339 a(I5260000 g1 (g5 S'\x9a\x99\x99\x99\x99\xd9C@' p2340 tp2341 Rp2342 g1 (g5 S'\x9a\x99\x99\x99\x99\xd9C@' p2343 tp2344 Rp2345 F48.0 tp2346 a(I5270000 g1 (g5 S'\x85\xebQ\xb8\x1eeD@' p2347 tp2348 Rp2349 g1 (g5 S'\x85\xebQ\xb8\x1eeD@' p2350 tp2351 Rp2352 F51.0 tp2353 a(I5280000 g1 (g5 S')\\\x8f\xc2\xf5HE@' p2354 tp2355 Rp2356 g1 (g5 S')\\\x8f\xc2\xf5HE@' p2357 tp2358 Rp2359 F66.0 tp2360 a(I5290000 g1 (g5 S'{\x14\xaeG\xe1\x1aF@' p2361 tp2362 Rp2363 g1 (g5 S'{\x14\xaeG\xe1\x1aF@' p2364 tp2365 Rp2366 F82.0 tp2367 a(I5300000 g1 (g5 S'\xc3\xf5(\\\x8f\x02G@' p2368 tp2369 Rp2370 g1 (g5 S'\xc3\xf5(\\\x8f\x02G@' p2371 tp2372 Rp2373 F111.0 tp2374 a(I5310000 g1 (g5 S'\xf6(\\\x8f\xc2\x95G@' p2375 tp2376 Rp2377 g1 (g5 S'\xf6(\\\x8f\xc2\x95G@' p2378 tp2379 Rp2380 F39.0 tp2381 a(I5320000 g1 (g5 S'\xf6(\\\x8f\xc2\xd5H@' p2382 tp2383 Rp2384 g1 (g5 S'\xf6(\\\x8f\xc2\xd5H@' p2385 tp2386 Rp2387 F74.0 tp2388 a(I5330000 g1 (g5 S'\xecQ\xb8\x1e\x85kI@' p2389 tp2390 Rp2391 g1 (g5 S'\xecQ\xb8\x1e\x85kI@' p2392 tp2393 Rp2394 F106.0 tp2395 a(I5340000 g1 (g5 S'\xe1z\x14\xaeGAJ@' p2396 tp2397 Rp2398 g1 (g5 S'\xe1z\x14\xaeGAJ@' p2399 tp2400 Rp2401 F80.0 tp2402 a(I5350000 g1 (g5 S'fffff\xe6J@' p2403 tp2404 Rp2405 g1 (g5 S'fffff\xe6J@' p2406 tp2407 Rp2408 F68.0 tp2409 a(I5360000 g1 (g5 S'{\x14\xaeG\xe1\xdaK@' p2410 tp2411 Rp2412 g1 (g5 S'{\x14\xaeG\xe1\xdaK@' p2413 tp2414 Rp2415 F47.0 tp2416 a(I5370000 g1 (g5 S'\xaeG\xe1z\x14\x8eL@' p2417 tp2418 Rp2419 g1 (g5 S'\xaeG\xe1z\x14\x8eL@' p2420 tp2421 Rp2422 F48.0 tp2423 a(I5380000 g1 (g5 S'\n\xd7\xa3p=\x8aM@' p2424 tp2425 Rp2426 g1 (g5 S'\n\xd7\xa3p=\x8aM@' p2427 tp2428 Rp2429 F108.0 tp2430 a(I5390000 g1 (g5 S'\xe1z\x14\xaeGaN@' p2431 tp2432 Rp2433 g1 (g5 S'\xe1z\x14\xaeGaN@' p2434 tp2435 Rp2436 F103.0 tp2437 a(I5400000 g1 (g5 S'\xaeG\xe1z\x14\xceN@' p2438 tp2439 Rp2440 g1 (g5 S'\xaeG\xe1z\x14\xceN@' p2441 tp2442 Rp2443 F58.0 tp2444 a(I5410000 g1 (g5 S'\xf6(\\\x8f\xc2UO@' p2445 tp2446 Rp2447 g1 (g5 S'\xf6(\\\x8f\xc2UO@' p2448 tp2449 Rp2450 F90.0 tp2451 a(I5420000 g1 (g5 S'\xb8\x1e\x85\xebQ8P@' p2452 tp2453 Rp2454 g1 (g5 S'\xb8\x1e\x85\xebQ8P@' p2455 tp2456 Rp2457 F93.0 tp2458 a(I5430000 g1 (g5 S'\xaeG\xe1z\x14NP@' p2459 tp2460 Rp2461 g1 (g5 S'\x00\x00\x00\x00\x00PP@' p2462 tp2463 Rp2464 F92.0 tp2465 a(I5440000 g1 (g5 S'q=\n\xd7\xa3 P@' p2466 tp2467 Rp2468 g1 (g5 S'H\xe1z\x14\xaeWP@' p2469 tp2470 Rp2471 F2.0 tp2472 a(I5450000 g1 (g5 S'\xe1z\x14\xaeG1P@' p2473 tp2474 Rp2475 g2471 F102.0 tp2476 a(I5460000 g1 (g5 S'\x00\x00\x00\x00\x00 P@' p2477 tp2478 Rp2479 g2471 F28.0 tp2480 a(I5470000 g1 (g5 S'\x14\xaeG\xe1zTP@' p2481 tp2482 Rp2483 g2471 F29.0 tp2484 a(I5480000 g1 (g5 S'q=\n\xd7\xa3\x90P@' p2485 tp2486 Rp2487 g1 (g5 S'q=\n\xd7\xa3\x90P@' p2488 tp2489 Rp2490 F93.0 tp2491 a(I5490000 g1 (g5 S'\x85\xebQ\xb8\x1euP@' p2492 tp2493 Rp2494 g2490 F83.0 tp2495 a(I5500000 g1 (g5 S'\x8f\xc2\xf5(\\\x7fP@' p2496 tp2497 Rp2498 g2490 F72.0 tp2499 a(I5510000 g1 (g5 S'\x00\x00\x00\x00\x00\xc0P@' p2500 tp2501 Rp2502 g1 (g5 S'\x00\x00\x00\x00\x00\xc0P@' p2503 tp2504 Rp2505 F132.0 tp2506 a(I5520000 g1 (g5 S'\xd7\xa3p=\n\xc7P@' p2507 tp2508 Rp2509 g1 (g5 S'\xd7\xa3p=\n\xc7P@' p2510 tp2511 Rp2512 F94.0 tp2513 a(I5530000 g1 (g5 S'\xb8\x1e\x85\xebQ\xf8P@' p2514 tp2515 Rp2516 g1 (g5 S'\xb8\x1e\x85\xebQ\xf8P@' p2517 tp2518 Rp2519 F111.0 tp2520 a(I5540000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x0cQ@' p2521 tp2522 Rp2523 g1 (g5 S'R\xb8\x1e\x85\xeb\x11Q@' p2524 tp2525 Rp2526 F51.0 tp2527 a(I5550000 g1 (g5 S'\x85\xebQ\xb8\x1e\x15Q@' p2528 tp2529 Rp2530 g1 (g5 S'\x85\xebQ\xb8\x1e\x15Q@' p2531 tp2532 Rp2533 F91.0 tp2534 a(I5560000 g1 (g5 S'R\xb8\x1e\x85\xebqQ@' p2535 tp2536 Rp2537 g1 (g5 S'R\xb8\x1e\x85\xebqQ@' p2538 tp2539 Rp2540 F105.0 tp2541 a(I5570000 g1 (g5 S'\x14\xaeG\xe1z\xc4Q@' p2542 tp2543 Rp2544 g1 (g5 S'\x14\xaeG\xe1z\xc4Q@' p2545 tp2546 Rp2547 F81.0 tp2548 a(I5580000 g1 (g5 S'\xa4p=\n\xd7#R@' p2549 tp2550 Rp2551 g1 (g5 S'\xa4p=\n\xd7#R@' p2552 tp2553 Rp2554 F73.0 tp2555 a(I5590000 g1 (g5 S'\xaeG\xe1z\x14^R@' p2556 tp2557 Rp2558 g1 (g5 S'\xaeG\xe1z\x14^R@' p2559 tp2560 Rp2561 F50.0 tp2562 a(I5600000 g1 (g5 S'fffff\xb6R@' p2563 tp2564 Rp2565 g1 (g5 S'fffff\xb6R@' p2566 tp2567 Rp2568 F88.0 tp2569 a(I5610000 g1 (g5 S'\xf6(\\\x8f\xc2\x15S@' p2570 tp2571 Rp2572 g1 (g5 S'\xf6(\\\x8f\xc2\x15S@' p2573 tp2574 Rp2575 F115.0 tp2576 a(I5620000 g1 (g5 S'\x00\x00\x00\x00\x00`S@' p2577 tp2578 Rp2579 g1 (g5 S'\x00\x00\x00\x00\x00`S@' p2580 tp2581 Rp2582 F105.0 tp2583 a(I5630000 g1 (g5 S'\xd7\xa3p=\n\xa7S@' p2584 tp2585 Rp2586 g1 (g5 S'\xd7\xa3p=\n\xa7S@' p2587 tp2588 Rp2589 F111.0 tp2590 a(I5640000 g1 (g5 S'\xa4p=\n\xd7\xc3S@' p2591 tp2592 Rp2593 g1 (g5 S'\xa4p=\n\xd7\xc3S@' p2594 tp2595 Rp2596 F103.0 tp2597 a(I5650000 g1 (g5 S')\\\x8f\xc2\xf5\xd8S@' p2598 tp2599 Rp2600 g1 (g5 S'\xf6(\\\x8f\xc2\xf5S@' p2601 tp2602 Rp2603 F104.0 tp2604 a(I5660000 g1 (g5 S'\xf6(\\\x8f\xc25T@' p2605 tp2606 Rp2607 g1 (g5 S'\xf6(\\\x8f\xc25T@' p2608 tp2609 Rp2610 F116.0 tp2611 a(I5670000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xfcS@' p2612 tp2613 Rp2614 g2610 F41.0 tp2615 a(I5680000 g1 (g5 S'=\n\xd7\xa3p]T@' p2616 tp2617 Rp2618 g1 (g5 S'=\n\xd7\xa3p]T@' p2619 tp2620 Rp2621 F97.0 tp2622 a(I5690000 g1 (g5 S'\xecQ\xb8\x1e\x85{T@' p2623 tp2624 Rp2625 g1 (g5 S'q=\n\xd7\xa3\x80T@' p2626 tp2627 Rp2628 F94.0 tp2629 a(I5700000 g1 (g5 S'\x1f\x85\xebQ\xb8~T@' p2630 tp2631 Rp2632 g1 (g5 S'{\x14\xaeG\xe1\x9aT@' p2633 tp2634 Rp2635 F30.0 tp2636 a(I5710000 g1 (g5 S'q=\n\xd7\xa3\xa0T@' p2637 tp2638 Rp2639 g1 (g5 S'33333\xa3T@' p2640 tp2641 Rp2642 F116.0 tp2643 a(I5720000 g1 (g5 S'=\n\xd7\xa3p\x8dT@' p2644 tp2645 Rp2646 g2642 F126.0 tp2647 a(I5730000 g1 (g5 S'\x14\xaeG\xe1z\x84T@' p2648 tp2649 Rp2650 g2642 F8.0 tp2651 a(I5740000 g1 (g5 S'\\\x8f\xc2\xf5(\x8cT@' p2652 tp2653 Rp2654 g2642 F100.0 tp2655 a(I5750000 g1 (g5 S'\x85\xebQ\xb8\x1euT@' p2656 tp2657 Rp2658 g2642 F98.0 tp2659 a(I5760000 g1 (g5 S'\xc3\xf5(\\\x8f\xa2T@' p2660 tp2661 Rp2662 g2642 F117.0 tp2663 a(I5770000 g1 (g5 S'\xe1z\x14\xaeG\xd1T@' p2664 tp2665 Rp2666 g1 (g5 S'\xe1z\x14\xaeG\xd1T@' p2667 tp2668 Rp2669 F90.0 tp2670 a(I5780000 g1 (g5 S'\x8f\xc2\xf5(\\?U@' p2671 tp2672 Rp2673 g1 (g5 S'\x8f\xc2\xf5(\\?U@' p2674 tp2675 Rp2676 F56.0 tp2677 a(I5790000 g1 (g5 S'\xaeG\xe1z\x14^U@' p2678 tp2679 Rp2680 g1 (g5 S'\x1f\x85\xebQ\xb8\x8eU@' p2681 tp2682 Rp2683 F28.0 tp2684 a(I5800000 g1 (g5 S'\xe1z\x14\xaeG\xe1U@' p2685 tp2686 Rp2687 g1 (g5 S'\xe1z\x14\xaeG\xe1U@' p2688 tp2689 Rp2690 F105.0 tp2691 a(I5810000 g1 (g5 S"\xd7\xa3p=\n'V@" p2692 tp2693 Rp2694 g1 (g5 S"\xd7\xa3p=\n'V@" p2695 tp2696 Rp2697 F132.0 tp2698 a(I5820000 g1 (g5 S'\xaeG\xe1z\x14.V@' p2699 tp2700 Rp2701 g1 (g5 S'\xf6(\\\x8f\xc2UV@' p2702 tp2703 Rp2704 F0.0 tp2705 a(I5830000 g1 (g5 S'=\n\xd7\xa3p\x1dV@' p2706 tp2707 Rp2708 g2704 F32.0 tp2709 a(I5840000 g1 (g5 S'\xaeG\xe1z\x14.V@' p2710 tp2711 Rp2712 g2704 F23.0 tp2713 a(I5850000 g1 (g5 S'\x85\xebQ\xb8\x1e%V@' p2714 tp2715 Rp2716 g2704 F23.0 tp2717 a(I5860000 g1 (g5 S')\\\x8f\xc2\xf58V@' p2718 tp2719 Rp2720 g2704 F83.0 tp2721 a(I5870000 g1 (g5 S'\xa4p=\n\xd7SV@' p2722 tp2723 Rp2724 g2704 F92.0 tp2725 a(I5880000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8V@' p2726 tp2727 Rp2728 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8V@' p2729 tp2730 Rp2731 F106.0 tp2732 a(I5890000 g1 (g5 S'=\n\xd7\xa3p\xadV@' p2733 tp2734 Rp2735 g1 (g5 S'\x8f\xc2\xf5(\\\xbfV@' p2736 tp2737 Rp2738 F106.0 tp2739 a(I5900000 g1 (g5 S'R\xb8\x1e\x85\xeb\xa1V@' p2740 tp2741 Rp2742 g1 (g5 S'\x14\xaeG\xe1z\xc4V@' p2743 tp2744 Rp2745 F52.0 tp2746 a(I5910000 g1 (g5 S'R\xb8\x1e\x85\xeb\xe1V@' p2747 tp2748 Rp2749 g1 (g5 S'R\xb8\x1e\x85\xeb\xe1V@' p2750 tp2751 Rp2752 F140.0 tp2753 a(I5920000 g1 (g5 S'{\x14\xaeG\xe1\xfaV@' p2754 tp2755 Rp2756 g1 (g5 S'R\xb8\x1e\x85\xeb\x11W@' p2757 tp2758 Rp2759 F128.0 tp2760 a(I5930000 g1 (g5 S'H\xe1z\x14\xaegW@' p2761 tp2762 Rp2763 g1 (g5 S'H\xe1z\x14\xaegW@' p2764 tp2765 Rp2766 F116.0 tp2767 a(I5940000 g1 (g5 S'\x00\x00\x00\x00\x00\xa0W@' p2768 tp2769 Rp2770 g1 (g5 S'\x00\x00\x00\x00\x00\xa0W@' p2771 tp2772 Rp2773 F138.0 tp2774 a(I5950000 g1 (g5 S'\xf6(\\\x8f\xc2\x95W@' p2775 tp2776 Rp2777 g1 (g5 S'\xaeG\xe1z\x14\xaeW@' p2778 tp2779 Rp2780 F75.0 tp2781 a(I5960000 g1 (g5 S'fffff\x96W@' p2782 tp2783 Rp2784 g2780 F77.0 tp2785 a(I5970000 g1 (g5 S'\x00\x00\x00\x00\x00\xe0W@' p2786 tp2787 Rp2788 g1 (g5 S'\x00\x00\x00\x00\x00\xe0W@' p2789 tp2790 Rp2791 F189.0 tp2792 a(I5980000 g1 (g5 S'\xa4p=\n\xd7\x13X@' p2793 tp2794 Rp2795 g1 (g5 S'\xa4p=\n\xd7\x13X@' p2796 tp2797 Rp2798 F104.0 tp2799 a(I5990000 g1 (g5 S'\xc3\xf5(\\\x8fBX@' p2800 tp2801 Rp2802 g1 (g5 S'\xa4p=\n\xd7CX@' p2803 tp2804 Rp2805 F109.0 tp2806 a(I6000000 g1 (g5 S'\xaeG\xe1z\x14>X@' p2807 tp2808 Rp2809 g2805 F108.0 tp2810 a(I6010000 g1 (g5 S'\xaeG\xe1z\x14\x1eX@' p2811 tp2812 Rp2813 g1 (g5 S'\xaeG\xe1z\x14nX@' p2814 tp2815 Rp2816 F49.0 tp2817 a(I6020000 g1 (g5 S'q=\n\xd7\xa3\xb0W@' p2818 tp2819 Rp2820 g2816 F39.0 tp2821 a(I6030000 g1 (g5 S'\xd7\xa3p=\n\xb7W@' p2822 tp2823 Rp2824 g2816 F129.0 tp2825 a(I6040000 g1 (g5 S'R\xb8\x1e\x85\xeb\xc1W@' p2826 tp2827 Rp2828 g2816 F2.0 tp2829 a(I6050000 g1 (g5 S'fffff\xe6W@' p2830 tp2831 Rp2832 g2816 F158.0 tp2833 a(I6060000 g1 (g5 S'\n\xd7\xa3p=\xdaW@' p2834 tp2835 Rp2836 g2816 F135.0 tp2837 a(I6070000 g1 (g5 S'\x1f\x85\xebQ\xb8NX@' p2838 tp2839 Rp2840 g2816 F103.0 tp2841 a(I6080000 g1 (g5 S'\xe1z\x14\xaeGaX@' p2842 tp2843 Rp2844 g2816 F106.0 tp2845 a(I6090000 g1 (g5 S'\x14\xaeG\xe1z\xa4X@' p2846 tp2847 Rp2848 g1 (g5 S'\x14\xaeG\xe1z\xa4X@' p2849 tp2850 Rp2851 F82.0 tp2852 a(I6100000 g1 (g5 S'33333\x93X@' p2853 tp2854 Rp2855 g2851 F79.0 tp2856 a(I6110000 g1 (g5 S'\xe1z\x14\xaeG\xc1X@' p2857 tp2858 Rp2859 g1 (g5 S'\xe1z\x14\xaeG\xc1X@' p2860 tp2861 Rp2862 F107.0 tp2863 a(I6120000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99X@' p2864 tp2865 Rp2866 g1 (g5 S'\x9a\x99\x99\x99\x99\xc9X@' p2867 tp2868 Rp2869 F66.0 tp2870 a(I6130000 g1 (g5 S'\\\x8f\xc2\xf5(\x9cX@' p2871 tp2872 Rp2873 g2869 F93.0 tp2874 a(I6140000 g1 (g5 S'\xc3\xf5(\\\x8f\xa2X@' p2875 tp2876 Rp2877 g2869 F62.0 tp2878 a(I6150000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xccX@' p2879 tp2880 Rp2881 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xccX@' p2882 tp2883 Rp2884 F56.0 tp2885 a(I6160000 g1 (g5 S'q=\n\xd7\xa3\xd0X@' p2886 tp2887 Rp2888 g1 (g5 S'q=\n\xd7\xa3\xd0X@' p2889 tp2890 Rp2891 F149.0 tp2892 a(I6170000 g1 (g5 S'\xb8\x1e\x85\xebQXY@' p2893 tp2894 Rp2895 g1 (g5 S'\xb8\x1e\x85\xebQXY@' p2896 tp2897 Rp2898 F111.0 tp2899 a(I6180000 g1 (g5 S'=\n\xd7\xa3p\x9dY@' p2900 tp2901 Rp2902 g1 (g5 S'=\n\xd7\xa3p\x9dY@' p2903 tp2904 Rp2905 F129.0 tp2906 a(I6190000 g1 (g5 S'q=\n\xd7\xa3\xc0Y@' p2907 tp2908 Rp2909 g1 (g5 S'q=\n\xd7\xa3\xc0Y@' p2910 tp2911 Rp2912 F130.0 tp2913 a(I6200000 g1 (g5 S'\xc3\xf5(\\\x8f\x02Z@' p2914 tp2915 Rp2916 g1 (g5 S'\xc3\xf5(\\\x8f\x02Z@' p2917 tp2918 Rp2919 F160.0 tp2920 a(I6210000 g1 (g5 S'\xc3\xf5(\\\x8f\xe2Y@' p2921 tp2922 Rp2923 g1 (g5 S'\n\xd7\xa3p=\nZ@' p2924 tp2925 Rp2926 F99.0 tp2927 a(I6220000 g1 (g5 S'\x14\xaeG\xe1z\xf4Y@' p2928 tp2929 Rp2930 g2926 F96.0 tp2931 a(I6230000 g1 (g5 S'\x1f\x85\xebQ\xb8>Y@' p2932 tp2933 Rp2934 g2926 F7.0 tp2935 a(I6240000 g1 (g5 S'\n\xd7\xa3p=\xdaX@' p2936 tp2937 Rp2938 g2926 F104.0 tp2939 a(I6250000 g1 (g5 S'\xc3\xf5(\\\x8f\xe2X@' p2940 tp2941 Rp2942 g2926 F131.0 tp2943 a(I6260000 g1 (g5 S'\x8f\xc2\xf5(\\\xcfX@' p2944 tp2945 Rp2946 g2926 F102.0 tp2947 a(I6270000 g1 (g5 S'{\x14\xaeG\xe1\xbaX@' p2948 tp2949 Rp2950 g2926 F94.0 tp2951 a(I6280000 g1 (g5 S'\xecQ\xb8\x1e\x85\xcbX@' p2952 tp2953 Rp2954 g2926 F99.0 tp2955 a(I6290000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99X@' p2956 tp2957 Rp2958 g2926 F63.0 tp2959 a(I6300000 g1 (g5 S'\xd7\xa3p=\n\xf7X@' p2960 tp2961 Rp2962 g2926 F173.0 tp2963 a(I6310000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x0cY@' p2964 tp2965 Rp2966 g2926 F111.0 tp2967 a(I6320000 g1 (g5 S'\xaeG\xe1z\x14\xfeX@' p2968 tp2969 Rp2970 g2926 F113.0 tp2971 a(I6330000 g1 (g5 S'fffff\x16Y@' p2972 tp2973 Rp2974 g2926 F118.0 tp2975 a(I6340000 g1 (g5 S'\x14\xaeG\xe1z4Y@' p2976 tp2977 Rp2978 g2926 F119.0 tp2979 a(I6350000 g1 (g5 S'{\x14\xaeG\xe1\xdaY@' p2980 tp2981 Rp2982 g2926 F82.0 tp2983 a(I6360000 g1 (g5 S'fffff\xf6Y@' p2984 tp2985 Rp2986 g2926 F166.0 tp2987 a(I6370000 g1 (g5 S'\xe1z\x14\xaeG\x81Z@' p2988 tp2989 Rp2990 g1 (g5 S'\xe1z\x14\xaeG\x81Z@' p2991 tp2992 Rp2993 F132.0 tp2994 a(I6380000 g1 (g5 S'\\\x8f\xc2\xf5(\xfcZ@' p2995 tp2996 Rp2997 g1 (g5 S'\\\x8f\xc2\xf5(\xfcZ@' p2998 tp2999 Rp3000 F174.0 tp3001 a(I6390000 g1 (g5 S'\n\xd7\xa3p=J[@' p3002 tp3003 Rp3004 g1 (g5 S'\x8f\xc2\xf5(\\O[@' p3005 tp3006 Rp3007 F127.0 tp3008 a(I6400000 g1 (g5 S'=\n\xd7\xa3pM[@' p3009 tp3010 Rp3011 g3007 F129.0 tp3012 a(I6410000 g1 (g5 S'fffff\xa6[@' p3013 tp3014 Rp3015 g1 (g5 S'fffff\xa6[@' p3016 tp3017 Rp3018 F110.0 tp3019 a(I6420000 g1 (g5 S')\\\x8f\xc2\xf5\x08\\@' p3020 tp3021 Rp3022 g1 (g5 S')\\\x8f\xc2\xf5\x08\\@' p3023 tp3024 Rp3025 F172.0 tp3026 a(I6430000 g1 (g5 S'\x00\x00\x00\x00\x00`\\@' p3027 tp3028 Rp3029 g1 (g5 S'\x00\x00\x00\x00\x00`\\@' p3030 tp3031 Rp3032 F123.0 tp3033 a(I6440000 g1 (g5 S'\n\xd7\xa3p=\xaa\\@' p3034 tp3035 Rp3036 g1 (g5 S'\n\xd7\xa3p=\xaa\\@' p3037 tp3038 Rp3039 F175.0 tp3040 a(I6450000 g1 (g5 S'R\xb8\x1e\x85\xeb!]@' p3041 tp3042 Rp3043 g1 (g5 S'R\xb8\x1e\x85\xeb!]@' p3044 tp3045 Rp3046 F140.0 tp3047 a(I6460000 g1 (g5 S')\\\x8f\xc2\xf5h]@' p3048 tp3049 Rp3050 g1 (g5 S')\\\x8f\xc2\xf5h]@' p3051 tp3052 Rp3053 F109.0 tp3054 a(I6470000 g1 (g5 S')\\\x8f\xc2\xf5\xd8]@' p3055 tp3056 Rp3057 g1 (g5 S')\\\x8f\xc2\xf5\xd8]@' p3058 tp3059 Rp3060 F185.0 tp3061 a(I6480000 g1 (g5 S'=\n\xd7\xa3p\x1d^@' p3062 tp3063 Rp3064 g1 (g5 S'=\n\xd7\xa3p\x1d^@' p3065 tp3066 Rp3067 F175.0 tp3068 a(I6490000 g1 (g5 S'fffff\xf6^@' p3069 tp3070 Rp3071 g1 (g5 S'fffff\xf6^@' p3072 tp3073 Rp3074 F174.0 tp3075 a(I6500000 g1 (g5 S'\xc3\xf5(\\\x8f\xf2^@' p3076 tp3077 Rp3078 g1 (g5 S'\x9a\x99\x99\x99\x99\xf9^@' p3079 tp3080 Rp3081 F139.0 tp3082 a(I6510000 g1 (g5 S'q=\n\xd7\xa30_@' p3083 tp3084 Rp3085 g1 (g5 S'q=\n\xd7\xa30_@' p3086 tp3087 Rp3088 F165.0 tp3089 a(I6520000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8_@' p3090 tp3091 Rp3092 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8_@' p3093 tp3094 Rp3095 F166.0 tp3096 a(I6530000 g1 (g5 S'\x00\x00\x00\x00\x00\x08`@' p3097 tp3098 Rp3099 g1 (g5 S'\x00\x00\x00\x00\x00\x08`@' p3100 tp3101 Rp3102 F272.0 tp3103 a(I6540000 g1 (g5 S'\x85\xebQ\xb8\x1eU`@' p3104 tp3105 Rp3106 g1 (g5 S'\x14\xaeG\xe1z\\`@' p3107 tp3108 Rp3109 F137.0 tp3110 a(I6550000 g1 (g5 S'\x85\xebQ\xb8\x1e\xbd`@' p3111 tp3112 Rp3113 g1 (g5 S'\x85\xebQ\xb8\x1e\xbd`@' p3114 tp3115 Rp3116 F351.0 tp3117 a(I6560000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xdc`@' p3118 tp3119 Rp3120 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xdc`@' p3121 tp3122 Rp3123 F153.0 tp3124 a(I6570000 g1 (g5 S'\\\x8f\xc2\xf5(\\a@' p3125 tp3126 Rp3127 g1 (g5 S'\\\x8f\xc2\xf5(\\a@' p3128 tp3129 Rp3130 F180.0 tp3131 a(I6580000 g1 (g5 S')\\\x8f\xc2\xf5\xf0a@' p3132 tp3133 Rp3134 g1 (g5 S')\\\x8f\xc2\xf5\xf0a@' p3135 tp3136 Rp3137 F196.0 tp3138 a(I6590000 g1 (g5 S'H\xe1z\x14\xae?b@' p3139 tp3140 Rp3141 g1 (g5 S'H\xe1z\x14\xae?b@' p3142 tp3143 Rp3144 F199.0 tp3145 a(I6600000 g1 (g5 S'{\x14\xaeG\xe1\x12b@' p3146 tp3147 Rp3148 g3144 F153.0 tp3149 a(I6610000 g1 (g5 S'\xecQ\xb8\x1e\x85Kb@' p3150 tp3151 Rp3152 g1 (g5 S'\xecQ\xb8\x1e\x85Kb@' p3153 tp3154 Rp3155 F136.0 tp3156 a(I6620000 g1 (g5 S'\n\xd7\xa3p=zb@' p3157 tp3158 Rp3159 g1 (g5 S'\xa4p=\n\xd7{b@' p3160 tp3161 Rp3162 F174.0 tp3163 a(I6630000 g1 (g5 S'\xaeG\xe1z\x14\x9eb@' p3164 tp3165 Rp3166 g1 (g5 S'\xaeG\xe1z\x14\x9eb@' p3167 tp3168 Rp3169 F157.0 tp3170 a(I6640000 g1 (g5 S'{\x14\xaeG\xe1\xf2b@' p3171 tp3172 Rp3173 g1 (g5 S'{\x14\xaeG\xe1\xf2b@' p3174 tp3175 Rp3176 F364.0 tp3177 a(I6650000 g1 (g5 S'\xe1z\x14\xaeG\x81c@' p3178 tp3179 Rp3180 g1 (g5 S'\xe1z\x14\xaeG\x81c@' p3181 tp3182 Rp3183 F313.0 tp3184 a(I6660000 g1 (g5 S'R\xb8\x1e\x85\xeb\xe1c@' p3185 tp3186 Rp3187 g1 (g5 S'R\xb8\x1e\x85\xeb\xe1c@' p3188 tp3189 Rp3190 F187.0 tp3191 a(I6670000 g1 (g5 S'=\n\xd7\xa3p\xc5c@' p3192 tp3193 Rp3194 g1 (g5 S'\n\xd7\xa3p=\xe2c@' p3195 tp3196 Rp3197 F83.0 tp3198 a(I6680000 g1 (g5 S'\x00\x00\x00\x00\x00\x00d@' p3199 tp3200 Rp3201 g1 (g5 S'\x00\x00\x00\x00\x00\x00d@' p3202 tp3203 Rp3204 F171.0 tp3205 a(I6690000 g1 (g5 S'{\x14\xaeG\xe1Rd@' p3206 tp3207 Rp3208 g1 (g5 S'{\x14\xaeG\xe1Rd@' p3209 tp3210 Rp3211 F120.0 tp3212 a(I6700000 g1 (g5 S'\xf6(\\\x8f\xc2\xb5d@' p3213 tp3214 Rp3215 g1 (g5 S'\xf6(\\\x8f\xc2\xb5d@' p3216 tp3217 Rp3218 F325.0 tp3219 a(I6710000 g1 (g5 S'\x1f\x85\xebQ\xb8\x16e@' p3220 tp3221 Rp3222 g1 (g5 S'\x1f\x85\xebQ\xb8\x16e@' p3223 tp3224 Rp3225 F407.0 tp3226 a(I6720000 g1 (g5 S'{\x14\xaeG\xe1\x92e@' p3227 tp3228 Rp3229 g1 (g5 S'{\x14\xaeG\xe1\x92e@' p3230 tp3231 Rp3232 F385.0 tp3233 a(I6730000 g1 (g5 S'\x00\x00\x00\x00\x00\xb0e@' p3234 tp3235 Rp3236 g1 (g5 S'\x00\x00\x00\x00\x00\xb0e@' p3237 tp3238 Rp3239 F150.0 tp3240 a(I6740000 g1 (g5 S'q=\n\xd7\xa3\xf0e@' p3241 tp3242 Rp3243 g1 (g5 S'q=\n\xd7\xa3\xf0e@' p3244 tp3245 Rp3246 F179.0 tp3247 a(I6750000 g1 (g5 S'\xd7\xa3p=\n\xe7e@' p3248 tp3249 Rp3250 g1 (g5 S'\xf6(\\\x8f\xc2%f@' p3251 tp3252 Rp3253 F84.0 tp3254 a(I6760000 g1 (g5 S'fffff\xdee@' p3255 tp3256 Rp3257 g3253 F165.0 tp3258 a(I6770000 g1 (g5 S'\\\x8f\xc2\xf5($f@' p3259 tp3260 Rp3261 g3253 F356.0 tp3262 a(I6780000 g1 (g5 S'\xecQ\xb8\x1e\x85cf@' p3263 tp3264 Rp3265 g1 (g5 S'\xecQ\xb8\x1e\x85cf@' p3266 tp3267 Rp3268 F405.0 tp3269 a(I6790000 g1 (g5 S')\\\x8f\xc2\xf5\xc0f@' p3270 tp3271 Rp3272 g1 (g5 S')\\\x8f\xc2\xf5\xc0f@' p3273 tp3274 Rp3275 F172.0 tp3276 a(I6800000 g1 (g5 S'\x8f\xc2\xf5(\\\xfff@' p3277 tp3278 Rp3279 g1 (g5 S'\x8f\xc2\xf5(\\\xfff@' p3280 tp3281 Rp3282 F270.0 tp3283 a(I6810000 g1 (g5 S'\x8f\xc2\xf5(\\Og@' p3284 tp3285 Rp3286 g1 (g5 S'\x8f\xc2\xf5(\\Og@' p3287 tp3288 Rp3289 F181.0 tp3290 a(I6820000 g1 (g5 S'\xb8\x1e\x85\xebQ\x90g@' p3291 tp3292 Rp3293 g1 (g5 S'\xb8\x1e\x85\xebQ\x90g@' p3294 tp3295 Rp3296 F128.0 tp3297 a(I6830000 g1 (g5 S')\\\x8f\xc2\xf5\xb8g@' p3298 tp3299 Rp3300 g1 (g5 S'\xc3\xf5(\\\x8f\xcag@' p3301 tp3302 Rp3303 F120.0 tp3304 a(I6840000 g1 (g5 S'\xf6(\\\x8f\xc2\xcdg@' p3305 tp3306 Rp3307 g1 (g5 S'\xf6(\\\x8f\xc2\xcdg@' p3308 tp3309 Rp3310 F170.0 tp3311 a(I6850000 g1 (g5 S'\x9a\x99\x99\x99\x99\x19h@' p3312 tp3313 Rp3314 g1 (g5 S'\x9a\x99\x99\x99\x99\x19h@' p3315 tp3316 Rp3317 F197.0 tp3318 a(I6860000 g1 (g5 S'\x85\xebQ\xb8\x1emh@' p3319 tp3320 Rp3321 g1 (g5 S'H\xe1z\x14\xaeoh@' p3322 tp3323 Rp3324 F167.0 tp3325 a(I6870000 g1 (g5 S'\xb8\x1e\x85\xebQ\xa0h@' p3326 tp3327 Rp3328 g1 (g5 S'\xb8\x1e\x85\xebQ\xa0h@' p3329 tp3330 Rp3331 F345.0 tp3332 a(I6880000 g1 (g5 S'\xb8\x1e\x85\xebQ\xf8h@' p3333 tp3334 Rp3335 g1 (g5 S'\xb8\x1e\x85\xebQ\xf8h@' p3336 tp3337 Rp3338 F174.0 tp3339 a(I6890000 g1 (g5 S'\n\xd7\xa3p=Zi@' p3340 tp3341 Rp3342 g1 (g5 S'\n\xd7\xa3p=Zi@' p3343 tp3344 Rp3345 F150.0 tp3346 a(I6900000 g1 (g5 S'\x1f\x85\xebQ\xb8\x8ei@' p3347 tp3348 Rp3349 g1 (g5 S'\x1f\x85\xebQ\xb8\x8ei@' p3350 tp3351 Rp3352 F164.0 tp3353 a(I6910000 g1 (g5 S'\xa4p=\n\xd7\xa3i@' p3354 tp3355 Rp3356 g1 (g5 S'\xa4p=\n\xd7\xa3i@' p3357 tp3358 Rp3359 F251.0 tp3360 a(I6920000 g1 (g5 S'=\n\xd7\xa3p-j@' p3361 tp3362 Rp3363 g1 (g5 S'=\n\xd7\xa3p-j@' p3364 tp3365 Rp3366 F270.0 tp3367 a(I6930000 g1 (g5 S'\xaeG\xe1z\x14~j@' p3368 tp3369 Rp3370 g1 (g5 S'\xaeG\xe1z\x14~j@' p3371 tp3372 Rp3373 F155.0 tp3374 a(I6940000 g1 (g5 S'\n\xd7\xa3p=\x92j@' p3375 tp3376 Rp3377 g1 (g5 S'\n\xd7\xa3p=\x92j@' p3378 tp3379 Rp3380 F175.0 tp3381 a(I6950000 g1 (g5 S'{\x14\xaeG\xe1\xe2j@' p3382 tp3383 Rp3384 g1 (g5 S'{\x14\xaeG\xe1\xe2j@' p3385 tp3386 Rp3387 F368.0 tp3388 a(I6960000 g1 (g5 S'q=\n\xd7\xa3\x18k@' p3389 tp3390 Rp3391 g1 (g5 S'q=\n\xd7\xa3\x18k@' p3392 tp3393 Rp3394 F159.0 tp3395 a(I6970000 g1 (g5 S'fffff\x0ek@' p3396 tp3397 Rp3398 g1 (g5 S'\xecQ\xb8\x1e\x85#k@' p3399 tp3400 Rp3401 F177.0 tp3402 a(I6980000 g1 (g5 S'\x8f\xc2\xf5(\\\xffj@' p3403 tp3404 Rp3405 g3401 F134.0 tp3406 a(I6990000 g1 (g5 S'\x9a\x99\x99\x99\x99\xc9j@' p3407 tp3408 Rp3409 g3401 F114.0 tp3410 a(I7000000 g1 (g5 S'\xa4p=\n\xd7\xcbj@' p3411 tp3412 Rp3413 g3401 F160.0 tp3414 a(I7010000 g1 (g5 S'\xb8\x1e\x85\xebQ8k@' p3415 tp3416 Rp3417 g1 (g5 S'\xb8\x1e\x85\xebQ8k@' p3418 tp3419 Rp3420 F477.0 tp3421 a(I7020000 g1 (g5 S'\x9a\x99\x99\x99\x99Ik@' p3422 tp3423 Rp3424 g1 (g5 S'\xc3\xf5(\\\x8f\x82k@' p3425 tp3426 Rp3427 F168.0 tp3428 a(I7030000 g1 (g5 S'{\x14\xaeG\xe1:k@' p3429 tp3430 Rp3431 g3427 F188.0 tp3432 a(I7040000 g1 (g5 S'\xd7\xa3p=\n\xa7k@' p3433 tp3434 Rp3435 g1 (g5 S'\xd7\xa3p=\n\xa7k@' p3436 tp3437 Rp3438 F330.0 tp3439 a(I7050000 g1 (g5 S'{\x14\xaeG\xe1\xe2k@' p3440 tp3441 Rp3442 g1 (g5 S'{\x14\xaeG\xe1\xe2k@' p3443 tp3444 Rp3445 F353.0 tp3446 a(I7060000 g1 (g5 S'{\x14\xaeG\xe1\x92l@' p3447 tp3448 Rp3449 g1 (g5 S'{\x14\xaeG\xe1\x92l@' p3450 tp3451 Rp3452 F424.0 tp3453 a(I7070000 g1 (g5 S'\n\xd7\xa3p=\x02m@' p3454 tp3455 Rp3456 g1 (g5 S'\n\xd7\xa3p=\x02m@' p3457 tp3458 Rp3459 F376.0 tp3460 a(I7080000 g1 (g5 S'\xc3\xf5(\\\x8fbm@' p3461 tp3462 Rp3463 g1 (g5 S'\xc3\xf5(\\\x8fbm@' p3464 tp3465 Rp3466 F180.0 tp3467 a(I7090000 g1 (g5 S'\\\x8f\xc2\xf5(|m@' p3468 tp3469 Rp3470 g1 (g5 S'\xe1z\x14\xaeG\x91m@' p3471 tp3472 Rp3473 F191.0 tp3474 a(I7100000 g1 (g5 S'R\xb8\x1e\x85\xeb\xc1m@' p3475 tp3476 Rp3477 g1 (g5 S'R\xb8\x1e\x85\xeb\xc1m@' p3478 tp3479 Rp3480 F381.0 tp3481 a(I7110000 g1 (g5 S'\x9a\x99\x99\x99\x99\x11n@' p3482 tp3483 Rp3484 g1 (g5 S'\x9a\x99\x99\x99\x99\x11n@' p3485 tp3486 Rp3487 F358.0 tp3488 a(I7120000 g1 (g5 S'\x9a\x99\x99\x99\x99yn@' p3489 tp3490 Rp3491 g1 (g5 S'\x9a\x99\x99\x99\x99yn@' p3492 tp3493 Rp3494 F450.0 tp3495 a(I7130000 g1 (g5 S'\x14\xaeG\xe1z\\n@' p3496 tp3497 Rp3498 g3494 F360.0 tp3499 a(I7140000 g1 (g5 S'\x14\xaeG\xe1ztn@' p3500 tp3501 Rp3502 g1 (g5 S'\n\xd7\xa3p=zn@' p3503 tp3504 Rp3505 F169.0 tp3506 a(I7150000 g1 (g5 S'\x1f\x85\xebQ\xb8nn@' p3507 tp3508 Rp3509 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x8cn@' p3510 tp3511 Rp3512 F184.0 tp3513 a(I7160000 g1 (g5 S'\xf6(\\\x8f\xc2\x9dn@' p3514 tp3515 Rp3516 g1 (g5 S'\xf6(\\\x8f\xc2\x9dn@' p3517 tp3518 Rp3519 F318.0 tp3520 a(I7170000 g1 (g5 S'\x14\xaeG\xe1z\xacn@' p3521 tp3522 Rp3523 g1 (g5 S'\x14\xaeG\xe1z\xacn@' p3524 tp3525 Rp3526 F193.0 tp3527 a(I7180000 g1 (g5 S'\xaeG\xe1z\x14\xfen@' p3528 tp3529 Rp3530 g1 (g5 S'33333\x03o@' p3531 tp3532 Rp3533 F158.0 tp3534 a(I7190000 g1 (g5 S'\x9a\x99\x99\x99\x99)o@' p3535 tp3536 Rp3537 g1 (g5 S'\x9a\x99\x99\x99\x99)o@' p3538 tp3539 Rp3540 F175.0 tp3541 a(I7200000 g1 (g5 S'\x00\x00\x00\x00\x00xo@' p3542 tp3543 Rp3544 g1 (g5 S'\x00\x00\x00\x00\x00xo@' p3545 tp3546 Rp3547 F465.0 tp3548 a(I7210000 g1 (g5 S'\\\x8f\xc2\xf5(\x8co@' p3549 tp3550 Rp3551 g1 (g5 S'=\n\xd7\xa3p\x8do@' p3552 tp3553 Rp3554 F179.0 tp3555 a(I7220000 g1 (g5 S'33333\xb3o@' p3556 tp3557 Rp3558 g1 (g5 S'33333\xb3o@' p3559 tp3560 Rp3561 F388.0 tp3562 a(I7230000 g1 (g5 S'\x8f\xc2\xf5(\\\xd7o@' p3563 tp3564 Rp3565 g1 (g5 S'\x8f\xc2\xf5(\\\xd7o@' p3566 tp3567 Rp3568 F292.0 tp3569 a(I7240000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc0p@' p3570 tp3571 Rp3572 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc0p@' p3573 tp3574 Rp3575 F287.0 tp3576 a(I7250000 g1 (g5 S'=\n\xd7\xa3pqp@' p3577 tp3578 Rp3579 g1 (g5 S'=\n\xd7\xa3pqp@' p3580 tp3581 Rp3582 F428.0 tp3583 a(I7260000 g1 (g5 S'R\xb8\x1e\x85\xeb\x9dp@' p3584 tp3585 Rp3586 g1 (g5 S'R\xb8\x1e\x85\xeb\x9dp@' p3587 tp3588 Rp3589 F387.0 tp3590 a(I7270000 g1 (g5 S')\\\x8f\xc2\xf5\xecp@' p3591 tp3592 Rp3593 g1 (g5 S')\\\x8f\xc2\xf5\xecp@' p3594 tp3595 Rp3596 F383.0 tp3597 a(I7280000 g1 (g5 S'\xecQ\xb8\x1e\x85\xf3p@' p3598 tp3599 Rp3600 g1 (g5 S'\xecQ\xb8\x1e\x85\xf3p@' p3601 tp3602 Rp3603 F186.0 tp3604 a(I7290000 g1 (g5 S'q=\n\xd7\xa3\xf8p@' p3605 tp3606 Rp3607 g1 (g5 S'q=\n\xd7\xa3\xf8p@' p3608 tp3609 Rp3610 F437.0 tp3611 a(I7300000 g1 (g5 S'H\xe1z\x14\xae\x1bq@' p3612 tp3613 Rp3614 g1 (g5 S'H\xe1z\x14\xae\x1bq@' p3615 tp3616 Rp3617 F356.0 tp3618 a(I7310000 g1 (g5 S'\xe1z\x14\xaeG\x1dq@' p3619 tp3620 Rp3621 g1 (g5 S'\xe1z\x14\xaeG\x1dq@' p3622 tp3623 Rp3624 F457.0 tp3625 a(I7320000 g1 (g5 S'\xb8\x1e\x85\xebQt@' p3878 tp3879 Rp3880 g3836 F385.0 tp3881 a(I7780000 g1 (g5 S'\x9a\x99\x99\x99\x99At@' p3882 tp3883 Rp3884 g3836 F185.0 tp3885 a(I7790000 g1 (g5 S'\x9a\x99\x99\x99\x99\x81t@' p3886 tp3887 Rp3888 g1 (g5 S'\x9a\x99\x99\x99\x99\x81t@' p3889 tp3890 Rp3891 F396.0 tp3892 a(I7800000 g1 (g5 S')\\\x8f\xc2\xf5Lt@' p3893 tp3894 Rp3895 g3891 F161.0 tp3896 a(I7810000 g1 (g5 S'=\n\xd7\xa3pMt@' p3897 tp3898 Rp3899 g3891 F431.0 tp3900 a(I7820000 g1 (g5 S'\xb8\x1e\x85\xebQPt@' p3901 tp3902 Rp3903 g3891 F411.0 tp3904 a(I7830000 g1 (g5 S'\x00\x00\x00\x00\x00\\t@' p3905 tp3906 Rp3907 g3891 F456.0 tp3908 a(I7840000 g1 (g5 S'\xc3\xf5(\\\x8fJt@' p3909 tp3910 Rp3911 g3891 F170.0 tp3912 a(I7850000 g1 (g5 S'\xa4p=\n\xd7Gt@' p3913 tp3914 Rp3915 g3891 F325.0 tp3916 a(I7860000 g1 (g5 S'\xecQ\xb8\x1e\x85\xe7s@' p3917 tp3918 Rp3919 g3891 F153.0 tp3920 a(I7870000 g1 (g5 S'{\x14\xaeG\xe1\xb6s@' p3921 tp3922 Rp3923 g3891 F128.0 tp3924 a(I7880000 g1 (g5 S'\x9a\x99\x99\x99\x99\xb1s@' p3925 tp3926 Rp3927 g3891 F359.0 tp3928 a(I7890000 g1 (g5 S'\x8f\xc2\xf5(\\\x97s@' p3929 tp3930 Rp3931 g3891 F189.0 tp3932 a(I7900000 g1 (g5 S'33333\xc7s@' p3933 tp3934 Rp3935 g3891 F424.0 tp3936 a(I7910000 g1 (g5 S'R\xb8\x1e\x85\xebqs@' p3937 tp3938 Rp3939 g3891 F193.0 tp3940 a(I7920000 g1 (g5 S'{\x14\xaeG\xe1\x82s@' p3941 tp3942 Rp3943 g3891 F495.0 tp3944 a(I7930000 g1 (g5 S'\xecQ\xb8\x1e\x85\xabs@' p3945 tp3946 Rp3947 g3891 F627.0 tp3948 a(I7940000 g1 (g5 S'\xf6(\\\x8f\xc2\xb1s@' p3949 tp3950 Rp3951 g3891 F413.0 tp3952 a(I7950000 g1 (g5 S'33333\xabs@' p3953 tp3954 Rp3955 g3891 F348.0 tp3956 a(I7960000 g1 (g5 S'\x00\x00\x00\x00\x00|s@' p3957 tp3958 Rp3959 g3891 F111.0 tp3960 a(I7970000 g1 (g5 S'q=\n\xd7\xa3\x8cs@' p3961 tp3962 Rp3963 g3891 F403.0 tp3964 a(I7980000 g1 (g5 S'\xa4p=\n\xd7{s@' p3965 tp3966 Rp3967 g3891 F351.0 tp3968 a(I7990000 g1 (g5 S'R\xb8\x1e\x85\xeb\xa9s@' p3969 tp3970 Rp3971 g3891 F461.0 tp3972 a(I8000000 g1 (g5 S'\xa4p=\n\xd7\xd3s@' p3973 tp3974 Rp3975 g3891 F370.0 tp3976 a(I8010000 g1 (g5 S'\xb8\x1e\x85\xebQ\xccs@' p3977 tp3978 Rp3979 g3891 F401.0 tp3980 a(I8020000 g1 (g5 S'fffff\xces@' p3981 tp3982 Rp3983 g3891 F430.0 tp3984 a(I8030000 g1 (g5 S'\xf6(\\\x8f\xc2\xd1s@' p3985 tp3986 Rp3987 g3891 F341.0 tp3988 a(I8040000 g1 (g5 S'\xaeG\xe1z\x14\xf6s@' p3989 tp3990 Rp3991 g3891 F408.0 tp3992 a(I8050000 g1 (g5 S'\\\x8f\xc2\xf5(\x14t@' p3993 tp3994 Rp3995 g3891 F436.0 tp3996 a(I8060000 g1 (g5 S'\xd7\xa3p=\n_t@' p3997 tp3998 Rp3999 g3891 F667.0 tp4000 a(I8070000 g1 (g5 S'R\xb8\x1e\x85\xeb]t@' p4001 tp4002 Rp4003 g3891 F387.0 tp4004 a(I8080000 g1 (g5 S'\x00\x00\x00\x00\x00lt@' p4005 tp4006 Rp4007 g3891 F407.0 tp4008 a(I8090000 g1 (g5 S'\\\x8f\xc2\xf5(Lt@' p4009 tp4010 Rp4011 g3891 F192.0 tp4012 a(I8100000 g1 (g5 S'fffff\x96t@' p4013 tp4014 Rp4015 g1 (g5 S'fffff\x96t@' p4016 tp4017 Rp4018 F481.0 tp4019 a(I8110000 g1 (g5 S'fffff\xc6t@' p4020 tp4021 Rp4022 g1 (g5 S'fffff\xc6t@' p4023 tp4024 Rp4025 F494.0 tp4026 a(I8120000 g1 (g5 S'\xa4p=\n\xd7\xb3t@' p4027 tp4028 Rp4029 g1 (g5 S'\xb8\x1e\x85\xebQ\xc8t@' p4030 tp4031 Rp4032 F195.0 tp4033 a(I8130000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xe4t@' p4034 tp4035 Rp4036 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xe4t@' p4037 tp4038 Rp4039 F353.0 tp4040 a(I8140000 g1 (g5 S'\xd7\xa3p=\n\xe3t@' p4041 tp4042 Rp4043 g4039 F441.0 tp4044 a(I8150000 g1 (g5 S'\xd7\xa3p=\n\x0bu@' p4045 tp4046 Rp4047 g1 (g5 S'\xd7\xa3p=\n\x0bu@' p4048 tp4049 Rp4050 F449.0 tp4051 a(I8160000 g1 (g5 S'fffffJu@' p4052 tp4053 Rp4054 g1 (g5 S'fffffJu@' p4055 tp4056 Rp4057 F394.0 tp4058 a(I8170000 g1 (g5 S'q=\n\xd7\xa3pu@' p4059 tp4060 Rp4061 g1 (g5 S'q=\n\xd7\xa3pu@' p4062 tp4063 Rp4064 F409.0 tp4065 a(I8180000 g1 (g5 S'\\\x8f\xc2\xf5(\x80u@' p4066 tp4067 Rp4068 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xa4u@' p4069 tp4070 Rp4071 F155.0 tp4072 a(I8190000 g1 (g5 S'\xd7\xa3p=\ncu@' p4073 tp4074 Rp4075 g4071 F196.0 tp4076 a(I8200000 g1 (g5 S'\xf6(\\\x8f\xc2\ru@' p4077 tp4078 Rp4079 g4071 F177.0 tp4080 a(I8210000 g1 (g5 S'\xc3\xf5(\\\x8f\xd2t@' p4081 tp4082 Rp4083 g4071 F383.0 tp4084 a(I8220000 g1 (g5 S'\xf6(\\\x8f\xc2%u@' p4085 tp4086 Rp4087 g4071 F683.0 tp4088 a(I8230000 g1 (g5 S'\xecQ\xb8\x1e\x85Su@' p4089 tp4090 Rp4091 g4071 F721.0 tp4092 a(I8240000 g1 (g5 S'fffffbu@' p4093 tp4094 Rp4095 g4071 F410.0 tp4096 a(I8250000 g1 (g5 S'\x1f\x85\xebQ\xb8\x96u@' p4097 tp4098 Rp4099 g4071 F446.0 tp4100 a(I8260000 g1 (g5 S'\n\xd7\xa3p=\xf2u@' p4101 tp4102 Rp4103 g1 (g5 S'\n\xd7\xa3p=\xf2u@' p4104 tp4105 Rp4106 F453.0 tp4107 a(I8270000 g1 (g5 S'{\x14\xaeG\xe1\xfau@' p4108 tp4109 Rp4110 g1 (g5 S'{\x14\xaeG\xe1\xfau@' p4111 tp4112 Rp4113 F445.0 tp4114 a(I8280000 g1 (g5 S'\x8f\xc2\xf5(\\3v@' p4115 tp4116 Rp4117 g1 (g5 S'\x8f\xc2\xf5(\\3v@' p4118 tp4119 Rp4120 F384.0 tp4121 a(I8290000 g1 (g5 S'\x00\x00\x00\x00\x000v@' p4122 tp4123 Rp4124 g4120 F374.0 tp4125 a(I8300000 g1 (g5 S'H\xe1z\x14\xaecv@' p4126 tp4127 Rp4128 g1 (g5 S'H\xe1z\x14\xaecv@' p4129 tp4130 Rp4131 F490.0 tp4132 a(I8310000 g1 (g5 S')\\\x8f\xc2\xf5dv@' p4133 tp4134 Rp4135 g1 (g5 S')\\\x8f\xc2\xf5dv@' p4136 tp4137 Rp4138 F194.0 tp4139 a(I8320000 g1 (g5 S'fffffJv@' p4140 tp4141 Rp4142 g1 (g5 S'\x8f\xc2\xf5(\\ov@' p4143 tp4144 Rp4145 F137.0 tp4146 a(I8330000 g1 (g5 S'\x14\xaeG\xe1z\x18v@' p4147 tp4148 Rp4149 g4145 F377.0 tp4150 a(I8340000 g1 (g5 S'=\n\xd7\xa3p\xcdu@' p4151 tp4152 Rp4153 g4145 F197.0 tp4154 a(I8350000 g1 (g5 S'q=\n\xd7\xa3\xd0u@' p4155 tp4156 Rp4157 g4145 F166.0 tp4158 a(I8360000 g1 (g5 S'33333\x83u@' p4159 tp4160 Rp4161 g4145 F161.0 tp4162 a(I8370000 g1 (g5 S'\xf6(\\\x8f\xc2\xc1u@' p4163 tp4164 Rp4165 g4145 F377.0 tp4166 a(I8380000 g1 (g5 S'\\\x8f\xc2\xf5(\xe8u@' p4167 tp4168 Rp4169 g4145 F401.0 tp4170 a(I8390000 g1 (g5 S')\\\x8f\xc2\xf5\xa4u@' p4171 tp4172 Rp4173 g4145 F197.0 tp4174 a(I8400000 g1 (g5 S'\xaeG\xe1z\x14\xaau@' p4175 tp4176 Rp4177 g4145 F443.0 tp4178 a(I8410000 g1 (g5 S'33333\xcfu@' p4179 tp4180 Rp4181 g4145 F490.0 tp4182 a(I8420000 g1 (g5 S')\\\x8f\xc2\xf5\xf0u@' p4183 tp4184 Rp4185 g4145 F404.0 tp4186 a(I8430000 g1 (g5 S'\x00\x00\x00\x00\x000v@' p4187 tp4188 Rp4189 g4145 F164.0 tp4190 a(I8440000 g1 (g5 S'\xe1z\x14\xaeGiv@' p4191 tp4192 Rp4193 g4145 F683.0 tp4194 a(I8450000 g1 (g5 S'fffff\xa6v@' p4195 tp4196 Rp4197 g1 (g5 S'fffff\xa6v@' p4198 tp4199 Rp4200 F164.0 tp4201 a(I8460000 g1 (g5 S'\xb8\x1e\x85\xebQ\xa0v@' p4202 tp4203 Rp4204 g1 (g5 S'{\x14\xaeG\xe1\xaav@' p4205 tp4206 Rp4207 F186.0 tp4208 a(I8470000 g1 (g5 S'\xecQ\xb8\x1e\x85\xc3v@' p4209 tp4210 Rp4211 g1 (g5 S'\xecQ\xb8\x1e\x85\xc3v@' p4212 tp4213 Rp4214 F348.0 tp4215 a(I8480000 g1 (g5 S'33333\xcfv@' p4216 tp4217 Rp4218 g1 (g5 S'33333\xcfv@' p4219 tp4220 Rp4221 F406.0 tp4222 a(I8490000 g1 (g5 S'\x8f\xc2\xf5(\\\xefv@' p4223 tp4224 Rp4225 g1 (g5 S'\x8f\xc2\xf5(\\\xefv@' p4226 tp4227 Rp4228 F390.0 tp4229 a(I8500000 g1 (g5 S'\x8f\xc2\xf5(\\\x17w@' p4230 tp4231 Rp4232 g1 (g5 S'\x8f\xc2\xf5(\\\x17w@' p4233 tp4234 Rp4235 F650.0 tp4236 a(I8510000 g1 (g5 S'\x00\x00\x00\x00\x00{@' p4760 tp4761 Rp4762 g1 (g5 S'\n\xd7\xa3p=>{@' p4763 tp4764 Rp4765 F450.0 tp4766 a(I9610000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x14{@' p4767 tp4768 Rp4769 g4765 F491.0 tp4770 a(I9620000 g1 (g5 S'=\n\xd7\xa3p\xe5z@' p4771 tp4772 Rp4773 g4765 F183.0 tp4774 a(I9630000 g1 (g5 S'\x9a\x99\x99\x99\x99\xf1z@' p4775 tp4776 Rp4777 g4765 F174.0 tp4778 a(I9640000 g1 (g5 S'\xb8\x1e\x85\xebQ {@' p4779 tp4780 Rp4781 g4765 F782.0 tp4782 a(I9650000 g1 (g5 S'R\xb8\x1e\x85\xeb\xcdz@' p4783 tp4784 Rp4785 g4765 F31.0 tp4786 a(I9660000 g1 (g5 S'\x9a\x99\x99\x99\x99qz@' p4787 tp4788 Rp4789 g4765 F197.0 tp4790 a(I9670000 g1 (g5 S'=\n\xd7\xa3pqz@' p4791 tp4792 Rp4793 g4765 F447.0 tp4794 a(I9680000 g1 (g5 S'fffff\xe6y@' p4795 tp4796 Rp4797 g4765 F64.0 tp4798 a(I9690000 g1 (g5 S'\xf6(\\\x8f\xc2yy@' p4799 tp4800 Rp4801 g4765 F173.0 tp4802 a(I9700000 g1 (g5 S'\x1f\x85\xebQ\xb8"y@' p4803 tp4804 Rp4805 g4765 F173.0 tp4806 a(I9710000 g1 (g5 S'\x1f\x85\xebQ\xb8\xaex@' p4807 tp4808 Rp4809 g4765 F64.0 tp4810 a(I9720000 g1 (g5 S'\xc3\xf5(\\\x8f\xb2x@' p4811 tp4812 Rp4813 g4765 F428.0 tp4814 a(I9730000 g1 (g5 S'\x85\xebQ\xb8\x1eIx@' p4815 tp4816 Rp4817 g4765 F290.0 tp4818 a(I9740000 g1 (g5 S'\xa4p=\n\xd7Gx@' p4819 tp4820 Rp4821 g4765 F189.0 tp4822 a(I9750000 g1 (g5 S'{\x14\xaeG\xe1Nx@' p4823 tp4824 Rp4825 g4765 F445.0 tp4826 a(I9760000 g1 (g5 S')\\\x8f\xc2\xf5\x04x@' p4827 tp4828 Rp4829 g4765 F195.0 tp4830 a(I9770000 g1 (g5 S'\xf6(\\\x8f\xc2\xc1w@' p4831 tp4832 Rp4833 g4765 F190.0 tp4834 a(I9780000 g1 (g5 S'\xd7\xa3p=\n\x0bx@' p4835 tp4836 Rp4837 g4765 F414.0 tp4838 a(I9790000 g1 (g5 S'\x00\x00\x00\x00\x00\x18x@' p4839 tp4840 Rp4841 g4765 F484.0 tp4842 a(I9800000 g1 (g5 S'\xb8\x1e\x85\xebQ\x1cx@' p4843 tp4844 Rp4845 g4765 F454.0 tp4846 a(I9810000 g1 (g5 S'\x14\xaeG\xe1z\x14x@' p4847 tp4848 Rp4849 g4765 F447.0 tp4850 a(I9820000 g1 (g5 S'\xecQ\xb8\x1e\x85Gx@' p4851 tp4852 Rp4853 g4765 F489.0 tp4854 a(I9830000 g1 (g5 S'\\\x8f\xc2\xf5(\x94w@' p4855 tp4856 Rp4857 g4765 F30.0 tp4858 a(I9840000 g1 (g5 S'fffff^w@' p4859 tp4860 Rp4861 g4765 F192.0 tp4862 a(I9850000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x80w@' p4863 tp4864 Rp4865 g4765 F396.0 tp4866 a(I9860000 g1 (g5 S'\x8f\xc2\xf5(\\+w@' p4867 tp4868 Rp4869 g4765 F157.0 tp4870 a(I9870000 g1 (g5 S'\xaeG\xe1z\x14"w@' p4871 tp4872 Rp4873 g4765 F188.0 tp4874 a(I9880000 g1 (g5 S'\n\xd7\xa3p=\xcev@' p4875 tp4876 Rp4877 g4765 F160.0 tp4878 a(I9890000 g1 (g5 S'\x8f\xc2\xf5(\\\xffv@' p4879 tp4880 Rp4881 g4765 F793.0 tp4882 a(I9900000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc(w@' p4883 tp4884 Rp4885 g4765 F708.0 tp4886 a(I9910000 g1 (g5 S'\xb8\x1e\x85\xebQ\x04w@' p4887 tp4888 Rp4889 g4765 F187.0 tp4890 a(I9920000 g1 (g5 S'\xb8\x1e\x85\xebQ4w@' p4891 tp4892 Rp4893 g4765 F492.0 tp4894 a(I9930000 g1 (g5 S'\x00\x00\x00\x00\x004w@' p4895 tp4896 Rp4897 g4765 F448.0 tp4898 a(I9940000 g1 (g5 S'\x85\xebQ\xb8\x1eYw@' p4899 tp4900 Rp4901 g4765 F693.0 tp4902 a(I9950000 g1 (g5 S'\x85\xebQ\xb8\x1e]w@' p4903 tp4904 Rp4905 g4765 F496.0 tp4906 a(I9960000 g1 (g5 S'{\x14\xaeG\xe16w@' p4907 tp4908 Rp4909 g4765 F489.0 tp4910 a(I9970000 g1 (g5 S'33333/w@' p4911 tp4912 Rp4913 g4765 F197.0 tp4914 a(I9980000 g1 (g5 S'\xa4p=\n\xd7\xb7v@' p4915 tp4916 Rp4917 g4765 F193.0 tp4918 a(I9985964 g1 (g5 S'\xa4p=\n\xd7\xb7v@' p4919 tp4920 Rp4921 g4765 F193.0 tp4922 a.(lp0 F0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 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S'\x00\x00\x00\x00\x00\x00\x00\x00' p32 tp33 Rp34 F-inf F0.0 tp35 a(I130000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p36 tp37 Rp38 F-inf F0.0 tp39 a(I140000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p40 tp41 Rp42 F-inf F0.0 tp43 a(I150000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p44 tp45 Rp46 F-inf F0.0 tp47 a(I160000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p48 tp49 Rp50 F-inf F0.0 tp51 a(I170000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p52 tp53 Rp54 F-inf F0.0 tp55 a(I180000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p56 tp57 Rp58 F-inf F0.0 tp59 a(I190000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p60 tp61 Rp62 F-inf F0.0 tp63 a(I200000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p64 tp65 Rp66 F-inf F0.0 tp67 a(I210000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p68 tp69 Rp70 F-inf F0.0 tp71 a(I220000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p72 tp73 Rp74 F-inf F0.0 tp75 a(I230000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p76 tp77 Rp78 F-inf F0.0 tp79 a(I240000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p80 tp81 Rp82 F-inf F0.0 tp83 a(I250000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p84 tp85 Rp86 F-inf F0.0 tp87 a(I260000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p88 tp89 Rp90 F-inf F0.0 tp91 a(I270000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p92 tp93 Rp94 F-inf F0.0 tp95 a(I280000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p96 tp97 Rp98 F-inf F0.0 tp99 a(I290000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p100 tp101 Rp102 F-inf F0.0 tp103 a(I300000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p104 tp105 Rp106 F-inf F0.0 tp107 a(I310000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p108 tp109 Rp110 F-inf F0.0 tp111 a(I320000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p112 tp113 Rp114 F-inf F0.0 tp115 a(I330000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p116 tp117 Rp118 F-inf F0.0 tp119 a(I340000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p120 tp121 Rp122 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p123 tp124 Rp125 F0.0 tp126 a(I350000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p127 tp128 Rp129 g125 F0.0 tp130 a(I360000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p131 tp132 Rp133 g125 F0.0 tp134 a(I370000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p135 tp136 Rp137 g125 F0.0 tp138 a(I380000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p139 tp140 Rp141 g125 F0.0 tp142 a(I390000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p143 tp144 Rp145 g125 F0.0 tp146 a(I400000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p147 tp148 Rp149 g125 F0.0 tp150 a(I410000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p151 tp152 Rp153 g125 F0.0 tp154 a(I420000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p155 tp156 Rp157 g125 F0.0 tp158 a(I430000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p159 tp160 Rp161 g125 F0.0 tp162 a(I440000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p163 tp164 Rp165 g125 F0.0 tp166 a(I450000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p167 tp168 Rp169 g125 F0.0 tp170 a(I460000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p171 tp172 Rp173 g125 F0.0 tp174 a(I470000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p175 tp176 Rp177 g125 F0.0 tp178 a(I480000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p179 tp180 Rp181 g125 F0.0 tp182 a(I490000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p183 tp184 Rp185 g125 F0.0 tp186 a(I500000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p187 tp188 Rp189 g125 F0.0 tp190 a(I510000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p191 tp192 Rp193 g125 F0.0 tp194 a(I520000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p195 tp196 Rp197 g125 F0.0 tp198 a(I530000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p199 tp200 Rp201 g125 F0.0 tp202 a(I540000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p203 tp204 Rp205 g125 F0.0 tp206 a(I550000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p207 tp208 Rp209 g125 F0.0 tp210 a(I560000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p211 tp212 Rp213 g125 F0.0 tp214 a(I570000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p215 tp216 Rp217 g125 F0.0 tp218 a(I580000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p219 tp220 Rp221 g125 F0.0 tp222 a(I590000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p223 tp224 Rp225 g125 F0.0 tp226 a(I600000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p227 tp228 Rp229 g125 F0.0 tp230 a(I610000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p231 tp232 Rp233 g125 F0.0 tp234 a(I620000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p235 tp236 Rp237 g125 F0.0 tp238 a(I630000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p239 tp240 Rp241 g125 F0.0 tp242 a(I640000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p243 tp244 Rp245 g125 F0.0 tp246 a(I650000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p247 tp248 Rp249 g125 F0.0 tp250 a(I660000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p251 tp252 Rp253 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p254 tp255 Rp256 F0.0 tp257 a(I670000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p258 tp259 Rp260 g256 F0.0 tp261 a(I680000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p262 tp263 Rp264 g256 F0.0 tp265 a(I690000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p266 tp267 Rp268 g256 F0.0 tp269 a(I700000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p270 tp271 Rp272 g256 F0.0 tp273 a(I710000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p274 tp275 Rp276 g256 F0.0 tp277 a(I720000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p278 tp279 Rp280 g256 F0.0 tp281 a(I730000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p282 tp283 Rp284 g256 F0.0 tp285 a(I740000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p286 tp287 Rp288 g256 F0.0 tp289 a(I750000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p290 tp291 Rp292 g256 F0.0 tp293 a(I760000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p294 tp295 Rp296 g256 F0.0 tp297 a(I770000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p298 tp299 Rp300 g256 F0.0 tp301 a(I780000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p302 tp303 Rp304 g256 F0.0 tp305 a(I790000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p306 tp307 Rp308 g256 F0.0 tp309 a(I800000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p310 tp311 Rp312 g256 F0.0 tp313 a(I810000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p314 tp315 Rp316 g256 F0.0 tp317 a(I820000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p318 tp319 Rp320 g256 F0.0 tp321 a(I830000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p322 tp323 Rp324 g256 F0.0 tp325 a(I840000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p326 tp327 Rp328 g256 F0.0 tp329 a(I850000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p330 tp331 Rp332 g256 F0.0 tp333 a(I860000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p334 tp335 Rp336 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p337 tp338 Rp339 F4.0 tp340 a(I870000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p341 tp342 Rp343 g339 F0.0 tp344 a(I880000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p345 tp346 Rp347 g339 F0.0 tp348 a(I890000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xb9?' p349 tp350 Rp351 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xb9?' p352 tp353 Rp354 F0.0 tp355 a(I900000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p356 tp357 Rp358 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p359 tp360 Rp361 F0.0 tp362 a(I910000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p363 tp364 Rp365 g361 F0.0 tp366 a(I920000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p367 tp368 Rp369 g361 F0.0 tp370 a(I930000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p371 tp372 Rp373 g361 F0.0 tp374 a(I940000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p375 tp376 Rp377 g361 F0.0 tp378 a(I950000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p379 tp380 Rp381 g361 F0.0 tp382 a(I960000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p383 tp384 Rp385 g361 F0.0 tp386 a(I970000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p387 tp388 Rp389 g361 F0.0 tp390 a(I980000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xc1?' p391 tp392 Rp393 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xc1?' p394 tp395 Rp396 F0.0 tp397 a(I990000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xc1?' p398 tp399 Rp400 g1 (g5 S'333333\xc3?' p401 tp402 Rp403 F0.0 tp404 a(I1000000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xc1?' p405 tp406 Rp407 g403 F0.0 tp408 a(I1010000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xc1?' p409 tp410 Rp411 g403 F0.0 tp412 a(I1020000 g1 (g5 S'333333\xc3?' p413 tp414 Rp415 g403 F0.0 tp416 a(I1030000 g1 (g5 S'333333\xc3?' p417 tp418 Rp419 g403 F0.0 tp420 a(I1040000 g1 (g5 S'R\xb8\x1e\x85\xebQ\xc8?' p421 tp422 Rp423 g1 (g5 S'R\xb8\x1e\x85\xebQ\xc8?' p424 tp425 Rp426 F0.0 tp427 a(I1050000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xce?' p428 tp429 Rp430 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xce?' p431 tp432 Rp433 F0.0 tp434 a(I1060000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xce?' p435 tp436 Rp437 g433 F0.0 tp438 a(I1070000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xce?' p439 tp440 Rp441 g433 F0.0 tp442 a(I1080000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xce?' p443 tp444 Rp445 g433 F0.0 tp446 a(I1090000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xce?' p447 tp448 Rp449 g433 F0.0 tp450 a(I1100000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xce?' p451 tp452 Rp453 g433 F0.0 tp454 a(I1110000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xce?' p455 tp456 Rp457 g433 F0.0 tp458 a(I1120000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xce?' p459 tp460 Rp461 g433 F0.0 tp462 a(I1130000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xce?' p463 tp464 Rp465 g433 F0.0 tp466 a(I1140000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xce?' p467 tp468 Rp469 g433 F0.0 tp470 a(I1150000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xce?' p471 tp472 Rp473 g433 F0.0 tp474 a(I1160000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xce?' p475 tp476 Rp477 g433 F0.0 tp478 a(I1170000 g1 (g5 S'\xa4p=\n\xd7\xa3\xd0?' p479 tp480 Rp481 g1 (g5 S'\xa4p=\n\xd7\xa3\xd0?' p482 tp483 Rp484 F0.0 tp485 a(I1180000 g1 (g5 S'\xa4p=\n\xd7\xa3\xd0?' p486 tp487 Rp488 g484 F0.0 tp489 a(I1190000 g1 (g5 S')\\\x8f\xc2\xf5(\xcc?' p490 tp491 Rp492 g484 F0.0 tp493 a(I1200000 g1 (g5 S')\\\x8f\xc2\xf5(\xcc?' p494 tp495 Rp496 g484 F0.0 tp497 a(I1210000 g1 (g5 S')\\\x8f\xc2\xf5(\xcc?' p498 tp499 Rp500 g484 F0.0 tp501 a(I1220000 g1 (g5 S'\xc3\xf5(\\\x8f\xc2\xc5?' p502 tp503 Rp504 g484 F0.0 tp505 a(I1230000 g1 (g5 S'{\x14\xaeG\xe1z\xc4?' p506 tp507 Rp508 g484 F0.0 tp509 a(I1240000 g1 (g5 S'{\x14\xaeG\xe1z\xc4?' p510 tp511 Rp512 g484 F0.0 tp513 a(I1250000 g1 (g5 S'{\x14\xaeG\xe1z\xc4?' p514 tp515 Rp516 g484 F0.0 tp517 a(I1260000 g1 (g5 S'\xc3\xf5(\\\x8f\xc2\xc5?' p518 tp519 Rp520 g484 F0.0 tp521 a(I1270000 g1 (g5 S'\xc3\xf5(\\\x8f\xc2\xc5?' p522 tp523 Rp524 g484 F0.0 tp525 a(I1280000 g1 (g5 S'\xc3\xf5(\\\x8f\xc2\xc5?' p526 tp527 Rp528 g484 F0.0 tp529 a(I1290000 g1 (g5 S'\xc3\xf5(\\\x8f\xc2\xc5?' p530 tp531 Rp532 g484 F0.0 tp533 a(I1300000 g1 (g5 S'\xc3\xf5(\\\x8f\xc2\xc5?' p534 tp535 Rp536 g484 F0.0 tp537 a(I1310000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xc1?' p538 tp539 Rp540 g484 F0.0 tp541 a(I1320000 g1 (g5 S'\xa4p=\n\xd7\xa3\xc0?' p542 tp543 Rp544 g484 F0.0 tp545 a(I1330000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xc1?' p546 tp547 Rp548 g484 F0.0 tp549 a(I1340000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xc1?' p550 tp551 Rp552 g484 F0.0 tp553 a(I1350000 g1 (g5 S'\xa4p=\n\xd7\xa3\xc0?' p554 tp555 Rp556 g484 F0.0 tp557 a(I1360000 g1 (g5 S'\xa4p=\n\xd7\xa3\xc0?' p558 tp559 Rp560 g484 F0.0 tp561 a(I1370000 g1 (g5 S'\n\xd7\xa3p=\n\xb7?' p562 tp563 Rp564 g484 F0.0 tp565 a(I1380000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p566 tp567 Rp568 g484 F0.0 tp569 a(I1390000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p570 tp571 Rp572 g484 F0.0 tp573 a(I1400000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p574 tp575 Rp576 g484 F0.0 tp577 a(I1410000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p578 tp579 Rp580 g484 F0.0 tp581 a(I1420000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p582 tp583 Rp584 g484 F0.0 tp585 a(I1430000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p586 tp587 Rp588 g484 F0.0 tp589 a(I1440000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p590 tp591 Rp592 g484 F0.0 tp593 a(I1450000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p594 tp595 Rp596 g484 F0.0 tp597 a(I1460000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p598 tp599 Rp600 g484 F1.0 tp601 a(I1470000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p602 tp603 Rp604 g484 F0.0 tp605 a(I1480000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p606 tp607 Rp608 g484 F0.0 tp609 a(I1490000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p610 tp611 Rp612 g484 F0.0 tp613 a(I1500000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p614 tp615 Rp616 g484 F0.0 tp617 a(I1510000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p618 tp619 Rp620 g484 F0.0 tp621 a(I1520000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p622 tp623 Rp624 g484 F0.0 tp625 a(I1530000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p626 tp627 Rp628 g484 F0.0 tp629 a(I1540000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p630 tp631 Rp632 g484 F0.0 tp633 a(I1550000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p634 tp635 Rp636 g484 F0.0 tp637 a(I1560000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p638 tp639 Rp640 g484 F0.0 tp641 a(I1570000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p642 tp643 Rp644 g484 F0.0 tp645 a(I1580000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p646 tp647 Rp648 g484 F0.0 tp649 a(I1590000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p650 tp651 Rp652 g484 F0.0 tp653 a(I1600000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p654 tp655 Rp656 g484 F0.0 tp657 a(I1610000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p658 tp659 Rp660 g484 F0.0 tp661 a(I1620000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p662 tp663 Rp664 g484 F0.0 tp665 a(I1630000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p666 tp667 Rp668 g484 F0.0 tp669 a(I1640000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p670 tp671 Rp672 g484 F0.0 tp673 a(I1650000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p674 tp675 Rp676 g484 F0.0 tp677 a(I1660000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p678 tp679 Rp680 g484 F0.0 tp681 a(I1670000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p682 tp683 Rp684 g484 F0.0 tp685 a(I1680000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p686 tp687 Rp688 g484 F0.0 tp689 a(I1690000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p690 tp691 Rp692 g484 F0.0 tp693 a(I1700000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p694 tp695 Rp696 g484 F0.0 tp697 a(I1710000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p698 tp699 Rp700 g484 F0.0 tp701 a(I1720000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p702 tp703 Rp704 g484 F0.0 tp705 a(I1730000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p706 tp707 Rp708 g484 F0.0 tp709 a(I1740000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p710 tp711 Rp712 g484 F0.0 tp713 a(I1750000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p714 tp715 Rp716 g484 F0.0 tp717 a(I1760000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p718 tp719 Rp720 g484 F0.0 tp721 a(I1770000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p722 tp723 Rp724 g484 F0.0 tp725 a(I1780000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p726 tp727 Rp728 g484 F0.0 tp729 a(I1790000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p730 tp731 Rp732 g484 F0.0 tp733 a(I1800000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p734 tp735 Rp736 g484 F0.0 tp737 a(I1810000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p738 tp739 Rp740 g484 F0.0 tp741 a(I1820000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p742 tp743 Rp744 g484 F0.0 tp745 a(I1830000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p746 tp747 Rp748 g484 F0.0 tp749 a(I1840000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p750 tp751 Rp752 g484 F0.0 tp753 a(I1850000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p754 tp755 Rp756 g484 F0.0 tp757 a(I1860000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p758 tp759 Rp760 g484 F0.0 tp761 a(I1870000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p762 tp763 Rp764 g484 F0.0 tp765 a(I1880000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p766 tp767 Rp768 g484 F0.0 tp769 a(I1890000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p770 tp771 Rp772 g484 F0.0 tp773 a(I1900000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p774 tp775 Rp776 g484 F0.0 tp777 a(I1910000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p778 tp779 Rp780 g484 F1.0 tp781 a(I1920000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p782 tp783 Rp784 g484 F0.0 tp785 a(I1930000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p786 tp787 Rp788 g484 F0.0 tp789 a(I1940000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p790 tp791 Rp792 g484 F0.0 tp793 a(I1950000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p794 tp795 Rp796 g484 F0.0 tp797 a(I1960000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p798 tp799 Rp800 g484 F0.0 tp801 a(I1970000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p802 tp803 Rp804 g484 F0.0 tp805 a(I1980000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p806 tp807 Rp808 g484 F0.0 tp809 a(I1990000 g1 (g5 S'\n\xd7\xa3p=\n\xb7?' p810 tp811 Rp812 g484 F0.0 tp813 a(I2000000 g1 (g5 S'\n\xd7\xa3p=\n\xb7?' p814 tp815 Rp816 g484 F0.0 tp817 a(I2010000 g1 (g5 S'\n\xd7\xa3p=\n\xb7?' p818 tp819 Rp820 g484 F0.0 tp821 a(I2020000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xb9?' p822 tp823 Rp824 g484 F1.0 tp825 a(I2030000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xb9?' p826 tp827 Rp828 g484 F0.0 tp829 a(I2040000 g1 (g5 S'\n\xd7\xa3p=\n\xb7?' p830 tp831 Rp832 g484 F0.0 tp833 a(I2050000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xb9?' p834 tp835 Rp836 g484 F1.0 tp837 a(I2060000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xb9?' p838 tp839 Rp840 g484 F0.0 tp841 a(I2070000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xb9?' p842 tp843 Rp844 g484 F0.0 tp845 a(I2080000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p846 tp847 Rp848 g484 F0.0 tp849 a(I2090000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p850 tp851 Rp852 g484 F0.0 tp853 a(I2100000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p854 tp855 Rp856 g484 F0.0 tp857 a(I2110000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p858 tp859 Rp860 g484 F0.0 tp861 a(I2120000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p862 tp863 Rp864 g484 F0.0 tp865 a(I2130000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xbe?' p866 tp867 Rp868 g484 F0.0 tp869 a(I2140000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xbe?' p870 tp871 Rp872 g484 F0.0 tp873 a(I2150000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xbe?' p874 tp875 Rp876 g484 F0.0 tp877 a(I2160000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p878 tp879 Rp880 g484 F1.0 tp881 a(I2170000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p882 tp883 Rp884 g484 F0.0 tp885 a(I2180000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p886 tp887 Rp888 g484 F0.0 tp889 a(I2190000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p890 tp891 Rp892 g484 F0.0 tp893 a(I2200000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p894 tp895 Rp896 g484 F0.0 tp897 a(I2210000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p898 tp899 Rp900 g484 F0.0 tp901 a(I2220000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p902 tp903 Rp904 g484 F0.0 tp905 a(I2230000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p906 tp907 Rp908 g484 F0.0 tp909 a(I2240000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xb9?' p910 tp911 Rp912 g484 F0.0 tp913 a(I2250000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xb9?' p914 tp915 Rp916 g484 F0.0 tp917 a(I2260000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xb9?' p918 tp919 Rp920 g484 F0.0 tp921 a(I2270000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p922 tp923 Rp924 g484 F0.0 tp925 a(I2280000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p926 tp927 Rp928 g484 F0.0 tp929 a(I2290000 g1 (g5 S')\\\x8f\xc2\xf5(\xbc?' p930 tp931 Rp932 g484 F0.0 tp933 a(I2300000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xb9?' p934 tp935 Rp936 g484 F0.0 tp937 a(I2310000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xb9?' p938 tp939 Rp940 g484 F0.0 tp941 a(I2320000 g1 (g5 S'\n\xd7\xa3p=\n\xb7?' p942 tp943 Rp944 g484 F0.0 tp945 a(I2330000 g1 (g5 S'\n\xd7\xa3p=\n\xb7?' p946 tp947 Rp948 g484 F0.0 tp949 a(I2340000 g1 (g5 S'\n\xd7\xa3p=\n\xb7?' p950 tp951 Rp952 g484 F0.0 tp953 a(I2350000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p954 tp955 Rp956 g484 F0.0 tp957 a(I2360000 g1 (g5 S'\n\xd7\xa3p=\n\xb7?' p958 tp959 Rp960 g484 F1.0 tp961 a(I2370000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xb9?' p962 tp963 Rp964 g484 F0.0 tp965 a(I2380000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xb9?' p966 tp967 Rp968 g484 F0.0 tp969 a(I2390000 g1 (g5 S'\n\xd7\xa3p=\n\xb7?' p970 tp971 Rp972 g484 F0.0 tp973 a(I2400000 g1 (g5 S'\n\xd7\xa3p=\n\xb7?' p974 tp975 Rp976 g484 F0.0 tp977 a(I2410000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xb9?' p978 tp979 Rp980 g484 F0.0 tp981 a(I2420000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xb9?' p982 tp983 Rp984 g484 F0.0 tp985 a(I2430000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xb9?' p986 tp987 Rp988 g484 F0.0 tp989 a(I2440000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xb9?' p990 tp991 Rp992 g484 F0.0 tp993 a(I2450000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xb9?' p994 tp995 Rp996 g484 F0.0 tp997 a(I2460000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p998 tp999 Rp1000 g484 F0.0 tp1001 a(I2470000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1002 tp1003 Rp1004 g484 F0.0 tp1005 a(I2480000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1006 tp1007 Rp1008 g484 F0.0 tp1009 a(I2490000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p1010 tp1011 Rp1012 g484 F0.0 tp1013 a(I2500000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1014 tp1015 Rp1016 g484 F0.0 tp1017 a(I2510000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1018 tp1019 Rp1020 g484 F0.0 tp1021 a(I2520000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1022 tp1023 Rp1024 g484 F0.0 tp1025 a(I2530000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1026 tp1027 Rp1028 g484 F0.0 tp1029 a(I2540000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1030 tp1031 Rp1032 g484 F0.0 tp1033 a(I2550000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1034 tp1035 Rp1036 g484 F0.0 tp1037 a(I2560000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p1038 tp1039 Rp1040 g484 F0.0 tp1041 a(I2570000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p1042 tp1043 Rp1044 g484 F0.0 tp1045 a(I2580000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p1046 tp1047 Rp1048 g484 F0.0 tp1049 a(I2590000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p1050 tp1051 Rp1052 g484 F1.0 tp1053 a(I2600000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p1054 tp1055 Rp1056 g484 F0.0 tp1057 a(I2610000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p1058 tp1059 Rp1060 g484 F0.0 tp1061 a(I2620000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p1062 tp1063 Rp1064 g484 F0.0 tp1065 a(I2630000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p1066 tp1067 Rp1068 g484 F0.0 tp1069 a(I2640000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p1070 tp1071 Rp1072 g484 F0.0 tp1073 a(I2650000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p1074 tp1075 Rp1076 g484 F0.0 tp1077 a(I2660000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p1078 tp1079 Rp1080 g484 F0.0 tp1081 a(I2670000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p1082 tp1083 Rp1084 g484 F0.0 tp1085 a(I2680000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p1086 tp1087 Rp1088 g484 F0.0 tp1089 a(I2690000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p1090 tp1091 Rp1092 g484 F0.0 tp1093 a(I2700000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1094 tp1095 Rp1096 g484 F0.0 tp1097 a(I2710000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1098 tp1099 Rp1100 g484 F0.0 tp1101 a(I2720000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1102 tp1103 Rp1104 g484 F0.0 tp1105 a(I2730000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1106 tp1107 Rp1108 g484 F0.0 tp1109 a(I2740000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1110 tp1111 Rp1112 g484 F0.0 tp1113 a(I2750000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1114 tp1115 Rp1116 g484 F0.0 tp1117 a(I2760000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1118 tp1119 Rp1120 g484 F0.0 tp1121 a(I2770000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1122 tp1123 Rp1124 g484 F0.0 tp1125 a(I2780000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1126 tp1127 Rp1128 g484 F0.0 tp1129 a(I2790000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1130 tp1131 Rp1132 g484 F0.0 tp1133 a(I2800000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1134 tp1135 Rp1136 g484 F1.0 tp1137 a(I2810000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1138 tp1139 Rp1140 g484 F0.0 tp1141 a(I2820000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1142 tp1143 Rp1144 g484 F0.0 tp1145 a(I2830000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1146 tp1147 Rp1148 g484 F0.0 tp1149 a(I2840000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1150 tp1151 Rp1152 g484 F0.0 tp1153 a(I2850000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1154 tp1155 Rp1156 g484 F0.0 tp1157 a(I2860000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1158 tp1159 Rp1160 g484 F0.0 tp1161 a(I2870000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1162 tp1163 Rp1164 g484 F0.0 tp1165 a(I2880000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1166 tp1167 Rp1168 g484 F0.0 tp1169 a(I2890000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1170 tp1171 Rp1172 g484 F0.0 tp1173 a(I2900000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1174 tp1175 Rp1176 g484 F1.0 tp1177 a(I2910000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1178 tp1179 Rp1180 g484 F0.0 tp1181 a(I2920000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1182 tp1183 Rp1184 g484 F0.0 tp1185 a(I2930000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1186 tp1187 Rp1188 g484 F0.0 tp1189 a(I2940000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1190 tp1191 Rp1192 g484 F0.0 tp1193 a(I2950000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1194 tp1195 Rp1196 g484 F1.0 tp1197 a(I2960000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1198 tp1199 Rp1200 g484 F0.0 tp1201 a(I2970000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1202 tp1203 Rp1204 g484 F0.0 tp1205 a(I2980000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1206 tp1207 Rp1208 g484 F0.0 tp1209 a(I2990000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1210 tp1211 Rp1212 g484 F0.0 tp1213 a(I3000000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1214 tp1215 Rp1216 g484 F0.0 tp1217 a(I3010000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1218 tp1219 Rp1220 g484 F0.0 tp1221 a(I3020000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1222 tp1223 Rp1224 g484 F0.0 tp1225 a(I3030000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1226 tp1227 Rp1228 g484 F0.0 tp1229 a(I3040000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1230 tp1231 Rp1232 g484 F0.0 tp1233 a(I3050000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1234 tp1235 Rp1236 g484 F0.0 tp1237 a(I3060000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1238 tp1239 Rp1240 g484 F0.0 tp1241 a(I3070000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1242 tp1243 Rp1244 g484 F0.0 tp1245 a(I3080000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1246 tp1247 Rp1248 g484 F0.0 tp1249 a(I3090000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1250 tp1251 Rp1252 g484 F0.0 tp1253 a(I3100000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1254 tp1255 Rp1256 g484 F0.0 tp1257 a(I3110000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p1258 tp1259 Rp1260 g484 F2.0 tp1261 a(I3120000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p1262 tp1263 Rp1264 g484 F0.0 tp1265 a(I3130000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1266 tp1267 Rp1268 g484 F0.0 tp1269 a(I3140000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1270 tp1271 Rp1272 g484 F0.0 tp1273 a(I3150000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1274 tp1275 Rp1276 g484 F0.0 tp1277 a(I3160000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1278 tp1279 Rp1280 g484 F0.0 tp1281 a(I3170000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1282 tp1283 Rp1284 g484 F0.0 tp1285 a(I3180000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1286 tp1287 Rp1288 g484 F0.0 tp1289 a(I3190000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1290 tp1291 Rp1292 g484 F0.0 tp1293 a(I3200000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1294 tp1295 Rp1296 g484 F0.0 tp1297 a(I3210000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1298 tp1299 Rp1300 g484 F0.0 tp1301 a(I3220000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xb1?' p1302 tp1303 Rp1304 g484 F0.0 tp1305 a(I3230000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p1306 tp1307 Rp1308 g484 F0.0 tp1309 a(I3240000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p1310 tp1311 Rp1312 g484 F0.0 tp1313 a(I3250000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p1314 tp1315 Rp1316 g484 F0.0 tp1317 a(I3260000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xae?' p1318 tp1319 Rp1320 g484 F0.0 tp1321 a(I3270000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1322 tp1323 Rp1324 g484 F0.0 tp1325 a(I3280000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1326 tp1327 Rp1328 g484 F0.0 tp1329 a(I3290000 g1 (g5 S'{\x14\xaeG\xe1z\xa4?' p1330 tp1331 Rp1332 g484 F0.0 tp1333 a(I3300000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1334 tp1335 Rp1336 g484 F1.0 tp1337 a(I3310000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1338 tp1339 Rp1340 g484 F0.0 tp1341 a(I3320000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1342 tp1343 Rp1344 g484 F0.0 tp1345 a(I3330000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1346 tp1347 Rp1348 g484 F0.0 tp1349 a(I3340000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1350 tp1351 Rp1352 g484 F0.0 tp1353 a(I3350000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1354 tp1355 Rp1356 g484 F0.0 tp1357 a(I3360000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1358 tp1359 Rp1360 g484 F0.0 tp1361 a(I3370000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1362 tp1363 Rp1364 g484 F0.0 tp1365 a(I3380000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1366 tp1367 Rp1368 g484 F0.0 tp1369 a(I3390000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1370 tp1371 Rp1372 g484 F0.0 tp1373 a(I3400000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1374 tp1375 Rp1376 g484 F0.0 tp1377 a(I3410000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1378 tp1379 Rp1380 g484 F0.0 tp1381 a(I3420000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1382 tp1383 Rp1384 g484 F0.0 tp1385 a(I3430000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1386 tp1387 Rp1388 g484 F0.0 tp1389 a(I3440000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xa9?' p1390 tp1391 Rp1392 g484 F0.0 tp1393 a(I3450000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1394 tp1395 Rp1396 g484 F0.0 tp1397 a(I3460000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1398 tp1399 Rp1400 g484 F0.0 tp1401 a(I3470000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1402 tp1403 Rp1404 g484 F0.0 tp1405 a(I3480000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1406 tp1407 Rp1408 g484 F0.0 tp1409 a(I3490000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1410 tp1411 Rp1412 g484 F0.0 tp1413 a(I3500000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1414 tp1415 Rp1416 g484 F0.0 tp1417 a(I3510000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1418 tp1419 Rp1420 g484 F0.0 tp1421 a(I3520000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1422 tp1423 Rp1424 g484 F0.0 tp1425 a(I3530000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1426 tp1427 Rp1428 g484 F0.0 tp1429 a(I3540000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1430 tp1431 Rp1432 g484 F0.0 tp1433 a(I3550000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p1434 tp1435 Rp1436 g484 F0.0 tp1437 a(I3560000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p1438 tp1439 Rp1440 g484 F0.0 tp1441 a(I3570000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p1442 tp1443 Rp1444 g484 F0.0 tp1445 a(I3580000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p1446 tp1447 Rp1448 g484 F0.0 tp1449 a(I3590000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p1450 tp1451 Rp1452 g484 F0.0 tp1453 a(I3600000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p1454 tp1455 Rp1456 g484 F0.0 tp1457 a(I3610000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1458 tp1459 Rp1460 g484 F0.0 tp1461 a(I3620000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1462 tp1463 Rp1464 g484 F0.0 tp1465 a(I3630000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1466 tp1467 Rp1468 g484 F0.0 tp1469 a(I3640000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1470 tp1471 Rp1472 g484 F0.0 tp1473 a(I3650000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1474 tp1475 Rp1476 g484 F0.0 tp1477 a(I3660000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1478 tp1479 Rp1480 g484 F0.0 tp1481 a(I3670000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1482 tp1483 Rp1484 g484 F0.0 tp1485 a(I3680000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1486 tp1487 Rp1488 g484 F0.0 tp1489 a(I3690000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1490 tp1491 Rp1492 g484 F0.0 tp1493 a(I3700000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1494 tp1495 Rp1496 g484 F0.0 tp1497 a(I3710000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1498 tp1499 Rp1500 g484 F0.0 tp1501 a(I3720000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1502 tp1503 Rp1504 g484 F0.0 tp1505 a(I3730000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1506 tp1507 Rp1508 g484 F0.0 tp1509 a(I3740000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1510 tp1511 Rp1512 g484 F0.0 tp1513 a(I3750000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1514 tp1515 Rp1516 g484 F0.0 tp1517 a(I3760000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1518 tp1519 Rp1520 g484 F0.0 tp1521 a(I3770000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1522 tp1523 Rp1524 g484 F0.0 tp1525 a(I3780000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1526 tp1527 Rp1528 g484 F0.0 tp1529 a(I3790000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1530 tp1531 Rp1532 g484 F0.0 tp1533 a(I3800000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1534 tp1535 Rp1536 g484 F0.0 tp1537 a(I3810000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1538 tp1539 Rp1540 g484 F0.0 tp1541 a(I3820000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1542 tp1543 Rp1544 g484 F0.0 tp1545 a(I3830000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1546 tp1547 Rp1548 g484 F0.0 tp1549 a(I3840000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1550 tp1551 Rp1552 g484 F0.0 tp1553 a(I3850000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1554 tp1555 Rp1556 g484 F0.0 tp1557 a(I3860000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1558 tp1559 Rp1560 g484 F0.0 tp1561 a(I3870000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1562 tp1563 Rp1564 g484 F0.0 tp1565 a(I3880000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1566 tp1567 Rp1568 g484 F0.0 tp1569 a(I3890000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1570 tp1571 Rp1572 g484 F0.0 tp1573 a(I3900000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1574 tp1575 Rp1576 g484 F0.0 tp1577 a(I3910000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1578 tp1579 Rp1580 g484 F0.0 tp1581 a(I3920000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1582 tp1583 Rp1584 g484 F0.0 tp1585 a(I3930000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1586 tp1587 Rp1588 g484 F0.0 tp1589 a(I3940000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1590 tp1591 Rp1592 g484 F2.0 tp1593 a(I3950000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1594 tp1595 Rp1596 g484 F0.0 tp1597 a(I3960000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1598 tp1599 Rp1600 g484 F0.0 tp1601 a(I3970000 g1 (g5 S'{\x14\xaeG\xe1z\x94?' p1602 tp1603 Rp1604 g484 F0.0 tp1605 a(I3980000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1606 tp1607 Rp1608 g484 F0.0 tp1609 a(I3990000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1610 tp1611 Rp1612 g484 F0.0 tp1613 a(I4000000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1614 tp1615 Rp1616 g484 F0.0 tp1617 a(I4010000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1618 tp1619 Rp1620 g484 F0.0 tp1621 a(I4020000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1622 tp1623 Rp1624 g484 F0.0 tp1625 a(I4030000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1626 tp1627 Rp1628 g484 F0.0 tp1629 a(I4040000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1630 tp1631 Rp1632 g484 F0.0 tp1633 a(I4050000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1634 tp1635 Rp1636 g484 F0.0 tp1637 a(I4060000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1638 tp1639 Rp1640 g484 F0.0 tp1641 a(I4070000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1642 tp1643 Rp1644 g484 F0.0 tp1645 a(I4080000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1646 tp1647 Rp1648 g484 F0.0 tp1649 a(I4090000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1650 tp1651 Rp1652 g484 F0.0 tp1653 a(I4100000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1654 tp1655 Rp1656 g484 F0.0 tp1657 a(I4110000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1658 tp1659 Rp1660 g484 F0.0 tp1661 a(I4120000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1662 tp1663 Rp1664 g484 F0.0 tp1665 a(I4130000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1666 tp1667 Rp1668 g484 F0.0 tp1669 a(I4140000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1670 tp1671 Rp1672 g484 F0.0 tp1673 a(I4150000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1674 tp1675 Rp1676 g484 F0.0 tp1677 a(I4160000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1678 tp1679 Rp1680 g484 F0.0 tp1681 a(I4170000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1682 tp1683 Rp1684 g484 F0.0 tp1685 a(I4180000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1686 tp1687 Rp1688 g484 F0.0 tp1689 a(I4190000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1690 tp1691 Rp1692 g484 F0.0 tp1693 a(I4200000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1694 tp1695 Rp1696 g484 F0.0 tp1697 a(I4210000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1698 tp1699 Rp1700 g484 F0.0 tp1701 a(I4220000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1702 tp1703 Rp1704 g484 F0.0 tp1705 a(I4230000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1706 tp1707 Rp1708 g484 F0.0 tp1709 a(I4240000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1710 tp1711 Rp1712 g484 F0.0 tp1713 a(I4250000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1714 tp1715 Rp1716 g484 F0.0 tp1717 a(I4260000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x9e?' p1718 tp1719 Rp1720 g484 F0.0 tp1721 a(I4270000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p1722 tp1723 Rp1724 g484 F0.0 tp1725 a(I4280000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p1726 tp1727 Rp1728 g484 F0.0 tp1729 a(I4290000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p1730 tp1731 Rp1732 g484 F0.0 tp1733 a(I4300000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p1734 tp1735 Rp1736 g484 F0.0 tp1737 a(I4310000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p1738 tp1739 Rp1740 g484 F0.0 tp1741 a(I4320000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p1742 tp1743 Rp1744 g484 F0.0 tp1745 a(I4330000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p1746 tp1747 Rp1748 g484 F0.0 tp1749 a(I4340000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p1750 tp1751 Rp1752 g484 F0.0 tp1753 a(I4350000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p1754 tp1755 Rp1756 g484 F0.0 tp1757 a(I4360000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p1758 tp1759 Rp1760 g484 F0.0 tp1761 a(I4370000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p1762 tp1763 Rp1764 g484 F0.0 tp1765 a(I4380000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p1766 tp1767 Rp1768 g484 F0.0 tp1769 a(I4390000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p1770 tp1771 Rp1772 g484 F0.0 tp1773 a(I4400000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p1774 tp1775 Rp1776 g484 F0.0 tp1777 a(I4410000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p1778 tp1779 Rp1780 g484 F0.0 tp1781 a(I4420000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p1782 tp1783 Rp1784 g484 F0.0 tp1785 a(I4430000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p1786 tp1787 Rp1788 g484 F0.0 tp1789 a(I4440000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p1790 tp1791 Rp1792 g484 F0.0 tp1793 a(I4450000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p1794 tp1795 Rp1796 g484 F0.0 tp1797 a(I4460000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p1798 tp1799 Rp1800 g484 F0.0 tp1801 a(I4470000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p1802 tp1803 Rp1804 g484 F0.0 tp1805 a(I4480000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p1806 tp1807 Rp1808 g484 F0.0 tp1809 a(I4490000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p1810 tp1811 Rp1812 g484 F0.0 tp1813 a(I4500000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p1814 tp1815 Rp1816 g484 F0.0 tp1817 a(I4510000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p1818 tp1819 Rp1820 g484 F0.0 tp1821 a(I4520000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p1822 tp1823 Rp1824 g484 F0.0 tp1825 a(I4530000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p1826 tp1827 Rp1828 g484 F0.0 tp1829 a(I4540000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p1830 tp1831 Rp1832 g484 F0.0 tp1833 a(I4550000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p1834 tp1835 Rp1836 g484 F0.0 tp1837 a(I4560000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p1838 tp1839 Rp1840 g484 F0.0 tp1841 a(I4570000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p1842 tp1843 Rp1844 g484 F0.0 tp1845 a(I4580000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p1846 tp1847 Rp1848 g484 F0.0 tp1849 a(I4590000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p1850 tp1851 Rp1852 g484 F0.0 tp1853 a(I4600000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p1854 tp1855 Rp1856 g484 F0.0 tp1857 a(I4610000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p1858 tp1859 Rp1860 g484 F0.0 tp1861 a(I4620000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\x00' p1862 tp1863 Rp1864 g484 F0.0 tp1865 a(I4630000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p1866 tp1867 Rp1868 g484 F0.0 tp1869 a(I4640000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p1870 tp1871 Rp1872 g484 F0.0 tp1873 a(I4650000 g1 (g5 S'{\x14\xaeG\xe1z\x84?' p1874 tp1875 Rp1876 g484 F0.0 tp1877 a(I4660000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p1878 tp1879 Rp1880 g484 F5.0 tp1881 a(I4670000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p1882 tp1883 Rp1884 g484 F0.0 tp1885 a(I4680000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p1886 tp1887 Rp1888 g484 F0.0 tp1889 a(I4690000 g1 (g5 S'{\x14\xaeG\xe1z\xb4?' p1890 tp1891 Rp1892 g484 F0.0 tp1893 a(I4700000 g1 (g5 S'\xc3\xf5(\\\x8f\xc2\xe5?' p1894 tp1895 Rp1896 g1 (g5 S'\xc3\xf5(\\\x8f\xc2\xe5?' p1897 tp1898 Rp1899 F42.0 tp1900 a(I4710000 g1 (g5 S'\x85\xebQ\xb8\x1e\x85\xf7?' p1901 tp1902 Rp1903 g1 (g5 S'\x85\xebQ\xb8\x1e\x85\xf7?' p1904 tp1905 Rp1906 F25.0 tp1907 a(I4720000 g1 (g5 S'=\n\xd7\xa3p=\xfe?' p1908 tp1909 Rp1910 g1 (g5 S'=\n\xd7\xa3p=\xfe?' p1911 tp1912 Rp1913 F21.0 tp1914 a(I4730000 g1 (g5 S'\xaeG\xe1z\x14\xae\x05@' p1915 tp1916 Rp1917 g1 (g5 S'\xaeG\xe1z\x14\xae\x05@' p1918 tp1919 Rp1920 F29.0 tp1921 a(I4740000 g1 (g5 S'\xd7\xa3p=\n\xd7\r@' p1922 tp1923 Rp1924 g1 (g5 S'\xd7\xa3p=\n\xd7\r@' p1925 tp1926 Rp1927 F30.0 tp1928 a(I4750000 g1 (g5 S'\xc3\xf5(\\\x8f\xc2\x11@' p1929 tp1930 Rp1931 g1 (g5 S'\xc3\xf5(\\\x8f\xc2\x11@' p1932 tp1933 Rp1934 F26.0 tp1935 a(I4760000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\x15@' p1936 tp1937 Rp1938 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\x15@' p1939 tp1940 Rp1941 F31.0 tp1942 a(I4770000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\x18@' p1943 tp1944 Rp1945 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\x18@' p1946 tp1947 Rp1948 F12.0 tp1949 a(I4780000 g1 (g5 S'\xe1z\x14\xaeG\xe1\x1c@' p1950 tp1951 Rp1952 g1 (g5 S'\xe1z\x14\xaeG\xe1\x1c@' p1953 tp1954 Rp1955 F38.0 tp1956 a(I4790000 g1 (g5 S')\\\x8f\xc2\xf5( @' p1957 tp1958 Rp1959 g1 (g5 S')\\\x8f\xc2\xf5( @' p1960 tp1961 Rp1962 F39.0 tp1963 a(I4800000 g1 (g5 S'q=\n\xd7\xa3\xf0 @' p1964 tp1965 Rp1966 g1 (g5 S'q=\n\xd7\xa3\xf0 @' p1967 tp1968 Rp1969 F18.0 tp1970 a(I4810000 g1 (g5 S'\x00\x00\x00\x00\x00\x00#@' p1971 tp1972 Rp1973 g1 (g5 S'\x00\x00\x00\x00\x00\x00#@' p1974 tp1975 Rp1976 F41.0 tp1977 a(I4820000 g1 (g5 S'\xb8\x1e\x85\xebQ8$@' p1978 tp1979 Rp1980 g1 (g5 S'\xb8\x1e\x85\xebQ8$@' p1981 tp1982 Rp1983 F5.0 tp1984 a(I4830000 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e%@' p1985 tp1986 Rp1987 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e%@' p1988 tp1989 Rp1990 F32.0 tp1991 a(I4840000 g1 (g5 S"\xd7\xa3p=\n\xd7'@" p1992 tp1993 Rp1994 g1 (g5 S"\xd7\xa3p=\n\xd7'@" p1995 tp1996 Rp1997 F26.0 tp1998 a(I4850000 g1 (g5 S'\x85\xebQ\xb8\x1e\x05+@' p1999 tp2000 Rp2001 g1 (g5 S'\x85\xebQ\xb8\x1e\x05+@' p2002 tp2003 Rp2004 F41.0 tp2005 a(I4860000 g1 (g5 S'\xc3\xf5(\\\x8fB-@' p2006 tp2007 Rp2008 g1 (g5 S'\xc3\xf5(\\\x8fB-@' p2009 tp2010 Rp2011 F30.0 tp2012 a(I4870000 g1 (g5 S'\xb8\x1e\x85\xebQ8.@' p2013 tp2014 Rp2015 g1 (g5 S'\xb8\x1e\x85\xebQ8.@' p2016 tp2017 Rp2018 F26.0 tp2019 a(I4880000 g1 (g5 S'\x00\x00\x00\x00\x00\x80/@' p2020 tp2021 Rp2022 g1 (g5 S'\x00\x00\x00\x00\x00\x80/@' p2023 tp2024 Rp2025 F19.0 tp2026 a(I4890000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f0@' p2027 tp2028 Rp2029 g1 (g5 S'\x8f\xc2\xf5(\\\x8f0@' p2030 tp2031 Rp2032 F21.0 tp2033 a(I4900000 g1 (g5 S'\xd7\xa3p=\nW1@' p2034 tp2035 Rp2036 g1 (g5 S'\xd7\xa3p=\nW1@' p2037 tp2038 Rp2039 F45.0 tp2040 a(I4910000 g1 (g5 S'\x1f\x85\xebQ\xb8\xde1@' p2041 tp2042 Rp2043 g1 (g5 S'\x1f\x85\xebQ\xb8\xde1@' p2044 tp2045 Rp2046 F10.0 tp2047 a(I4920000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc52@' p2048 tp2049 Rp2050 g1 (g5 S'\x85\xebQ\xb8\x1e\xc52@' p2051 tp2052 Rp2053 F18.0 tp2054 a(I4930000 g1 (g5 S'\\\x8f\xc2\xf5(\\3@' p2055 tp2056 Rp2057 g1 (g5 S'\\\x8f\xc2\xf5(\\3@' p2058 tp2059 Rp2060 F36.0 tp2061 a(I4940000 g1 (g5 S'\x00\x00\x00\x00\x00\x004@' p2062 tp2063 Rp2064 g1 (g5 S'\x00\x00\x00\x00\x00\x004@' p2065 tp2066 Rp2067 F25.0 tp2068 a(I4950000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc4@' p2069 tp2070 Rp2071 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc4@' p2072 tp2073 Rp2074 F7.0 tp2075 a(I4960000 g1 (g5 S'\x8f\xc2\xf5(\\O5@' p2076 tp2077 Rp2078 g1 (g5 S'R\xb8\x1e\x85\xebQ5@' p2079 tp2080 Rp2081 F0.0 tp2082 a(I4970000 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e6@' p2083 tp2084 Rp2085 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e6@' p2086 tp2087 Rp2088 F26.0 tp2089 a(I4980000 g1 (g5 S'\x00\x00\x00\x00\x00\xc06@' p2090 tp2091 Rp2092 g1 (g5 S'\x00\x00\x00\x00\x00\xc06@' p2093 tp2094 Rp2095 F34.0 tp2096 a(I4990000 g1 (g5 S'\x1f\x85\xebQ\xb8^7@' p2097 tp2098 Rp2099 g1 (g5 S'\x1f\x85\xebQ\xb8^7@' p2100 tp2101 Rp2102 F32.0 tp2103 a(I5000000 g1 (g5 S'\n\xd7\xa3p=\xca7@' p2104 tp2105 Rp2106 g1 (g5 S'\n\xd7\xa3p=\xca7@' p2107 tp2108 Rp2109 F6.0 tp2110 a(I5010000 g1 (g5 S'\xd7\xa3p=\n\x978@' p2111 tp2112 Rp2113 g1 (g5 S'\xd7\xa3p=\n\x978@' p2114 tp2115 Rp2116 F29.0 tp2117 a(I5020000 g1 (g5 S'\xa4p=\n\xd7c9@' p2118 tp2119 Rp2120 g1 (g5 S'\xa4p=\n\xd7c9@' p2121 tp2122 Rp2123 F27.0 tp2124 a(I5030000 g1 (g5 S'{\x14\xaeG\xe1\xba9@' p2125 tp2126 Rp2127 g1 (g5 S'{\x14\xaeG\xe1\xba9@' p2128 tp2129 Rp2130 F25.0 tp2131 a(I5040000 g1 (g5 S'\n\xd7\xa3p=\xca9@' p2132 tp2133 Rp2134 g1 (g5 S'\n\xd7\xa3p=\xca9@' p2135 tp2136 Rp2137 F31.0 tp2138 a(I5050000 g1 (g5 S'\x9a\x99\x99\x99\x99\x19:@' p2139 tp2140 Rp2141 g1 (g5 S'\x9a\x99\x99\x99\x99\x19:@' p2142 tp2143 Rp2144 F43.0 tp2145 a(I5060000 g1 (g5 S'\x14\xaeG\xe1z\x14:@' p2146 tp2147 Rp2148 g2144 F18.0 tp2149 a(I5070000 g1 (g5 S'\xa4p=\n\xd7\xe39@' p2150 tp2151 Rp2152 g1 (g5 S'333333:@' p2153 tp2154 Rp2155 F6.0 tp2156 a(I5080000 g1 (g5 S'\\\x8f\xc2\xf5(\x1c:@' p2157 tp2158 Rp2159 g2155 F35.0 tp2160 a(I5090000 g1 (g5 S'\x8f\xc2\xf5(\\\x0f:@' p2161 tp2162 Rp2163 g2155 F22.0 tp2164 a(I5100000 g1 (g5 S'\x00\x00\x00\x00\x00\xc09@' p2165 tp2166 Rp2167 g2155 F10.0 tp2168 a(I5110000 g1 (g5 S'\\\x8f\xc2\xf5(\x9c9@' p2169 tp2170 Rp2171 g2155 F31.0 tp2172 a(I5120000 g1 (g5 S'\xa4p=\n\xd7#9@' p2173 tp2174 Rp2175 g2155 F23.0 tp2176 a(I5130000 g1 (g5 S'\xc3\xf5(\\\x8f\x829@' p2177 tp2178 Rp2179 g2155 F25.0 tp2180 a(I5140000 g1 (g5 S'\x8f\xc2\xf5(\\\xcf8@' p2181 tp2182 Rp2183 g2155 F1.0 tp2184 a(I5150000 g1 (g5 S'\x8f\xc2\xf5(\\\xcf8@' p2185 tp2186 Rp2187 g2155 F13.0 tp2188 a(I5160000 g1 (g5 S'33333\xb38@' p2189 tp2190 Rp2191 g2155 F18.0 tp2192 a(I5170000 g1 (g5 S'\x1f\x85\xebQ\xb8^8@' p2193 tp2194 Rp2195 g2155 F37.0 tp2196 a(I5180000 g1 (g5 S'\x9a\x99\x99\x99\x99\xd97@' p2197 tp2198 Rp2199 g2155 F36.0 tp2200 a(I5190000 g1 (g5 S'\xc3\xf5(\\\x8fB7@' p2201 tp2202 Rp2203 g2155 F13.0 tp2204 a(I5200000 g1 (g5 S'H\xe1z\x14\xae\xc77@' p2205 tp2206 Rp2207 g2155 F34.0 tp2208 a(I5210000 g1 (g5 S'H\xe1z\x14\xae\x078@' p2209 tp2210 Rp2211 g2155 F33.0 tp2212 a(I5220000 g1 (g5 S'\x9a\x99\x99\x99\x99\x198@' p2213 tp2214 Rp2215 g2155 F27.0 tp2216 a(I5230000 g1 (g5 S'H\xe1z\x14\xae\xc77@' p2217 tp2218 Rp2219 g2155 F22.0 tp2220 a(I5240000 g1 (g5 S'\xaeG\xe1z\x14\xee7@' p2221 tp2222 Rp2223 g2155 F19.0 tp2224 a(I5250000 g1 (g5 S'\\\x8f\xc2\xf5(\xdc7@' p2225 tp2226 Rp2227 g2155 F33.0 tp2228 a(I5260000 g1 (g5 S'=\n\xd7\xa3p=8@' p2229 tp2230 Rp2231 g2155 F11.0 tp2232 a(I5270000 g1 (g5 S'q=\n\xd7\xa3\xf07@' p2233 tp2234 Rp2235 g2155 F34.0 tp2236 a(I5280000 g1 (g5 S'\xe1z\x14\xaeG\xe17@' p2237 tp2238 Rp2239 g2155 F13.0 tp2240 a(I5290000 g1 (g5 S'R\xb8\x1e\x85\xebQ8@' p2241 tp2242 Rp2243 g2155 F36.0 tp2244 a(I5300000 g1 (g5 S'\x1f\x85\xebQ\xb8^8@' p2245 tp2246 Rp2247 g2155 F29.0 tp2248 a(I5310000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x8c8@' p2249 tp2250 Rp2251 g2155 F22.0 tp2252 a(I5320000 g1 (g5 S'\n\xd7\xa3p=\n8@' p2253 tp2254 Rp2255 g2155 F4.0 tp2256 a(I5330000 g1 (g5 S'\xf6(\\\x8f\xc258@' p2257 tp2258 Rp2259 g2155 F20.0 tp2260 a(I5340000 g1 (g5 S'\xf6(\\\x8f\xc2\xf57@' p2261 tp2262 Rp2263 g2155 F26.0 tp2264 a(I5350000 g1 (g5 S'\x1f\x85\xebQ\xb8\xde7@' p2265 tp2266 Rp2267 g2155 F2.0 tp2268 a(I5360000 g1 (g5 S'3333338@' p2269 tp2270 Rp2271 g2155 F25.0 tp2272 a(I5370000 g1 (g5 S'\xe1z\x14\xaeG!8@' p2273 tp2274 Rp2275 g2155 F36.0 tp2276 a(I5380000 g1 (g5 S'3333338@' p2277 tp2278 Rp2279 g2155 F11.0 tp2280 a(I5390000 g1 (g5 S'=\n\xd7\xa3p=8@' p2281 tp2282 Rp2283 g2155 F34.0 tp2284 a(I5400000 g1 (g5 S'\xecQ\xb8\x1e\x85+8@' p2285 tp2286 Rp2287 g2155 F41.0 tp2288 a(I5410000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc7@' p2289 tp2290 Rp2291 g2155 F32.0 tp2292 a(I5420000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f7@' p2293 tp2294 Rp2295 g2155 F34.0 tp2296 a(I5430000 g1 (g5 S'\x9a\x99\x99\x99\x99\x197@' p2297 tp2298 Rp2299 g2155 F3.0 tp2300 a(I5440000 g1 (g5 S'=\n\xd7\xa3p\xfd6@' p2301 tp2302 Rp2303 g2155 F28.0 tp2304 a(I5450000 g1 (g5 S'\x1f\x85\xebQ\xb8\xde6@' p2305 tp2306 Rp2307 g2155 F42.0 tp2308 a(I5460000 g1 (g5 S'\x85\xebQ\xb8\x1e\x057@' p2309 tp2310 Rp2311 g2155 F32.0 tp2312 a(I5470000 g1 (g5 S'\x1f\x85\xebQ\xb8\xde6@' p2313 tp2314 Rp2315 g2155 F7.0 tp2316 a(I5480000 g1 (g5 S'\x9a\x99\x99\x99\x99\x197@' p2317 tp2318 Rp2319 g2155 F38.0 tp2320 a(I5490000 g1 (g5 S'33333s7@' p2321 tp2322 Rp2323 g2155 F28.0 tp2324 a(I5500000 g1 (g5 S'ffffff7@' p2325 tp2326 Rp2327 g2155 F33.0 tp2328 a(I5510000 g1 (g5 S'fffff&7@' p2329 tp2330 Rp2331 g2155 F32.0 tp2332 a(I5520000 g1 (g5 S'=\n\xd7\xa3p=7@' p2333 tp2334 Rp2335 g2155 F44.0 tp2336 a(I5530000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f6@' p2337 tp2338 Rp2339 g2155 F6.0 tp2340 a(I5540000 g1 (g5 S'\xe1z\x14\xaeGa6@' p2341 tp2342 Rp2343 g2155 F21.0 tp2344 a(I5550000 g1 (g5 S'\n\xd7\xa3p=\n6@' p2345 tp2346 Rp2347 g2155 F25.0 tp2348 a(I5560000 g1 (g5 S'33333s6@' p2349 tp2350 Rp2351 g2155 F38.0 tp2352 a(I5570000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc56@' p2353 tp2354 Rp2355 g2155 F37.0 tp2356 a(I5580000 g1 (g5 S'{\x14\xaeG\xe1z6@' p2357 tp2358 Rp2359 g2155 F28.0 tp2360 a(I5590000 g1 (g5 S'\xa4p=\n\xd7\xe35@' p2361 tp2362 Rp2363 g2155 F33.0 tp2364 a(I5600000 g1 (g5 S'\xc3\xf5(\\\x8f\x826@' p2365 tp2366 Rp2367 g2155 F31.0 tp2368 a(I5610000 g1 (g5 S'\xaeG\xe1z\x14\xee5@' p2369 tp2370 Rp2371 g2155 F3.0 tp2372 a(I5620000 g1 (g5 S'\n\xd7\xa3p=\n6@' p2373 tp2374 Rp2375 g2155 F23.0 tp2376 a(I5630000 g1 (g5 S'\\\x8f\xc2\xf5(\x1c5@' p2377 tp2378 Rp2379 g2155 F0.0 tp2380 a(I5640000 g1 (g5 S'\xd7\xa3p=\n\x974@' p2381 tp2382 Rp2383 g2155 F11.0 tp2384 a(I5650000 g1 (g5 S'=\n\xd7\xa3p\xfd4@' p2385 tp2386 Rp2387 g2155 F33.0 tp2388 a(I5660000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x8c4@' p2389 tp2390 Rp2391 g2155 F0.0 tp2392 a(I5670000 g1 (g5 S'\x8f\xc2\xf5(\\\x0f4@' p2393 tp2394 Rp2395 g2155 F0.0 tp2396 a(I5680000 g1 (g5 S'\\\x8f\xc2\xf5(\x1c3@' p2397 tp2398 Rp2399 g2155 F0.0 tp2400 a(I5690000 g1 (g5 S'\x1f\x85\xebQ\xb8^3@' p2401 tp2402 Rp2403 g2155 F35.0 tp2404 a(I5700000 g1 (g5 S'\xa4p=\n\xd7#3@' p2405 tp2406 Rp2407 g2155 F21.0 tp2408 a(I5710000 g1 (g5 S'=\n\xd7\xa3p}2@' p2409 tp2410 Rp2411 g2155 F9.0 tp2412 a(I5720000 g1 (g5 S'\x8f\xc2\xf5(\\O2@' p2413 tp2414 Rp2415 g2155 F23.0 tp2416 a(I5730000 g1 (g5 S'\x00\x00\x00\x00\x00\xc02@' p2417 tp2418 Rp2419 g2155 F35.0 tp2420 a(I5740000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb82@' p2421 tp2422 Rp2423 g2155 F31.0 tp2424 a(I5750000 g1 (g5 S'\xc3\xf5(\\\x8f\x822@' p2425 tp2426 Rp2427 g2155 F0.0 tp2428 a(I5760000 g1 (g5 S'R\xb8\x1e\x85\xeb\x112@' p2429 tp2430 Rp2431 g2155 F4.0 tp2432 a(I5770000 g1 (g5 S'\xa4p=\n\xd7c2@' p2433 tp2434 Rp2435 g2155 F14.0 tp2436 a(I5780000 g1 (g5 S'\xb8\x1e\x85\xebQ\xf82@' p2437 tp2438 Rp2439 g2155 F16.0 tp2440 a(I5790000 g1 (g5 S'\x85\xebQ\xb8\x1eE3@' p2441 tp2442 Rp2443 g2155 F39.0 tp2444 a(I5800000 g1 (g5 S'\\\x8f\xc2\xf5(\x9c3@' p2445 tp2446 Rp2447 g2155 F24.0 tp2448 a(I5810000 g1 (g5 S'\x14\xaeG\xe1zT4@' p2449 tp2450 Rp2451 g2155 F35.0 tp2452 a(I5820000 g1 (g5 S'=\n\xd7\xa3p=4@' p2453 tp2454 Rp2455 g2155 F36.0 tp2456 a(I5830000 g1 (g5 S'\xa4p=\n\xd7c4@' p2457 tp2458 Rp2459 g2155 F32.0 tp2460 a(I5840000 g1 (g5 S'\x9a\x99\x99\x99\x99Y4@' p2461 tp2462 Rp2463 g2155 F42.0 tp2464 a(I5850000 g1 (g5 S'\xe1z\x14\xaeG\xa14@' p2465 tp2466 Rp2467 g2155 F45.0 tp2468 a(I5860000 g1 (g5 S'R\xb8\x1e\x85\xeb\xd14@' p2469 tp2470 Rp2471 g2155 F19.0 tp2472 a(I5870000 g1 (g5 S'\x00\x00\x00\x00\x00\x005@' p2473 tp2474 Rp2475 g2155 F36.0 tp2476 a(I5880000 g1 (g5 S'\x9a\x99\x99\x99\x99\x195@' p2477 tp2478 Rp2479 g2155 F33.0 tp2480 a(I5890000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab4@' p2481 tp2482 Rp2483 g2155 F0.0 tp2484 a(I5900000 g1 (g5 S'\x9a\x99\x99\x99\x99\x994@' p2485 tp2486 Rp2487 g2155 F36.0 tp2488 a(I5910000 g1 (g5 S'\x00\x00\x00\x00\x00\x005@' p2489 tp2490 Rp2491 g2155 F30.0 tp2492 a(I5920000 g1 (g5 S'\xf6(\\\x8f\xc255@' p2493 tp2494 Rp2495 g2155 F41.0 tp2496 a(I5930000 g1 (g5 S'R\xb8\x1e\x85\xebQ5@' p2497 tp2498 Rp2499 g2155 F46.0 tp2500 a(I5940000 g1 (g5 S')\\\x8f\xc2\xf5h5@' p2501 tp2502 Rp2503 g2155 F24.0 tp2504 a(I5950000 g1 (g5 S'\x14\xaeG\xe1zT5@' p2505 tp2506 Rp2507 g2155 F26.0 tp2508 a(I5960000 g1 (g5 S'R\xb8\x1e\x85\xebQ6@' p2509 tp2510 Rp2511 g2155 F30.0 tp2512 a(I5970000 g1 (g5 S'\x9a\x99\x99\x99\x99\x996@' p2513 tp2514 Rp2515 g2155 F30.0 tp2516 a(I5980000 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e7@' p2517 tp2518 Rp2519 g2155 F40.0 tp2520 a(I5990000 g1 (g5 S'\x00\x00\x00\x00\x00@7@' p2521 tp2522 Rp2523 g2155 F8.0 tp2524 a(I6000000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x0c8@' p2525 tp2526 Rp2527 g2155 F26.0 tp2528 a(I6010000 g1 (g5 S'=\n\xd7\xa3p\xfd8@' p2529 tp2530 Rp2531 g2155 F22.0 tp2532 a(I6020000 g1 (g5 S'q=\n\xd7\xa309@' p2533 tp2534 Rp2535 g2155 F27.0 tp2536 a(I6030000 g1 (g5 S'\x85\xebQ\xb8\x1eE9@' p2537 tp2538 Rp2539 g2155 F32.0 tp2540 a(I6040000 g1 (g5 S'H\xe1z\x14\xaeG9@' p2541 tp2542 Rp2543 g2155 F19.0 tp2544 a(I6050000 g1 (g5 S'\\\x8f\xc2\xf5(\x1c:@' p2545 tp2546 Rp2547 g2155 F28.0 tp2548 a(I6060000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc59@' p2549 tp2550 Rp2551 g2155 F17.0 tp2552 a(I6070000 g1 (g5 S'\xc3\xf5(\\\x8f\xc29@' p2553 tp2554 Rp2555 g2155 F24.0 tp2556 a(I6080000 g1 (g5 S'\xf6(\\\x8f\xc2\xb59@' p2557 tp2558 Rp2559 g2155 F8.0 tp2560 a(I6090000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x8c:@' p2561 tp2562 Rp2563 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x8c:@' p2564 tp2565 Rp2566 F24.0 tp2567 a(I6100000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99:@' p2568 tp2569 Rp2570 g1 (g5 S'\x9a\x99\x99\x99\x99\x99:@' p2571 tp2572 Rp2573 F21.0 tp2574 a(I6110000 g1 (g5 S'\xd7\xa3p=\n\x97:@' p2575 tp2576 Rp2577 g1 (g5 S'\\\x8f\xc2\xf5(\x9c:@' p2578 tp2579 Rp2580 F43.0 tp2581 a(I6120000 g1 (g5 S'\xa4p=\n\xd7#:@' p2582 tp2583 Rp2584 g1 (g5 S')\\\x8f\xc2\xf5\xa8:@' p2585 tp2586 Rp2587 F13.0 tp2588 a(I6130000 g1 (g5 S'\\\x8f\xc2\xf5(\x9c9@' p2589 tp2590 Rp2591 g2587 F16.0 tp2592 a(I6140000 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e9@' p2593 tp2594 Rp2595 g2587 F29.0 tp2596 a(I6150000 g1 (g5 S'3333339@' p2597 tp2598 Rp2599 g2587 F25.0 tp2600 a(I6160000 g1 (g5 S'\xf6(\\\x8f\xc259@' p2601 tp2602 Rp2603 g2587 F22.0 tp2604 a(I6170000 g1 (g5 S'\xaeG\xe1z\x14n9@' p2605 tp2606 Rp2607 g2587 F23.0 tp2608 a(I6180000 g1 (g5 S'\x1f\x85\xebQ\xb8^9@' p2609 tp2610 Rp2611 g2587 F30.0 tp2612 a(I6190000 g1 (g5 S'H\xe1z\x14\xaeG9@' p2613 tp2614 Rp2615 g2587 F17.0 tp2616 a(I6200000 g1 (g5 S'{\x14\xaeG\xe1\xba9@' p2617 tp2618 Rp2619 g2587 F33.0 tp2620 a(I6210000 g1 (g5 S'\x14\xaeG\xe1z\xd49@' p2621 tp2622 Rp2623 g2587 F17.0 tp2624 a(I6220000 g1 (g5 S'H\xe1z\x14\xae\x07:@' p2625 tp2626 Rp2627 g2587 F6.0 tp2628 a(I6230000 g1 (g5 S'\xaeG\xe1z\x14n:@' p2629 tp2630 Rp2631 g2587 F29.0 tp2632 a(I6240000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x0c:@' p2633 tp2634 Rp2635 g2587 F19.0 tp2636 a(I6250000 g1 (g5 S'fffff&:@' p2637 tp2638 Rp2639 g2587 F33.0 tp2640 a(I6260000 g1 (g5 S'\x9a\x99\x99\x99\x99\xd99@' p2641 tp2642 Rp2643 g2587 F12.0 tp2644 a(I6270000 g1 (g5 S'\xa4p=\n\xd7\xe39@' p2645 tp2646 Rp2647 g2587 F9.0 tp2648 a(I6280000 g1 (g5 S'\x8f\xc2\xf5(\\\xcf9@' p2649 tp2650 Rp2651 g2587 F25.0 tp2652 a(I6290000 g1 (g5 S'{\x14\xaeG\xe1z9@' p2653 tp2654 Rp2655 g2587 F64.0 tp2656 a(I6300000 g1 (g5 S'R\xb8\x1e\x85\xeb\x11:@' p2657 tp2658 Rp2659 g2587 F41.0 tp2660 a(I6310000 g1 (g5 S')\\\x8f\xc2\xf5\xa89@' p2661 tp2662 Rp2663 g2587 F29.0 tp2664 a(I6320000 g1 (g5 S'\xcd\xcc\xcc\xcc\xccL:@' p2665 tp2666 Rp2667 g2587 F71.0 tp2668 a(I6330000 g1 (g5 S'q=\n\xd7\xa3\xb0:@' p2669 tp2670 Rp2671 g1 (g5 S'q=\n\xd7\xa3\xb0:@' p2672 tp2673 Rp2674 F42.0 tp2675 a(I6340000 g1 (g5 S'{\x14\xaeG\xe1z:@' p2676 tp2677 Rp2678 g2674 F17.0 tp2679 a(I6350000 g1 (g5 S'\xa4p=\n\xd7#:@' p2680 tp2681 Rp2682 g2674 F25.0 tp2683 a(I6360000 g1 (g5 S'R\xb8\x1e\x85\xeb\x11:@' p2684 tp2685 Rp2686 g2674 F27.0 tp2687 a(I6370000 g1 (g5 S'R\xb8\x1e\x85\xeb\x91:@' p2688 tp2689 Rp2690 g2674 F48.0 tp2691 a(I6380000 g1 (g5 S'H\xe1z\x14\xaeG:@' p2692 tp2693 Rp2694 g2674 F31.0 tp2695 a(I6390000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab:@' p2696 tp2697 Rp2698 g2674 F53.0 tp2699 a(I6400000 g1 (g5 S'\xc3\xf5(\\\x8f\xc2:@' p2700 tp2701 Rp2702 g1 (g5 S'\x14\xaeG\xe1z\xd4:@' p2703 tp2704 Rp2705 F28.0 tp2706 a(I6410000 g1 (g5 S'\x85\xebQ\xb8\x1eE;@' p2707 tp2708 Rp2709 g1 (g5 S'\x85\xebQ\xb8\x1eE;@' p2710 tp2711 Rp2712 F51.0 tp2713 a(I6420000 g1 (g5 S'\n\xd7\xa3p=\xca;@' p2714 tp2715 Rp2716 g1 (g5 S'\n\xd7\xa3p=\xca;@' p2717 tp2718 Rp2719 F26.0 tp2720 a(I6430000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8;@' p2721 tp2722 Rp2723 g1 (g5 S'\xaeG\xe1z\x14\xee;@' p2724 tp2725 Rp2726 F22.0 tp2727 a(I6440000 g1 (g5 S'\xf6(\\\x8f\xc2u;@' p2728 tp2729 Rp2730 g2726 F19.0 tp2731 a(I6450000 g1 (g5 S'\xe1z\x14\xaeG\xa1;@' p2732 tp2733 Rp2734 g2726 F24.0 tp2735 a(I6460000 g1 (g5 S'\xaeG\xe1z\x14\xae;@' p2736 tp2737 Rp2738 g2726 F16.0 tp2739 a(I6470000 g1 (g5 S'=\n\xd7\xa3p};@' p2740 tp2741 Rp2742 g2726 F33.0 tp2743 a(I6480000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc5;@' p2744 tp2745 Rp2746 g2726 F32.0 tp2747 a(I6490000 g1 (g5 S'=\n\xd7\xa3p\xfd;@' p2748 tp2749 Rp2750 g1 (g5 S'\x85\xebQ\xb8\x1e\x05<@' p2751 tp2752 Rp2753 F40.0 tp2754 a(I6500000 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e;@' p2755 tp2756 Rp2757 g1 (g5 S'\x8f\xc2\xf5(\\\x0f<@' p2758 tp2759 Rp2760 F21.0 tp2761 a(I6510000 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e;@' p2762 tp2763 Rp2764 g2760 F36.0 tp2765 a(I6520000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f;@' p2766 tp2767 Rp2768 g2760 F37.0 tp2769 a(I6530000 g1 (g5 S'\\\x8f\xc2\xf5(\x9c:@' p2770 tp2771 Rp2772 g2760 F5.0 tp2773 a(I6540000 g1 (g5 S'\xd7\xa3p=\nW:@' p2774 tp2775 Rp2776 g2760 F45.0 tp2777 a(I6550000 g1 (g5 S'\xb8\x1e\x85\xebQ8:@' p2778 tp2779 Rp2780 g2760 F31.0 tp2781 a(I6560000 g1 (g5 S'\\\x8f\xc2\xf5(\xdc9@' p2782 tp2783 Rp2784 g2760 F23.0 tp2785 a(I6570000 g1 (g5 S'\xd7\xa3p=\n\x17:@' p2786 tp2787 Rp2788 g2760 F43.0 tp2789 a(I6580000 g1 (g5 S'\xa4p=\n\xd7#:@' p2790 tp2791 Rp2792 g2760 F30.0 tp2793 a(I6590000 g1 (g5 S'\xa4p=\n\xd7#:@' p2794 tp2795 Rp2796 g2760 F24.0 tp2797 a(I6600000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb9@' p2798 tp2799 Rp2800 g2760 F31.0 tp2801 a(I6610000 g1 (g5 S'\x14\xaeG\xe1zT:@' p2802 tp2803 Rp2804 g2760 F22.0 tp2805 a(I6620000 g1 (g5 S'R\xb8\x1e\x85\xebQ:@' p2806 tp2807 Rp2808 g2760 F2.0 tp2809 a(I6630000 g1 (g5 S')\\\x8f\xc2\xf5\xa89@' p2810 tp2811 Rp2812 g2760 F3.0 tp2813 a(I6640000 g1 (g5 S'\xc3\xf5(\\\x8f\x829@' p2814 tp2815 Rp2816 g2760 F48.0 tp2817 a(I6650000 g1 (g5 S'\x00\x00\x00\x00\x00\xc09@' p2818 tp2819 Rp2820 g2760 F6.0 tp2821 a(I6660000 g1 (g5 S'q=\n\xd7\xa3\xf08@' p2822 tp2823 Rp2824 g2760 F27.0 tp2825 a(I6670000 g1 (g5 S'33333\xb38@' p2826 tp2827 Rp2828 g2760 F27.0 tp2829 a(I6680000 g1 (g5 S'\xb8\x1e\x85\xebQx8@' p2830 tp2831 Rp2832 g2760 F0.0 tp2833 a(I6690000 g1 (g5 S'\x9a\x99\x99\x99\x99\xd97@' p2834 tp2835 Rp2836 g2760 F11.0 tp2837 a(I6700000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x8c7@' p2838 tp2839 Rp2840 g2760 F25.0 tp2841 a(I6710000 g1 (g5 S'\x00\x00\x00\x00\x00@7@' p2842 tp2843 Rp2844 g2760 F27.0 tp2845 a(I6720000 g1 (g5 S'\xb8\x1e\x85\xebQx7@' p2846 tp2847 Rp2848 g2760 F27.0 tp2849 a(I6730000 g1 (g5 S'{\x14\xaeG\xe1\xfa6@' p2850 tp2851 Rp2852 g2760 F10.0 tp2853 a(I6740000 g1 (g5 S'\x8f\xc2\xf5(\\O6@' p2854 tp2855 Rp2856 g2760 F15.0 tp2857 a(I6750000 g1 (g5 S'\\\x8f\xc2\xf5(\xdc5@' p2858 tp2859 Rp2860 g2760 F54.0 tp2861 a(I6760000 g1 (g5 S'=\n\xd7\xa3p\xbd5@' p2862 tp2863 Rp2864 g2760 F0.0 tp2865 a(I6770000 g1 (g5 S'\x00\x00\x00\x00\x00@5@' p2866 tp2867 Rp2868 g2760 F11.0 tp2869 a(I6780000 g1 (g5 S'fffff&5@' p2870 tp2871 Rp2872 g2760 F33.0 tp2873 a(I6790000 g1 (g5 S'\\\x8f\xc2\xf5(\xdc4@' p2874 tp2875 Rp2876 g2760 F0.0 tp2877 a(I6800000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc54@' p2878 tp2879 Rp2880 g2760 F18.0 tp2881 a(I6810000 g1 (g5 S'\x14\xaeG\xe1z\x145@' p2882 tp2883 Rp2884 g2760 F13.0 tp2885 a(I6820000 g1 (g5 S'\xd7\xa3p=\nW5@' p2886 tp2887 Rp2888 g2760 F28.0 tp2889 a(I6830000 g1 (g5 S'\xc3\xf5(\\\x8f\x025@' p2890 tp2891 Rp2892 g2760 F2.0 tp2893 a(I6840000 g1 (g5 S'\\\x8f\xc2\xf5(\x1c5@' p2894 tp2895 Rp2896 g2760 F29.0 tp2897 a(I6850000 g1 (g5 S'\x8f\xc2\xf5(\\O5@' p2898 tp2899 Rp2900 g2760 F33.0 tp2901 a(I6860000 g1 (g5 S'q=\n\xd7\xa3\xb05@' p2902 tp2903 Rp2904 g2760 F39.0 tp2905 a(I6870000 g1 (g5 S'\xecQ\xb8\x1e\x85+6@' p2906 tp2907 Rp2908 g2760 F28.0 tp2909 a(I6880000 g1 (g5 S'\n\xd7\xa3p=J6@' p2910 tp2911 Rp2912 g2760 F41.0 tp2913 a(I6890000 g1 (g5 S'\\\x8f\xc2\xf5(\x1c6@' p2914 tp2915 Rp2916 g2760 F34.0 tp2917 a(I6900000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x8c6@' p2918 tp2919 Rp2920 g2760 F44.0 tp2921 a(I6910000 g1 (g5 S'\xf6(\\\x8f\xc2\xb56@' p2922 tp2923 Rp2924 g2760 F36.0 tp2925 a(I6920000 g1 (g5 S'=\n\xd7\xa3p\xbd6@' p2926 tp2927 Rp2928 g2760 F30.0 tp2929 a(I6930000 g1 (g5 S'\xf6(\\\x8f\xc257@' p2930 tp2931 Rp2932 g2760 F27.0 tp2933 a(I6940000 g1 (g5 S'q=\n\xd7\xa3p7@' p2934 tp2935 Rp2936 g2760 F34.0 tp2937 a(I6950000 g1 (g5 S'\x9a\x99\x99\x99\x99Y7@' p2938 tp2939 Rp2940 g2760 F31.0 tp2941 a(I6960000 g1 (g5 S'\x8f\xc2\xf5(\\\xcf7@' p2942 tp2943 Rp2944 g2760 F49.0 tp2945 a(I6970000 g1 (g5 S'\xaeG\xe1z\x14n8@' p2946 tp2947 Rp2948 g2760 F29.0 tp2949 a(I6980000 g1 (g5 S'H\xe1z\x14\xae\xc77@' p2950 tp2951 Rp2952 g2760 F28.0 tp2953 a(I6990000 g1 (g5 S'\xaeG\xe1z\x14\xee7@' p2954 tp2955 Rp2956 g2760 F13.0 tp2957 a(I7000000 g1 (g5 S'\xaeG\xe1z\x14.8@' p2958 tp2959 Rp2960 g2760 F23.0 tp2961 a(I7010000 g1 (g5 S'\\\x8f\xc2\xf5(\\8@' p2962 tp2963 Rp2964 g2760 F17.0 tp2965 a(I7020000 g1 (g5 S'R\xb8\x1e\x85\xeb\x119@' p2966 tp2967 Rp2968 g2760 F34.0 tp2969 a(I7030000 g1 (g5 S'\x14\xaeG\xe1z\x949@' p2970 tp2971 Rp2972 g2760 F19.0 tp2973 a(I7040000 g1 (g5 S'\xa4p=\n\xd7\xa39@' p2974 tp2975 Rp2976 g2760 F6.0 tp2977 a(I7050000 g1 (g5 S'\xf6(\\\x8f\xc259@' p2978 tp2979 Rp2980 g2760 F9.0 tp2981 a(I7060000 g1 (g5 S'\xd7\xa3p=\nW9@' p2982 tp2983 Rp2984 g2760 F46.0 tp2985 a(I7070000 g1 (g5 S'\x1f\x85\xebQ\xb8^9@' p2986 tp2987 Rp2988 g2760 F7.0 tp2989 a(I7080000 g1 (g5 S')\\\x8f\xc2\xf5(9@' p2990 tp2991 Rp2992 g2760 F29.0 tp2993 a(I7090000 g1 (g5 S'\n\xd7\xa3p=\xca8@' p2994 tp2995 Rp2996 g2760 F16.0 tp2997 a(I7100000 g1 (g5 S'\xaeG\xe1z\x14.9@' p2998 tp2999 Rp3000 g2760 F1.0 tp3001 a(I7110000 g1 (g5 S'\xd7\xa3p=\n\x979@' p3002 tp3003 Rp3004 g2760 F35.0 tp3005 a(I7120000 g1 (g5 S'R\xb8\x1e\x85\xeb\x919@' p3006 tp3007 Rp3008 g2760 F22.0 tp3009 a(I7130000 g1 (g5 S'\xf6(\\\x8f\xc2\xb59@' p3010 tp3011 Rp3012 g2760 F9.0 tp3013 a(I7140000 g1 (g5 S'\xd7\xa3p=\nW9@' p3014 tp3015 Rp3016 g2760 F12.0 tp3017 a(I7150000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc58@' p3018 tp3019 Rp3020 g2760 F7.0 tp3021 a(I7160000 g1 (g5 S'=\n\xd7\xa3p=9@' p3022 tp3023 Rp3024 g2760 F32.0 tp3025 a(I7170000 g1 (g5 S'\x85\xebQ\xb8\x1eE9@' p3026 tp3027 Rp3028 g2760 F37.0 tp3029 a(I7180000 g1 (g5 S'\xf6(\\\x8f\xc259@' p3030 tp3031 Rp3032 g2760 F36.0 tp3033 a(I7190000 g1 (g5 S'\xcd\xcc\xcc\xcc\xccL9@' p3034 tp3035 Rp3036 g2760 F28.0 tp3037 a(I7200000 g1 (g5 S'R\xb8\x1e\x85\xebQ9@' p3038 tp3039 Rp3040 g2760 F29.0 tp3041 a(I7210000 g1 (g5 S'R\xb8\x1e\x85\xeb\x119@' p3042 tp3043 Rp3044 g2760 F20.0 tp3045 a(I7220000 g1 (g5 S'\xb8\x1e\x85\xebQ89@' p3046 tp3047 Rp3048 g2760 F13.0 tp3049 a(I7230000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb8@' p3050 tp3051 Rp3052 g2760 F26.0 tp3053 a(I7240000 g1 (g5 S'\xc3\xf5(\\\x8fB8@' p3054 tp3055 Rp3056 g2760 F15.0 tp3057 a(I7250000 g1 (g5 S'q=\n\xd7\xa308@' p3058 tp3059 Rp3060 g2760 F6.0 tp3061 a(I7260000 g1 (g5 S'H\xe1z\x14\xae\x877@' p3062 tp3063 Rp3064 g2760 F1.0 tp3065 a(I7270000 g1 (g5 S'\x8f\xc2\xf5(\\\x0f7@' p3066 tp3067 Rp3068 g2760 F15.0 tp3069 a(I7280000 g1 (g5 S'q=\n\xd7\xa3p7@' p3070 tp3071 Rp3072 g2760 F32.0 tp3073 a(I7290000 g1 (g5 S'=\n\xd7\xa3p}6@' p3074 tp3075 Rp3076 g2760 F13.0 tp3077 a(I7300000 g1 (g5 S'\xf6(\\\x8f\xc2u6@' p3078 tp3079 Rp3080 g2760 F27.0 tp3081 a(I7310000 g1 (g5 S'\x85\xebQ\xb8\x1e\x856@' p3082 tp3083 Rp3084 g2760 F31.0 tp3085 a(I7320000 g1 (g5 S'\xf6(\\\x8f\xc256@' p3086 tp3087 Rp3088 g2760 F13.0 tp3089 a(I7330000 g1 (g5 S'\n\xd7\xa3p=\n6@' p3090 tp3091 Rp3092 g2760 F31.0 tp3093 a(I7340000 g1 (g5 S'3333336@' p3094 tp3095 Rp3096 g2760 F11.0 tp3097 a(I7350000 g1 (g5 S'\x8f\xc2\xf5(\\\x0f6@' p3098 tp3099 Rp3100 g2760 F23.0 tp3101 a(I7360000 g1 (g5 S'=\n\xd7\xa3p}5@' p3102 tp3103 Rp3104 g2760 F12.0 tp3105 a(I7370000 g1 (g5 S'\xa4p=\n\xd7c5@' p3106 tp3107 Rp3108 g2760 F25.0 tp3109 a(I7380000 g1 (g5 S'\n\xd7\xa3p=\x8a5@' p3110 tp3111 Rp3112 g2760 F11.0 tp3113 a(I7390000 g1 (g5 S'\xc3\xf5(\\\x8f\x825@' p3114 tp3115 Rp3116 g2760 F2.0 tp3117 a(I7400000 g1 (g5 S'\xe1z\x14\xaeG!6@' p3118 tp3119 Rp3120 g2760 F31.0 tp3121 a(I7410000 g1 (g5 S'{\x14\xaeG\xe1:6@' p3122 tp3123 Rp3124 g2760 F14.0 tp3125 a(I7420000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc6@' p3126 tp3127 Rp3128 g2760 F30.0 tp3129 a(I7430000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb86@' p3130 tp3131 Rp3132 g2760 F26.0 tp3133 a(I7440000 g1 (g5 S')\\\x8f\xc2\xf5(6@' p3134 tp3135 Rp3136 g2760 F0.0 tp3137 a(I7450000 g1 (g5 S'\\\x8f\xc2\xf5(\xdc5@' p3138 tp3139 Rp3140 g2760 F32.0 tp3141 a(I7460000 g1 (g5 S'=\n\xd7\xa3p=6@' p3142 tp3143 Rp3144 g2760 F26.0 tp3145 a(I7470000 g1 (g5 S'\x1f\x85\xebQ\xb8^6@' p3146 tp3147 Rp3148 g2760 F28.0 tp3149 a(I7480000 g1 (g5 S'{\x14\xaeG\xe1\xba6@' p3150 tp3151 Rp3152 g2760 F46.0 tp3153 a(I7490000 g1 (g5 S'\xa4p=\n\xd7\xe36@' p3154 tp3155 Rp3156 g2760 F22.0 tp3157 a(I7500000 g1 (g5 S'\n\xd7\xa3p=\xca6@' p3158 tp3159 Rp3160 g2760 F19.0 tp3161 a(I7510000 g1 (g5 S'q=\n\xd7\xa3\xb06@' p3162 tp3163 Rp3164 g2760 F36.0 tp3165 a(I7520000 g1 (g5 S'\n\xd7\xa3p=\x8a6@' p3166 tp3167 Rp3168 g2760 F20.0 tp3169 a(I7530000 g1 (g5 S'\xe1z\x14\xaeGa6@' p3170 tp3171 Rp3172 g2760 F39.0 tp3173 a(I7540000 g1 (g5 S'\xa4p=\n\xd7\xe36@' p3174 tp3175 Rp3176 g2760 F77.0 tp3177 a(I7550000 g1 (g5 S'=\n\xd7\xa3p\xbd6@' p3178 tp3179 Rp3180 g2760 F8.0 tp3181 a(I7560000 g1 (g5 S'\x8f\xc2\xf5(\\\xcf6@' p3182 tp3183 Rp3184 g2760 F36.0 tp3185 a(I7570000 g1 (g5 S'q=\n\xd7\xa307@' p3186 tp3187 Rp3188 g2760 F34.0 tp3189 a(I7580000 g1 (g5 S'\xe1z\x14\xaeG!7@' p3190 tp3191 Rp3192 g2760 F23.0 tp3193 a(I7590000 g1 (g5 S'H\xe1z\x14\xae\x877@' p3194 tp3195 Rp3196 g2760 F12.0 tp3197 a(I7600000 g1 (g5 S')\\\x8f\xc2\xf5\xe87@' p3198 tp3199 Rp3200 g2760 F48.0 tp3201 a(I7610000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb87@' p3202 tp3203 Rp3204 g2760 F13.0 tp3205 a(I7620000 g1 (g5 S'\xd7\xa3p=\n\xd77@' p3206 tp3207 Rp3208 g2760 F16.0 tp3209 a(I7630000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb87@' p3210 tp3211 Rp3212 g2760 F3.0 tp3213 a(I7640000 g1 (g5 S'\\\x8f\xc2\xf5(\x9c7@' p3214 tp3215 Rp3216 g2760 F25.0 tp3217 a(I7650000 g1 (g5 S'\xc3\xf5(\\\x8f\xc27@' p3218 tp3219 Rp3220 g2760 F15.0 tp3221 a(I7660000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb7@' p3222 tp3223 Rp3224 g2760 F18.0 tp3225 a(I7670000 g1 (g5 S'{\x14\xaeG\xe1\xfa7@' p3226 tp3227 Rp3228 g2760 F34.0 tp3229 a(I7680000 g1 (g5 S'\xb8\x1e\x85\xebQ\xf88@' p3230 tp3231 Rp3232 g2760 F10.0 tp3233 a(I7690000 g1 (g5 S'{\x14\xaeG\xe1z9@' p3234 tp3235 Rp3236 g2760 F26.0 tp3237 a(I7700000 g1 (g5 S'\xaeG\xe1z\x14\xee9@' p3238 tp3239 Rp3240 g2760 F29.0 tp3241 a(I7710000 g1 (g5 S'q=\n\xd7\xa3\xf09@' p3242 tp3243 Rp3244 g2760 F24.0 tp3245 a(I7720000 g1 (g5 S'\xb8\x1e\x85\xebQ\xf89@' p3246 tp3247 Rp3248 g2760 F27.0 tp3249 a(I7730000 g1 (g5 S'\xf6(\\\x8f\xc25:@' p3250 tp3251 Rp3252 g2760 F25.0 tp3253 a(I7740000 g1 (g5 S'\xecQ\xb8\x1e\x85k:@' p3254 tp3255 Rp3256 g2760 F30.0 tp3257 a(I7750000 g1 (g5 S'\xecQ\xb8\x1e\x85+:@' p3258 tp3259 Rp3260 g2760 F31.0 tp3261 a(I7760000 g1 (g5 S'\x14\xaeG\xe1z\xd4:@' p3262 tp3263 Rp3264 g2760 F29.0 tp3265 a(I7770000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc5:@' p3266 tp3267 Rp3268 g2760 F31.0 tp3269 a(I7780000 g1 (g5 S'\xc3\xf5(\\\x8fB;@' p3270 tp3271 Rp3272 g2760 F3.0 tp3273 a(I7790000 g1 (g5 S'\xc3\xf5(\\\x8f\x02;@' p3274 tp3275 Rp3276 g2760 F22.0 tp3277 a(I7800000 g1 (g5 S'{\x14\xaeG\xe1:;@' p3278 tp3279 Rp3280 g2760 F35.0 tp3281 a(I7810000 g1 (g5 S'R\xb8\x1e\x85\xeb\x11;@' p3282 tp3283 Rp3284 g2760 F14.0 tp3285 a(I7820000 g1 (g5 S'\x00\x00\x00\x00\x00\x00;@' p3286 tp3287 Rp3288 g2760 F44.0 tp3289 a(I7830000 g1 (g5 S'333333;@' p3290 tp3291 Rp3292 g2760 F5.0 tp3293 a(I7840000 g1 (g5 S'{\x14\xaeG\xe1z;@' p3294 tp3295 Rp3296 g2760 F8.0 tp3297 a(I7850000 g1 (g5 S'33333\xb3;@' p3298 tp3299 Rp3300 g2760 F32.0 tp3301 a(I7860000 g1 (g5 S'\xf6(\\\x8f\xc25<@' p3302 tp3303 Rp3304 g1 (g5 S'\xf6(\\\x8f\xc25<@' p3305 tp3306 Rp3307 F23.0 tp3308 a(I7870000 g1 (g5 S'{\x14\xaeG\xe1\xfa;@' p3309 tp3310 Rp3311 g3307 F14.0 tp3312 a(I7880000 g1 (g5 S'\x14\xaeG\xe1zT;@' p3313 tp3314 Rp3315 g3307 F14.0 tp3316 a(I7890000 g1 (g5 S'\xe1z\x14\xaeG\xe1;@' p3317 tp3318 Rp3319 g3307 F29.0 tp3320 a(I7900000 g1 (g5 S'\xaeG\xe1z\x14\xee;@' p3321 tp3322 Rp3323 g3307 F40.0 tp3324 a(I7910000 g1 (g5 S'\xb8\x1e\x85\xebQ\xf8;@' p3325 tp3326 Rp3327 g3307 F17.0 tp3328 a(I7920000 g1 (g5 S'\xb8\x1e\x85\xebQ\xf8;@' p3329 tp3330 Rp3331 g3307 F29.0 tp3332 a(I7930000 g1 (g5 S'\xb8\x1e\x85\xebQ\xf8;@' p3333 tp3334 Rp3335 g3307 F5.0 tp3336 a(I7940000 g1 (g5 S'333333;@' p3337 tp3338 Rp3339 g3307 F12.0 tp3340 a(I7950000 g1 (g5 S'R\xb8\x1e\x85\xeb\xd1;@' p3341 tp3342 Rp3343 g3307 F39.0 tp3344 a(I7960000 g1 (g5 S'\x9a\x99\x99\x99\x99\xd9;@' p3345 tp3346 Rp3347 g3307 F3.0 tp3348 a(I7970000 g1 (g5 S'\x1f\x85\xebQ\xb8\xde<@' p3349 tp3350 Rp3351 g1 (g5 S'\x1f\x85\xebQ\xb8\xde<@' p3352 tp3353 Rp3354 F93.0 tp3355 a(I7980000 g1 (g5 S'\xc3\xf5(\\\x8f\x02=@' p3356 tp3357 Rp3358 g1 (g5 S'\xc3\xf5(\\\x8f\x02=@' p3359 tp3360 Rp3361 F35.0 tp3362 a(I7990000 g1 (g5 S'\xd7\xa3p=\n\x17=@' p3363 tp3364 Rp3365 g1 (g5 S'\xd7\xa3p=\n\x17=@' p3366 tp3367 Rp3368 F36.0 tp3369 a(I8000000 g1 (g5 S'fffff&=@' p3370 tp3371 Rp3372 g1 (g5 S'\xf6(\\\x8f\xc25=@' p3373 tp3374 Rp3375 F55.0 tp3376 a(I8010000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8;@' p3377 tp3378 Rp3379 g3375 F16.0 tp3380 a(I8020000 g1 (g5 S'{\x14\xaeG\xe1\xba;@' p3381 tp3382 Rp3383 g3375 F45.0 tp3384 a(I8030000 g1 (g5 S'R\xb8\x1e\x85\xeb\x11<@' p3385 tp3386 Rp3387 g3375 F52.0 tp3388 a(I8040000 g1 (g5 S'\x00\x00\x00\x00\x00\x80;@' p3389 tp3390 Rp3391 g3375 F11.0 tp3392 a(I8050000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f;@' p3393 tp3394 Rp3395 g3375 F45.0 tp3396 a(I8060000 g1 (g5 S'\xaeG\xe1z\x14n;@' p3397 tp3398 Rp3399 g3375 F29.0 tp3400 a(I8070000 g1 (g5 S'\xaeG\xe1z\x14\xae;@' p3401 tp3402 Rp3403 g3375 F41.0 tp3404 a(I8080000 g1 (g5 S'H\xe1z\x14\xae\x87;@' p3405 tp3406 Rp3407 g3375 F16.0 tp3408 a(I8090000 g1 (g5 S'R\xb8\x1e\x85\xeb\x11;@' p3409 tp3410 Rp3411 g3375 F11.0 tp3412 a(I8100000 g1 (g5 S'\x14\xaeG\xe1z\x94;@' p3413 tp3414 Rp3415 g3375 F46.0 tp3416 a(I8110000 g1 (g5 S'ffffff;@' p3417 tp3418 Rp3419 g3375 F13.0 tp3420 a(I8120000 g1 (g5 S'=\n\xd7\xa3p};@' p3421 tp3422 Rp3423 g3375 F29.0 tp3424 a(I8130000 g1 (g5 S'fffff&;@' p3425 tp3426 Rp3427 g3375 F38.0 tp3428 a(I8140000 g1 (g5 S'{\x14\xaeG\xe1\xfa:@' p3429 tp3430 Rp3431 g3375 F38.0 tp3432 a(I8150000 g1 (g5 S')\\\x8f\xc2\xf5\xe8:@' p3433 tp3434 Rp3435 g3375 F32.0 tp3436 a(I8160000 g1 (g5 S'\x9a\x99\x99\x99\x99\xd9:@' p3437 tp3438 Rp3439 g3375 F25.0 tp3440 a(I8170000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8:@' p3441 tp3442 Rp3443 g3375 F29.0 tp3444 a(I8180000 g1 (g5 S'\xa4p=\n\xd7\xa3:@' p3445 tp3446 Rp3447 g3375 F66.0 tp3448 a(I8190000 g1 (g5 S'\x14\xaeG\xe1zT:@' p3449 tp3450 Rp3451 g3375 F20.0 tp3452 a(I8200000 g1 (g5 S'\x8f\xc2\xf5(\\\x0f;@' p3453 tp3454 Rp3455 g3375 F40.0 tp3456 a(I8210000 g1 (g5 S'\xf6(\\\x8f\xc2\xf5:@' p3457 tp3458 Rp3459 g3375 F9.0 tp3460 a(I8220000 g1 (g5 S'ffffff:@' p3461 tp3462 Rp3463 g3375 F8.0 tp3464 a(I8230000 g1 (g5 S'\x9a\x99\x99\x99\x99Y:@' p3465 tp3466 Rp3467 g3375 F30.0 tp3468 a(I8240000 g1 (g5 S'\n\xd7\xa3p=\n:@' p3469 tp3470 Rp3471 g3375 F13.0 tp3472 a(I8250000 g1 (g5 S'\x85\xebQ\xb8\x1e\x85:@' p3473 tp3474 Rp3475 g3375 F84.0 tp3476 a(I8260000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc:@' p3477 tp3478 Rp3479 g3375 F42.0 tp3480 a(I8270000 g1 (g5 S'\xc3\xf5(\\\x8f\x02;@' p3481 tp3482 Rp3483 g3375 F9.0 tp3484 a(I8280000 g1 (g5 S'\xaeG\xe1z\x14\xae:@' p3485 tp3486 Rp3487 g3375 F24.0 tp3488 a(I8290000 g1 (g5 S'33333\xb3:@' p3489 tp3490 Rp3491 g3375 F14.0 tp3492 a(I8300000 g1 (g5 S'q=\n\xd7\xa3\xf09@' p3493 tp3494 Rp3495 g3375 F31.0 tp3496 a(I8310000 g1 (g5 S'fffff&:@' p3497 tp3498 Rp3499 g3375 F52.0 tp3500 a(I8320000 g1 (g5 S'\x1f\x85\xebQ\xb8^:@' p3501 tp3502 Rp3503 g3375 F27.0 tp3504 a(I8330000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f:@' p3505 tp3506 Rp3507 g3375 F38.0 tp3508 a(I8340000 g1 (g5 S'R\xb8\x1e\x85\xeb\xd1:@' p3509 tp3510 Rp3511 g3375 F14.0 tp3512 a(I8350000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab:@' p3513 tp3514 Rp3515 g3375 F8.0 tp3516 a(I8360000 g1 (g5 S'\x14\xaeG\xe1zT:@' p3517 tp3518 Rp3519 g3375 F41.0 tp3520 a(I8370000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x8c:@' p3521 tp3522 Rp3523 g3375 F18.0 tp3524 a(I8380000 g1 (g5 S'\xb8\x1e\x85\xebQx:@' p3525 tp3526 Rp3527 g3375 F14.0 tp3528 a(I8390000 g1 (g5 S'H\xe1z\x14\xae\x07:@' p3529 tp3530 Rp3531 g3375 F8.0 tp3532 a(I8400000 g1 (g5 S'\\\x8f\xc2\xf5(\x9c9@' p3533 tp3534 Rp3535 g3375 F3.0 tp3536 a(I8410000 g1 (g5 S'H\xe1z\x14\xaeG:@' p3537 tp3538 Rp3539 g3375 F92.0 tp3540 a(I8420000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99:@' p3541 tp3542 Rp3543 g3375 F39.0 tp3544 a(I8430000 g1 (g5 S'\x8f\xc2\xf5(\\\x0f:@' p3545 tp3546 Rp3547 g3375 F7.0 tp3548 a(I8440000 g1 (g5 S'{\x14\xaeG\xe1\xfa9@' p3549 tp3550 Rp3551 g3375 F8.0 tp3552 a(I8450000 g1 (g5 S'\xb8\x1e\x85\xebQ\xf89@' p3553 tp3554 Rp3555 g3375 F16.0 tp3556 a(I8460000 g1 (g5 S'\x00\x00\x00\x00\x00\x00:@' p3557 tp3558 Rp3559 g3375 F23.0 tp3560 a(I8470000 g1 (g5 S'\xaeG\xe1z\x14\xae9@' p3561 tp3562 Rp3563 g3375 F21.0 tp3564 a(I8480000 g1 (g5 S'\x00\x00\x00\x00\x00\x809@' p3565 tp3566 Rp3567 g3375 F0.0 tp3568 a(I8490000 g1 (g5 S'\xb8\x1e\x85\xebQx9@' p3569 tp3570 Rp3571 g3375 F21.0 tp3572 a(I8500000 g1 (g5 S'\x1f\x85\xebQ\xb8\xde9@' p3573 tp3574 Rp3575 g3375 F43.0 tp3576 a(I8510000 g1 (g5 S'H\xe1z\x14\xae\xc79@' p3577 tp3578 Rp3579 g3375 F20.0 tp3580 a(I8520000 g1 (g5 S'\x14\xaeG\xe1z\xd49@' p3581 tp3582 Rp3583 g3375 F55.0 tp3584 a(I8530000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb9@' p3585 tp3586 Rp3587 g3375 F37.0 tp3588 a(I8540000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab9@' p3589 tp3590 Rp3591 g3375 F16.0 tp3592 a(I8550000 g1 (g5 S'\xf6(\\\x8f\xc2u:@' p3593 tp3594 Rp3595 g3375 F1.0 tp3596 a(I8560000 g1 (g5 S'\x14\xaeG\xe1z\xd49@' p3597 tp3598 Rp3599 g3375 F5.0 tp3600 a(I8570000 g1 (g5 S'\x8f\xc2\xf5(\\O:@' p3601 tp3602 Rp3603 g3375 F76.0 tp3604 a(I8580000 g1 (g5 S'\xaeG\xe1z\x14.:@' p3605 tp3606 Rp3607 g3375 F37.0 tp3608 a(I8590000 g1 (g5 S'\x9a\x99\x99\x99\x99\x999@' p3609 tp3610 Rp3611 g3375 F22.0 tp3612 a(I8600000 g1 (g5 S'fffff\xa69@' p3613 tp3614 Rp3615 g3375 F23.0 tp3616 a(I8610000 g1 (g5 S'q=\n\xd7\xa3\xf09@' p3617 tp3618 Rp3619 g3375 F25.0 tp3620 a(I8620000 g1 (g5 S'\xe1z\x14\xaeGa:@' p3621 tp3622 Rp3623 g3375 F42.0 tp3624 a(I8630000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb:@' p3625 tp3626 Rp3627 g3375 F25.0 tp3628 a(I8640000 g1 (g5 S'\x14\xaeG\xe1z\xd4:@' p3629 tp3630 Rp3631 g3375 F10.0 tp3632 a(I8650000 g1 (g5 S'\x85\xebQ\xb8\x1e\x85:@' p3633 tp3634 Rp3635 g3375 F38.0 tp3636 a(I8660000 g1 (g5 S'\x9a\x99\x99\x99\x99Y:@' p3637 tp3638 Rp3639 g3375 F29.0 tp3640 a(I8670000 g1 (g5 S')\\\x8f\xc2\xf5\xa8:@' p3641 tp3642 Rp3643 g3375 F48.0 tp3644 a(I8680000 g1 (g5 S'33333s:@' p3645 tp3646 Rp3647 g3375 F9.0 tp3648 a(I8690000 g1 (g5 S'\xa4p=\n\xd7\xa3:@' p3649 tp3650 Rp3651 g3375 F1.0 tp3652 a(I8700000 g1 (g5 S'33333\xb3:@' p3653 tp3654 Rp3655 g3375 F22.0 tp3656 a(I8710000 g1 (g5 S'=\n\xd7\xa3p\xfd9@' p3657 tp3658 Rp3659 g3375 F10.0 tp3660 a(I8720000 g1 (g5 S'fffff\xa6:@' p3661 tp3662 Rp3663 g3375 F24.0 tp3664 a(I8730000 g1 (g5 S'\x1f\x85\xebQ\xb8\xde:@' p3665 tp3666 Rp3667 g3375 F30.0 tp3668 a(I8740000 g1 (g5 S'\x00\x00\x00\x00\x00\xc0;@' p3669 tp3670 Rp3671 g3375 F24.0 tp3672 a(I8750000 g1 (g5 S'\xaeG\xe1z\x14\xee:@' p3673 tp3674 Rp3675 g3375 F20.0 tp3676 a(I8760000 g1 (g5 S'\x9a\x99\x99\x99\x99\x19;@' p3677 tp3678 Rp3679 g3375 F27.0 tp3680 a(I8770000 g1 (g5 S'\\\x8f\xc2\xf5(\xdc:@' p3681 tp3682 Rp3683 g3375 F37.0 tp3684 a(I8780000 g1 (g5 S'\xc3\xf5(\\\x8fB;@' p3685 tp3686 Rp3687 g3375 F39.0 tp3688 a(I8790000 g1 (g5 S'\xcd\xcc\xcc\xcc\xccL;@' p3689 tp3690 Rp3691 g3375 F25.0 tp3692 a(I8800000 g1 (g5 S'{\x14\xaeG\xe1\xba;@' p3693 tp3694 Rp3695 g3375 F29.0 tp3696 a(I8810000 g1 (g5 S'\xc3\xf5(\\\x8f\xc2;@' p3697 tp3698 Rp3699 g3375 F7.0 tp3700 a(I8820000 g1 (g5 S'\xc3\xf5(\\\x8f\x02<@' p3701 tp3702 Rp3703 g3375 F15.0 tp3704 a(I8830000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc5;@' p3705 tp3706 Rp3707 g3375 F26.0 tp3708 a(I8840000 g1 (g5 S'H\xe1z\x14\xaeG;@' p3709 tp3710 Rp3711 g3375 F21.0 tp3712 a(I8850000 g1 (g5 S'ffffff;@' p3713 tp3714 Rp3715 g3375 F43.0 tp3716 a(I8860000 g1 (g5 S'\xf6(\\\x8f\xc2\xb5:@' p3717 tp3718 Rp3719 g3375 F16.0 tp3720 a(I8870000 g1 (g5 S'ffffff:@' p3721 tp3722 Rp3723 g3375 F28.0 tp3724 a(I8880000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb89@' p3725 tp3726 Rp3727 g3375 F12.0 tp3728 a(I8890000 g1 (g5 S'R\xb8\x1e\x85\xeb\x91:@' p3729 tp3730 Rp3731 g3375 F17.0 tp3732 a(I8900000 g1 (g5 S'fffff\xa6:@' p3733 tp3734 Rp3735 g3375 F18.0 tp3736 a(I8910000 g1 (g5 S'\xd7\xa3p=\n\x97:@' p3737 tp3738 Rp3739 g3375 F30.0 tp3740 a(I8920000 g1 (g5 S'\xd7\xa3p=\n\xd7:@' p3741 tp3742 Rp3743 g3375 F14.0 tp3744 a(I8930000 g1 (g5 S'\x14\xaeG\xe1z\x14;@' p3745 tp3746 Rp3747 g3375 F38.0 tp3748 a(I8940000 g1 (g5 S'\xe1z\x14\xaeG!;@' p3749 tp3750 Rp3751 g3375 F30.0 tp3752 a(I8950000 g1 (g5 S'\n\xd7\xa3p=J;@' p3753 tp3754 Rp3755 g3375 F41.0 tp3756 a(I8960000 g1 (g5 S'q=\n\xd7\xa30;@' p3757 tp3758 Rp3759 g3375 F38.0 tp3760 a(I8970000 g1 (g5 S'{\x14\xaeG\xe1z;@' p3761 tp3762 Rp3763 g3375 F43.0 tp3764 a(I8980000 g1 (g5 S'\x8f\xc2\xf5(\\O;@' p3765 tp3766 Rp3767 g3375 F26.0 tp3768 a(I8990000 g1 (g5 S'=\n\xd7\xa3p\xfd:@' p3769 tp3770 Rp3771 g3375 F18.0 tp3772 a(I9000000 g1 (g5 S'H\xe1z\x14\xae\x07;@' p3773 tp3774 Rp3775 g3375 F36.0 tp3776 a(I9010000 g1 (g5 S'\x8f\xc2\xf5(\\\xcf:@' p3777 tp3778 Rp3779 g3375 F36.0 tp3780 a(I9020000 g1 (g5 S'\xaeG\xe1z\x14\xee:@' p3781 tp3782 Rp3783 g3375 F18.0 tp3784 a(I9030000 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e;@' p3785 tp3786 Rp3787 g3375 F21.0 tp3788 a(I9040000 g1 (g5 S')\\\x8f\xc2\xf5\xa8;@' p3789 tp3790 Rp3791 g3375 F17.0 tp3792 a(I9050000 g1 (g5 S'\\\x8f\xc2\xf5(\\;@' p3793 tp3794 Rp3795 g3375 F44.0 tp3796 a(I9060000 g1 (g5 S'=\n\xd7\xa3p=;@' p3797 tp3798 Rp3799 g3375 F33.0 tp3800 a(I9070000 g1 (g5 S'ffffff:@' p3801 tp3802 Rp3803 g3375 F28.0 tp3804 a(I9080000 g1 (g5 S')\\\x8f\xc2\xf5\xa8:@' p3805 tp3806 Rp3807 g3375 F16.0 tp3808 a(I9090000 g1 (g5 S'\xf6(\\\x8f\xc2u:@' p3809 tp3810 Rp3811 g3375 F18.0 tp3812 a(I9100000 g1 (g5 S'\xf6(\\\x8f\xc2\xf5:@' p3813 tp3814 Rp3815 g3375 F33.0 tp3816 a(I9110000 g1 (g5 S'q=\n\xd7\xa3\xb0:@' p3817 tp3818 Rp3819 g3375 F29.0 tp3820 a(I9120000 g1 (g5 S'ffffff:@' p3821 tp3822 Rp3823 g3375 F23.0 tp3824 a(I9130000 g1 (g5 S'\xb8\x1e\x85\xebQx:@' p3825 tp3826 Rp3827 g3375 F30.0 tp3828 a(I9140000 g1 (g5 S'\xc3\xf5(\\\x8f\x02:@' p3829 tp3830 Rp3831 g3375 F28.0 tp3832 a(I9150000 g1 (g5 S'\xb8\x1e\x85\xebQ\xf89@' p3833 tp3834 Rp3835 g3375 F26.0 tp3836 a(I9160000 g1 (g5 S')\\\x8f\xc2\xf5\xe89@' p3837 tp3838 Rp3839 g3375 F3.0 tp3840 a(I9170000 g1 (g5 S'q=\n\xd7\xa3\xb0:@' p3841 tp3842 Rp3843 g3375 F54.0 tp3844 a(I9180000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab:@' p3845 tp3846 Rp3847 g3375 F20.0 tp3848 a(I9190000 g1 (g5 S'\x1f\x85\xebQ\xb8\xde:@' p3849 tp3850 Rp3851 g3375 F30.0 tp3852 a(I9200000 g1 (g5 S'33333\xf3:@' p3853 tp3854 Rp3855 g3375 F28.0 tp3856 a(I9210000 g1 (g5 S')\\\x8f\xc2\xf5\xe8:@' p3857 tp3858 Rp3859 g3375 F27.0 tp3860 a(I9220000 g1 (g5 S'H\xe1z\x14\xae\xc7:@' p3861 tp3862 Rp3863 g3375 F33.0 tp3864 a(I9230000 g1 (g5 S'\xaeG\xe1z\x14\xae:@' p3865 tp3866 Rp3867 g3375 F17.0 tp3868 a(I9240000 g1 (g5 S'\xecQ\xb8\x1e\x85k:@' p3869 tp3870 Rp3871 g3375 F30.0 tp3872 a(I9250000 g1 (g5 S'\xecQ\xb8\x1e\x85+:@' p3873 tp3874 Rp3875 g3375 F29.0 tp3876 a(I9260000 g1 (g5 S'\n\xd7\xa3p=\xca9@' p3877 tp3878 Rp3879 g3375 F6.0 tp3880 a(I9270000 g1 (g5 S'=\n\xd7\xa3p}9@' p3881 tp3882 Rp3883 g3375 F15.0 tp3884 a(I9280000 g1 (g5 S'\xe1z\x14\xaeG!9@' p3885 tp3886 Rp3887 g3375 F9.0 tp3888 a(I9290000 g1 (g5 S'\xc3\xf5(\\\x8f\x828@' p3889 tp3890 Rp3891 g3375 F9.0 tp3892 a(I9300000 g1 (g5 S'\x00\x00\x00\x00\x00\x008@' p3893 tp3894 Rp3895 g3375 F23.0 tp3896 a(I9310000 g1 (g5 S'\xf6(\\\x8f\xc258@' p3897 tp3898 Rp3899 g3375 F19.0 tp3900 a(I9320000 g1 (g5 S'\xd7\xa3p=\nW8@' p3901 tp3902 Rp3903 g3375 F1.0 tp3904 a(I9330000 g1 (g5 S'\xd7\xa3p=\n\x178@' p3905 tp3906 Rp3907 g3375 F26.0 tp3908 a(I9340000 g1 (g5 S'=\n\xd7\xa3p\xfd7@' p3909 tp3910 Rp3911 g3375 F14.0 tp3912 a(I9350000 g1 (g5 S'\x1f\x85\xebQ\xb8^7@' p3913 tp3914 Rp3915 g3375 F6.0 tp3916 a(I9360000 g1 (g5 S'R\xb8\x1e\x85\xebQ7@' p3917 tp3918 Rp3919 g3375 F29.0 tp3920 a(I9370000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc57@' p3921 tp3922 Rp3923 g3375 F50.0 tp3924 a(I9380000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc7@' p3925 tp3926 Rp3927 g3375 F3.0 tp3928 a(I9390000 g1 (g5 S'q=\n\xd7\xa3\xb07@' p3929 tp3930 Rp3931 g3375 F34.0 tp3932 a(I9400000 g1 (g5 S'33333\xf37@' p3933 tp3934 Rp3935 g3375 F31.0 tp3936 a(I9410000 g1 (g5 S'\xc3\xf5(\\\x8f\xc27@' p3937 tp3938 Rp3939 g3375 F25.0 tp3940 a(I9420000 g1 (g5 S'\x8f\xc2\xf5(\\\x0f8@' p3941 tp3942 Rp3943 g3375 F33.0 tp3944 a(I9430000 g1 (g5 S'\x8f\xc2\xf5(\\\x0f8@' p3945 tp3946 Rp3947 g3375 F27.0 tp3948 a(I9440000 g1 (g5 S'\x1f\x85\xebQ\xb8\xde7@' p3949 tp3950 Rp3951 g3375 F26.0 tp3952 a(I9450000 g1 (g5 S'\xecQ\xb8\x1e\x85+8@' p3953 tp3954 Rp3955 g3375 F12.0 tp3956 a(I9460000 g1 (g5 S'\x14\xaeG\xe1zT8@' p3957 tp3958 Rp3959 g3375 F35.0 tp3960 a(I9470000 g1 (g5 S'\xc3\xf5(\\\x8f\x828@' p3961 tp3962 Rp3963 g3375 F32.0 tp3964 a(I9480000 g1 (g5 S'\x14\xaeG\xe1z\x948@' p3965 tp3966 Rp3967 g3375 F23.0 tp3968 a(I9490000 g1 (g5 S'33333\xb38@' p3969 tp3970 Rp3971 g3375 F35.0 tp3972 a(I9500000 g1 (g5 S'\xa4p=\n\xd7\xe38@' p3973 tp3974 Rp3975 g3375 F31.0 tp3976 a(I9510000 g1 (g5 S'\x9a\x99\x99\x99\x99\x198@' p3977 tp3978 Rp3979 g3375 F0.0 tp3980 a(I9520000 g1 (g5 S'{\x14\xaeG\xe1:8@' p3981 tp3982 Rp3983 g3375 F46.0 tp3984 a(I9530000 g1 (g5 S'q=\n\xd7\xa308@' p3985 tp3986 Rp3987 g3375 F15.0 tp3988 a(I9540000 g1 (g5 S'\xd7\xa3p=\n\xd77@' p3989 tp3990 Rp3991 g3375 F8.0 tp3992 a(I9550000 g1 (g5 S'\xa4p=\n\xd7\xe37@' p3993 tp3994 Rp3995 g3375 F31.0 tp3996 a(I9560000 g1 (g5 S'\xa4p=\n\xd7c8@' p3997 tp3998 Rp3999 g3375 F37.0 tp4000 a(I9570000 g1 (g5 S'\n\xd7\xa3p=\x8a8@' p4001 tp4002 Rp4003 g3375 F4.0 tp4004 a(I9580000 g1 (g5 S'\xe1z\x14\xaeG\xa18@' p4005 tp4006 Rp4007 g3375 F38.0 tp4008 a(I9590000 g1 (g5 S'fffff\xa68@' p4009 tp4010 Rp4011 g3375 F26.0 tp4012 a(I9600000 g1 (g5 S'33333\xf38@' p4013 tp4014 Rp4015 g3375 F20.0 tp4016 a(I9610000 g1 (g5 S'H\xe1z\x14\xae\x878@' p4017 tp4018 Rp4019 g3375 F24.0 tp4020 a(I9620000 g1 (g5 S'=\n\xd7\xa3p}8@' p4021 tp4022 Rp4023 g3375 F0.0 tp4024 a(I9630000 g1 (g5 S'H\xe1z\x14\xae\x878@' p4025 tp4026 Rp4027 g3375 F0.0 tp4028 a(I9640000 g1 (g5 S'33333s8@' p4029 tp4030 Rp4031 g3375 F50.0 tp4032 a(I9650000 g1 (g5 S')\\\x8f\xc2\xf5h8@' p4033 tp4034 Rp4035 g3375 F24.0 tp4036 a(I9660000 g1 (g5 S'\xd7\xa3p=\nW8@' p4037 tp4038 Rp4039 g3375 F9.0 tp4040 a(I9670000 g1 (g5 S'fffff\xe67@' p4041 tp4042 Rp4043 g3375 F24.0 tp4044 a(I9680000 g1 (g5 S'\xf6(\\\x8f\xc2u8@' p4045 tp4046 Rp4047 g3375 F40.0 tp4048 a(I9690000 g1 (g5 S'\x1f\x85\xebQ\xb8\xde8@' p4049 tp4050 Rp4051 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S'\x1f\x85\xebQ\xb8^7@' p4101 tp4102 Rp4103 g3375 F40.0 tp4104 a(I9830000 g1 (g5 S'fffff&7@' p4105 tp4106 Rp4107 g3375 F26.0 tp4108 a(I9840000 g1 (g5 S'\\\x8f\xc2\xf5(\x9c6@' p4109 tp4110 Rp4111 g3375 F0.0 tp4112 a(I9850000 g1 (g5 S'ffffff6@' p4113 tp4114 Rp4115 g3375 F18.0 tp4116 a(I9860000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc55@' p4117 tp4118 Rp4119 g3375 F0.0 tp4120 a(I9870000 g1 (g5 S'=\n\xd7\xa3p\xbd5@' p4121 tp4122 Rp4123 g3375 F22.0 tp4124 a(I9880000 g1 (g5 S'ffffff6@' p4125 tp4126 Rp4127 g3375 F26.0 tp4128 a(I9890000 g1 (g5 S'\xe1z\x14\xaeG\xe15@' p4129 tp4130 Rp4131 g3375 F24.0 tp4132 a(I9900000 g1 (g5 S'R\xb8\x1e\x85\xeb\x115@' p4133 tp4134 Rp4135 g3375 F14.0 tp4136 a(I9910000 g1 (g5 S'\x8f\xc2\xf5(\\O5@' p4137 tp4138 Rp4139 g3375 F18.0 tp4140 a(I9920000 g1 (g5 S'\x9a\x99\x99\x99\x99\xd94@' p4141 tp4142 Rp4143 g3375 F18.0 tp4144 a(I9930000 g1 (g5 S'\xf6(\\\x8f\xc2u4@' p4145 tp4146 Rp4147 g3375 F19.0 tp4148 a(I9940000 g1 (g5 S'\n\xd7\xa3p=\xca4@' p4149 tp4150 Rp4151 g3375 F29.0 tp4152 a(I9950000 g1 (g5 S'fffff\xe64@' p4153 tp4154 Rp4155 g3375 F36.0 tp4156 a(I9960000 g1 (g5 S'\x9a\x99\x99\x99\x99\x195@' p4157 tp4158 Rp4159 g3375 F38.0 tp4160 a(I9970000 g1 (g5 S'33333s5@' p4161 tp4162 Rp4163 g3375 F5.0 tp4164 a(I9980000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab4@' p4165 tp4166 Rp4167 g3375 F5.0 tp4168 a(I9983323 g1 (g5 S'\xaeG\xe1z\x14n4@' p4169 tp4170 Rp4171 g3375 F0.0 tp4172 a.(lp0 F0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 aF0.0 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(S'f8' p3 I0 I1 tp4 Rp5 (I3 S'<' p6 NNNI-1 I-1 I0 tp7 bS'/\xf4B/\xf4B4\xc0' p8 tp9 Rp10 F-inf F-20.0 tp11 a(I70000 g1 (g5 S'=\n\xd7\xa3p=4\xc0' p12 tp13 Rp14 F-inf F-21.0 tp15 a(I80000 g1 (g5 S'\xd0\x17\xf4\x05}A4\xc0' p16 tp17 Rp18 F-inf F-20.0 tp19 a(I90000 g1 (g5 S'\xab\xaa\xaa\xaa\xaa:4\xc0' p20 tp21 Rp22 F-inf F-20.0 tp23 a(I100000 g1 (g5 S'\xb8\x1e\x85\xebQ84\xc0' p24 tp25 Rp26 g1 (g5 S'q=\n\xd7\xa304\xc0' p27 tp28 Rp29 F-21.0 tp30 a(I110000 g1 (g5 S'\x00\x00\x00\x00\x00@4\xc0' p31 tp32 Rp33 g29 F-21.0 tp34 a(I120000 g1 (g5 S'\xb8\x1e\x85\xebQ84\xc0' p35 tp36 Rp37 g29 F-21.0 tp38 a(I130000 g1 (g5 S'H\xe1z\x14\xaeG4\xc0' p39 tp40 Rp41 g29 F-20.0 tp42 a(I140000 g1 (g5 S'\x85\xebQ\xb8\x1eE4\xc0' p43 tp44 Rp45 g29 F-20.0 tp46 a(I150000 g1 (g5 S'\x85\xebQ\xb8\x1eE4\xc0' p47 tp48 Rp49 g29 F-19.0 tp50 a(I160000 g1 (g5 S'\xd7\xa3p=\nW4\xc0' p51 tp52 Rp53 g29 F-20.0 tp54 a(I170000 g1 (g5 S'\x00\x00\x00\x00\x00@4\xc0' p55 tp56 Rp57 g29 F-20.0 tp58 a(I180000 g1 (g5 S'\xcd\xcc\xcc\xcc\xccL4\xc0' p59 tp60 Rp61 g29 F-21.0 tp62 a(I190000 g1 (g5 S'\n\xd7\xa3p=J4\xc0' p63 tp64 Rp65 g29 F-19.0 tp66 a(I200000 g1 (g5 S'\x85\xebQ\xb8\x1eE4\xc0' p67 tp68 Rp69 g29 F-21.0 tp70 a(I210000 g1 (g5 S'3333334\xc0' p71 tp72 Rp73 g29 F-21.0 tp74 a(I220000 g1 (g5 S'\xaeG\xe1z\x14.4\xc0' p75 tp76 Rp77 g1 (g5 S'\xaeG\xe1z\x14.4\xc0' p78 tp79 Rp80 F-21.0 tp81 a(I230000 g1 (g5 S')\\\x8f\xc2\xf5(4\xc0' p82 tp83 Rp84 g1 (g5 S')\\\x8f\xc2\xf5(4\xc0' p85 tp86 Rp87 F-21.0 tp88 a(I240000 g1 (g5 S'\xecQ\xb8\x1e\x85+4\xc0' p89 tp90 Rp91 g1 (g5 S'\xa4p=\n\xd7#4\xc0' p92 tp93 Rp94 F-20.0 tp95 a(I250000 g1 (g5 S'\xaeG\xe1z\x14.4\xc0' p96 tp97 Rp98 g94 F-21.0 tp99 a(I260000 g1 (g5 S'\xecQ\xb8\x1e\x85+4\xc0' p100 tp101 Rp102 g94 F-21.0 tp103 a(I270000 g1 (g5 S'{\x14\xaeG\xe1:4\xc0' p104 tp105 Rp106 g94 F-21.0 tp107 a(I280000 g1 (g5 S'\xb8\x1e\x85\xebQ84\xc0' p108 tp109 Rp110 g94 F-20.0 tp111 a(I290000 g1 (g5 S'\x85\xebQ\xb8\x1eE4\xc0' p112 tp113 Rp114 g94 F-21.0 tp115 a(I300000 g1 (g5 S'H\xe1z\x14\xaeG4\xc0' p116 tp117 Rp118 g94 F-20.0 tp119 a(I310000 g1 (g5 S'\n\xd7\xa3p=J4\xc0' p120 tp121 Rp122 g94 F-20.0 tp123 a(I320000 g1 (g5 S'\x14\xaeG\xe1zT4\xc0' p124 tp125 Rp126 g94 F-21.0 tp127 a(I330000 g1 (g5 S'H\xe1z\x14\xaeG4\xc0' p128 tp129 Rp130 g94 F-17.0 tp131 a(I340000 g1 (g5 S'\xcd\xcc\xcc\xcc\xccL4\xc0' p132 tp133 Rp134 g94 F-21.0 tp135 a(I350000 g1 (g5 S'\x1f\x85\xebQ\xb8^4\xc0' p136 tp137 Rp138 g94 F-21.0 tp139 a(I360000 g1 (g5 S'\x9a\x99\x99\x99\x99Y4\xc0' p140 tp141 Rp142 g94 F-21.0 tp143 a(I370000 g1 (g5 S'R\xb8\x1e\x85\xebQ4\xc0' p144 tp145 Rp146 g94 F-20.0 tp147 a(I380000 g1 (g5 S'\xd7\xa3p=\nW4\xc0' p148 tp149 Rp150 g94 F-21.0 tp151 a(I390000 g1 (g5 S'\xd7\xa3p=\nW4\xc0' p152 tp153 Rp154 g94 F-19.0 tp155 a(I400000 g1 (g5 S'\x14\xaeG\xe1zT4\xc0' p156 tp157 Rp158 g94 F-20.0 tp159 a(I410000 g1 (g5 S'\n\xd7\xa3p=J4\xc0' p160 tp161 Rp162 g94 F-20.0 tp163 a(I420000 g1 (g5 S'\x00\x00\x00\x00\x00@4\xc0' p164 tp165 Rp166 g94 F-21.0 tp167 a(I430000 g1 (g5 S'\x9a\x99\x99\x99\x99\x194\xc0' p168 tp169 Rp170 g1 (g5 S'\x9a\x99\x99\x99\x99\x194\xc0' p171 tp172 Rp173 F-16.0 tp174 a(I440000 g1 (g5 S'\xb8\x1e\x85\xebQ\xf83\xc0' p175 tp176 Rp177 g1 (g5 S'\xb8\x1e\x85\xebQ\xf83\xc0' p178 tp179 Rp180 F-18.0 tp181 a(I450000 g1 (g5 S'\x1f\x85\xebQ\xb8\xde3\xc0' p182 tp183 Rp184 g1 (g5 S'\x1f\x85\xebQ\xb8\xde3\xc0' p185 tp186 Rp187 F-20.0 tp188 a(I460000 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e3\xc0' p189 tp190 Rp191 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e3\xc0' p192 tp193 Rp194 F-20.0 tp195 a(I470000 g1 (g5 S'33333s3\xc0' p196 tp197 Rp198 g1 (g5 S'33333s3\xc0' p199 tp200 Rp201 F-18.0 tp202 a(I480000 g1 (g5 S'\x8f\xc2\xf5(\\O3\xc0' p203 tp204 Rp205 g1 (g5 S'\x8f\xc2\xf5(\\O3\xc0' p206 tp207 Rp208 F-20.0 tp209 a(I490000 g1 (g5 S'\xb8\x1e\x85\xebQ83\xc0' p210 tp211 Rp212 g1 (g5 S'\xb8\x1e\x85\xebQ83\xc0' p213 tp214 Rp215 F-18.0 tp216 a(I500000 g1 (g5 S'H\xe1z\x14\xae\x073\xc0' p217 tp218 Rp219 g1 (g5 S'H\xe1z\x14\xae\x073\xc0' p220 tp221 Rp222 F-16.0 tp223 a(I510000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb2\xc0' p224 tp225 Rp226 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb2\xc0' p227 tp228 Rp229 F-16.0 tp230 a(I520000 g1 (g5 S'\x00\x00\x00\x00\x00\xc02\xc0' p231 tp232 Rp233 g1 (g5 S'\x00\x00\x00\x00\x00\xc02\xc0' p234 tp235 Rp236 F-15.0 tp237 a(I530000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f2\xc0' p238 tp239 Rp240 g1 (g5 S'\x8f\xc2\xf5(\\\x8f2\xc0' p241 tp242 Rp243 F-18.0 tp244 a(I540000 g1 (g5 S')\\\x8f\xc2\xf5h2\xc0' p245 tp246 Rp247 g1 (g5 S')\\\x8f\xc2\xf5h2\xc0' p248 tp249 Rp250 F-15.0 tp251 a(I550000 g1 (g5 S'\x00\x00\x00\x00\x00@2\xc0' p252 tp253 Rp254 g1 (g5 S'\x00\x00\x00\x00\x00@2\xc0' p255 tp256 Rp257 F-18.0 tp258 a(I560000 g1 (g5 S'\xd7\xa3p=\n\x172\xc0' p259 tp260 Rp261 g1 (g5 S'\xd7\xa3p=\n\x172\xc0' p262 tp263 Rp264 F-18.0 tp265 a(I570000 g1 (g5 S'\x8f\xc2\xf5(\\\x0f2\xc0' p266 tp267 Rp268 g1 (g5 S'\x8f\xc2\xf5(\\\x0f2\xc0' p269 tp270 Rp271 F-15.0 tp272 a(I580000 g1 (g5 S'fffff\xe61\xc0' p273 tp274 Rp275 g1 (g5 S'fffff\xe61\xc0' p276 tp277 Rp278 F-15.0 tp279 a(I590000 g1 (g5 S'\x14\xaeG\xe1z\xd41\xc0' p280 tp281 Rp282 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc1\xc0' p283 tp284 Rp285 F-20.0 tp286 a(I600000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab1\xc0' p287 tp288 Rp289 g1 (g5 S'\xecQ\xb8\x1e\x85\xab1\xc0' p290 tp291 Rp292 F-17.0 tp293 a(I610000 g1 (g5 S'{\x14\xaeG\xe1\xba1\xc0' p294 tp295 Rp296 g1 (g5 S'fffff\xa61\xc0' p297 tp298 Rp299 F-21.0 tp300 a(I620000 g1 (g5 S'33333\xb31\xc0' p301 tp302 Rp303 g299 F-19.0 tp304 a(I630000 g1 (g5 S'\xa4p=\n\xd7\xa31\xc0' p305 tp306 Rp307 g1 (g5 S'\xe1z\x14\xaeG\xa11\xc0' p308 tp309 Rp310 F-19.0 tp311 a(I640000 g1 (g5 S'\x00\x00\x00\x00\x00\x801\xc0' p312 tp313 Rp314 g1 (g5 S'\x00\x00\x00\x00\x00\x801\xc0' p315 tp316 Rp317 F-16.0 tp318 a(I650000 g1 (g5 S'\xecQ\xb8\x1e\x85k1\xc0' p319 tp320 Rp321 g1 (g5 S')\\\x8f\xc2\xf5h1\xc0' p322 tp323 Rp324 F-19.0 tp325 a(I660000 g1 (g5 S'\xe1z\x14\xaeGa1\xc0' p326 tp327 Rp328 g1 (g5 S'\\\x8f\xc2\xf5(\\1\xc0' p329 tp330 Rp331 F-20.0 tp332 a(I670000 g1 (g5 S'\xd7\xa3p=\nW1\xc0' p333 tp334 Rp335 g1 (g5 S'\xd7\xa3p=\nW1\xc0' p336 tp337 Rp338 F-18.0 tp339 a(I680000 g1 (g5 S'\n\xd7\xa3p=J1\xc0' p340 tp341 Rp342 g1 (g5 S'\n\xd7\xa3p=J1\xc0' p343 tp344 Rp345 F-18.0 tp346 a(I690000 g1 (g5 S'\x14\xaeG\xe1zT1\xc0' p347 tp348 Rp349 g345 F-17.0 tp350 a(I700000 g1 (g5 S'\xc3\xf5(\\\x8fB1\xc0' p351 tp352 Rp353 g1 (g5 S'\xc3\xf5(\\\x8fB1\xc0' p354 tp355 Rp356 F-15.0 tp357 a(I710000 g1 (g5 S'\xb8\x1e\x85\xebQ81\xc0' p358 tp359 Rp360 g1 (g5 S'\xb8\x1e\x85\xebQ81\xc0' p361 tp362 Rp363 F-19.0 tp364 a(I720000 g1 (g5 S'q=\n\xd7\xa301\xc0' p365 tp366 Rp367 g1 (g5 S'q=\n\xd7\xa301\xc0' p368 tp369 Rp370 F-16.0 tp371 a(I730000 g1 (g5 S'\x8f\xc2\xf5(\\\x0f1\xc0' p372 tp373 Rp374 g1 (g5 S'\x8f\xc2\xf5(\\\x0f1\xc0' p375 tp376 Rp377 F-14.0 tp378 a(I740000 g1 (g5 S'\x8f\xc2\xf5(\\\x0f1\xc0' p379 tp380 Rp381 g1 (g5 S'\n\xd7\xa3p=\n1\xc0' p382 tp383 Rp384 F-16.0 tp385 a(I750000 g1 (g5 S'\x00\x00\x00\x00\x00\x001\xc0' p386 tp387 Rp388 g1 (g5 S'{\x14\xaeG\xe1\xfa0\xc0' p389 tp390 Rp391 F-14.0 tp392 a(I760000 g1 (g5 S')\\\x8f\xc2\xf5\xe80\xc0' p393 tp394 Rp395 g1 (g5 S')\\\x8f\xc2\xf5\xe80\xc0' p396 tp397 Rp398 F-14.0 tp399 a(I770000 g1 (g5 S'\xc3\xf5(\\\x8f\xc20\xc0' p400 tp401 Rp402 g1 (g5 S'\x00\x00\x00\x00\x00\xc00\xc0' p403 tp404 Rp405 F-19.0 tp406 a(I780000 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e0\xc0' p407 tp408 Rp409 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e0\xc0' p410 tp411 Rp412 F-11.0 tp413 a(I790000 g1 (g5 S'\xd7\xa3p=\n\x970\xc0' p414 tp415 Rp416 g1 (g5 S'\xd7\xa3p=\n\x970\xc0' p417 tp418 Rp419 F-13.0 tp420 a(I800000 g1 (g5 S'{\x14\xaeG\xe1z0\xc0' p421 tp422 Rp423 g1 (g5 S'{\x14\xaeG\xe1z0\xc0' p424 tp425 Rp426 F-18.0 tp427 a(I810000 g1 (g5 S'\x1f\x85\xebQ\xb8^0\xc0' p428 tp429 Rp430 g1 (g5 S'\\\x8f\xc2\xf5(\\0\xc0' p431 tp432 Rp433 F-17.0 tp434 a(I820000 g1 (g5 S'\xe1z\x14\xaeGa0\xc0' p435 tp436 Rp437 g433 F-18.0 tp438 a(I830000 g1 (g5 S'\x8f\xc2\xf5(\\O0\xc0' p439 tp440 Rp441 g1 (g5 S'\x8f\xc2\xf5(\\O0\xc0' p442 tp443 Rp444 F-14.0 tp445 a(I840000 g1 (g5 S'q=\n\xd7\xa300\xc0' p446 tp447 Rp448 g1 (g5 S'q=\n\xd7\xa300\xc0' p449 tp450 Rp451 F-16.0 tp452 a(I850000 g1 (g5 S'\xd7\xa3p=\n\x170\xc0' p453 tp454 Rp455 g1 (g5 S'\xd7\xa3p=\n\x170\xc0' p456 tp457 Rp458 F-15.0 tp459 a(I860000 g1 (g5 S'\xd7\xa3p=\n\xd7/\xc0' p460 tp461 Rp462 g1 (g5 S'\xd7\xa3p=\n\xd7/\xc0' p463 tp464 Rp465 F-11.0 tp466 a(I870000 g1 (g5 S'{\x14\xaeG\xe1z/\xc0' p467 tp468 Rp469 g1 (g5 S'{\x14\xaeG\xe1z/\xc0' p470 tp471 Rp472 F-13.0 tp473 a(I880000 g1 (g5 S'\xc3\xf5(\\\x8fB/\xc0' p474 tp475 Rp476 g1 (g5 S'\xc3\xf5(\\\x8fB/\xc0' p477 tp478 Rp479 F-13.0 tp480 a(I890000 g1 (g5 S'\x85\xebQ\xb8\x1e\x05/\xc0' p481 tp482 Rp483 g1 (g5 S'\x85\xebQ\xb8\x1e\x05/\xc0' p484 tp485 Rp486 F-18.0 tp487 a(I900000 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e.\xc0' p488 tp489 Rp490 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e.\xc0' p491 tp492 Rp493 F-11.0 tp494 a(I910000 g1 (g5 S'\xb8\x1e\x85\xebQ8.\xc0' p495 tp496 Rp497 g1 (g5 S'\xb8\x1e\x85\xebQ8.\xc0' p498 tp499 Rp500 F-12.0 tp501 a(I920000 g1 (g5 S'\n\xd7\xa3p=\n.\xc0' p502 tp503 Rp504 g1 (g5 S'\n\xd7\xa3p=\n.\xc0' p505 tp506 Rp507 F-13.0 tp508 a(I930000 g1 (g5 S'H\xe1z\x14\xae\xc7-\xc0' p509 tp510 Rp511 g1 (g5 S'H\xe1z\x14\xae\xc7-\xc0' p512 tp513 Rp514 F-15.0 tp515 a(I940000 g1 (g5 S'\xaeG\xe1z\x14.-\xc0' p516 tp517 Rp518 g1 (g5 S'\xaeG\xe1z\x14.-\xc0' p519 tp520 Rp521 F-15.0 tp522 a(I950000 g1 (g5 S'\xf6(\\\x8f\xc2\xf5,\xc0' p523 tp524 Rp525 g1 (g5 S'\xf6(\\\x8f\xc2\xf5,\xc0' p526 tp527 Rp528 F-14.0 tp529 a(I960000 g1 (g5 S'R\xb8\x1e\x85\xeb\xd1,\xc0' p530 tp531 Rp532 g1 (g5 S'R\xb8\x1e\x85\xeb\xd1,\xc0' p533 tp534 Rp535 F-12.0 tp536 a(I970000 g1 (g5 S'\x85\xebQ\xb8\x1e\x85,\xc0' p537 tp538 Rp539 g1 (g5 S'\x85\xebQ\xb8\x1e\x85,\xc0' p540 tp541 Rp542 F-7.0 tp543 a(I980000 g1 (g5 S'\xc3\xf5(\\\x8fB,\xc0' p544 tp545 Rp546 g1 (g5 S'\xb8\x1e\x85\xebQ8,\xc0' p547 tp548 Rp549 F-18.0 tp550 a(I990000 g1 (g5 S'\xaeG\xe1z\x14\xae+\xc0' p551 tp552 Rp553 g1 (g5 S'\xaeG\xe1z\x14\xae+\xc0' p554 tp555 Rp556 F-6.0 tp557 a(I1000000 g1 (g5 S'=\n\xd7\xa3p=+\xc0' p558 tp559 Rp560 g1 (g5 S'=\n\xd7\xa3p=+\xc0' p561 tp562 Rp563 F-8.0 tp564 a(I1010000 g1 (g5 S'\x9a\x99\x99\x99\x99\x19+\xc0' p565 tp566 Rp567 g1 (g5 S'\x9a\x99\x99\x99\x99\x19+\xc0' p568 tp569 Rp570 F-8.0 tp571 a(I1020000 g1 (g5 S'\xf6(\\\x8f\xc2\xf5*\xc0' p572 tp573 Rp574 g1 (g5 S'R\xb8\x1e\x85\xeb\xd1*\xc0' p575 tp576 Rp577 F-16.0 tp578 a(I1030000 g1 (g5 S'\x00\x00\x00\x00\x00\x80*\xc0' p579 tp580 Rp581 g1 (g5 S'\x00\x00\x00\x00\x00\x80*\xc0' p582 tp583 Rp584 F-13.0 tp585 a(I1040000 g1 (g5 S'\xd7\xa3p=\nW*\xc0' p586 tp587 Rp588 g1 (g5 S'\xd7\xa3p=\nW*\xc0' p589 tp590 Rp591 F-10.0 tp592 a(I1050000 g1 (g5 S'\xc3\xf5(\\\x8f\xc2)\xc0' p593 tp594 Rp595 g1 (g5 S'\xc3\xf5(\\\x8f\xc2)\xc0' p596 tp597 Rp598 F-7.0 tp599 a(I1060000 g1 (g5 S'\x00\x00\x00\x00\x00\x80)\xc0' p600 tp601 Rp602 g1 (g5 S'\x00\x00\x00\x00\x00\x80)\xc0' p603 tp604 Rp605 F-7.0 tp606 a(I1070000 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e)\xc0' p607 tp608 Rp609 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e)\xc0' p610 tp611 Rp612 F-10.0 tp613 a(I1080000 g1 (g5 S'H\xe1z\x14\xae\xc7(\xc0' p614 tp615 Rp616 g1 (g5 S'H\xe1z\x14\xae\xc7(\xc0' p617 tp618 Rp619 F-5.0 tp620 a(I1090000 g1 (g5 S'333333(\xc0' p621 tp622 Rp623 g1 (g5 S'333333(\xc0' p624 tp625 Rp626 F-7.0 tp627 a(I1100000 g1 (g5 S'\xb8\x1e\x85\xebQ8(\xc0' p628 tp629 Rp630 g1 (g5 S'\n\xd7\xa3p=\n(\xc0' p631 tp632 Rp633 F-17.0 tp634 a(I1110000 g1 (g5 S"fffff\xe6'\xc0" p635 tp636 Rp637 g1 (g5 S"fffff\xe6'\xc0" p638 tp639 Rp640 F-15.0 tp641 a(I1120000 g1 (g5 S"\\\x8f\xc2\xf5(\\'\xc0" p642 tp643 Rp644 g1 (g5 S"\\\x8f\xc2\xf5(\\'\xc0" p645 tp646 Rp647 F-13.0 tp648 a(I1130000 g1 (g5 S"\n\xd7\xa3p=\n'\xc0" p649 tp650 Rp651 g1 (g5 S"\n\xd7\xa3p=\n'\xc0" p652 tp653 Rp654 F-9.0 tp655 a(I1140000 g1 (g5 S'\xd7\xa3p=\n\xd7&\xc0' p656 tp657 Rp658 g1 (g5 S'R\xb8\x1e\x85\xeb\xd1&\xc0' p659 tp660 Rp661 F-12.0 tp662 a(I1150000 g1 (g5 S'\n\xd7\xa3p=\x8a&\xc0' p663 tp664 Rp665 g1 (g5 S'\xf6(\\\x8f\xc2u&\xc0' p666 tp667 Rp668 F-13.0 tp669 a(I1160000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb%\xc0' p670 tp671 Rp672 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb%\xc0' p673 tp674 Rp675 F-14.0 tp676 a(I1170000 g1 (g5 S'\x85\xebQ\xb8\x1e\x85%\xc0' p677 tp678 Rp679 g1 (g5 S'\x85\xebQ\xb8\x1e\x85%\xc0' p680 tp681 Rp682 F-10.0 tp683 a(I1180000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8$\xc0' p684 tp685 Rp686 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8$\xc0' p687 tp688 Rp689 F-6.0 tp690 a(I1190000 g1 (g5 S'\xf6(\\\x8f\xc2u$\xc0' p691 tp692 Rp693 g1 (g5 S'\xf6(\\\x8f\xc2u$\xc0' p694 tp695 Rp696 F-9.0 tp697 a(I1200000 g1 (g5 S'\x9a\x99\x99\x99\x99\x19$\xc0' p698 tp699 Rp700 g1 (g5 S'\x9a\x99\x99\x99\x99\x19$\xc0' p701 tp702 Rp703 F-10.0 tp704 a(I1210000 g1 (g5 S'\xe1z\x14\xaeGa#\xc0' p705 tp706 Rp707 g1 (g5 S'\xe1z\x14\xaeGa#\xc0' p708 tp709 Rp710 F-6.0 tp711 a(I1220000 g1 (g5 S'\x85\xebQ\xb8\x1e\x05#\xc0' p712 tp713 Rp714 g1 (g5 S'\x85\xebQ\xb8\x1e\x05#\xc0' p715 tp716 Rp717 F-8.0 tp718 a(I1230000 g1 (g5 S'=\n\xd7\xa3p="\xc0' p719 tp720 Rp721 g1 (g5 S'=\n\xd7\xa3p="\xc0' p722 tp723 Rp724 F-4.0 tp725 a(I1240000 g1 (g5 S'\xa4p=\n\xd7#"\xc0' p726 tp727 Rp728 g1 (g5 S'\xa4p=\n\xd7#"\xc0' p729 tp730 Rp731 F-9.0 tp732 a(I1250000 g1 (g5 S'\xa4p=\n\xd7\xa3!\xc0' p733 tp734 Rp735 g1 (g5 S'\xa4p=\n\xd7\xa3!\xc0' p736 tp737 Rp738 F-4.0 tp739 a(I1260000 g1 (g5 S'\x85\xebQ\xb8\x1e\x05!\xc0' p740 tp741 Rp742 g1 (g5 S'\x85\xebQ\xb8\x1e\x05!\xc0' p743 tp744 Rp745 F-4.0 tp746 a(I1270000 g1 (g5 S'\xb8\x1e\x85\xebQ8 \xc0' p747 tp748 Rp749 g1 (g5 S'\xb8\x1e\x85\xebQ8 \xc0' p750 tp751 Rp752 F-4.0 tp753 a(I1280000 g1 (g5 S'\n\xd7\xa3p=\n \xc0' p754 tp755 Rp756 g1 (g5 S'\xf6(\\\x8f\xc2\xf5\x1f\xc0' p757 tp758 Rp759 F-8.0 tp760 a(I1290000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x1e\xc0' p761 tp762 Rp763 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x1e\xc0' p764 tp765 Rp766 F-9.0 tp767 a(I1300000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x1e\xc0' p768 tp769 Rp770 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x1e\xc0' p771 tp772 Rp773 F-1.0 tp774 a(I1310000 g1 (g5 S'\xf6(\\\x8f\xc2\xf5\x1c\xc0' p775 tp776 Rp777 g1 (g5 S'\xf6(\\\x8f\xc2\xf5\x1c\xc0' p778 tp779 Rp780 F2.0 tp781 a(I1320000 g1 (g5 S'\xaeG\xe1z\x14\xae\x1b\xc0' p782 tp783 Rp784 g1 (g5 S'\xaeG\xe1z\x14\xae\x1b\xc0' p785 tp786 Rp787 F-8.0 tp788 a(I1330000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f\x1a\xc0' p789 tp790 Rp791 g1 (g5 S'\x8f\xc2\xf5(\\\x8f\x1a\xc0' p792 tp793 Rp794 F2.0 tp795 a(I1340000 g1 (g5 S'ffffff\x19\xc0' p796 tp797 Rp798 g1 (g5 S'ffffff\x19\xc0' p799 tp800 Rp801 F3.0 tp802 a(I1350000 g1 (g5 S'\xf6(\\\x8f\xc2\xf5\x17\xc0' p803 tp804 Rp805 g1 (g5 S'\xf6(\\\x8f\xc2\xf5\x17\xc0' p806 tp807 Rp808 F-6.0 tp809 a(I1360000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x16\xc0' p810 tp811 Rp812 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\x16\xc0' p813 tp814 Rp815 F-1.0 tp816 a(I1370000 g1 (g5 S'\n\xd7\xa3p=\n\x16\xc0' p817 tp818 Rp819 g1 (g5 S'\n\xd7\xa3p=\n\x16\xc0' p820 tp821 Rp822 F-3.0 tp823 a(I1380000 g1 (g5 S'\\\x8f\xc2\xf5(\\\x14\xc0' p824 tp825 Rp826 g1 (g5 S'\\\x8f\xc2\xf5(\\\x14\xc0' p827 tp828 Rp829 F3.0 tp830 a(I1390000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f\x13\xc0' p831 tp832 Rp833 g1 (g5 S'\x8f\xc2\xf5(\\\x8f\x13\xc0' p834 tp835 Rp836 F2.0 tp837 a(I1400000 g1 (g5 S'\x85\xebQ\xb8\x1e\x85\x12\xc0' p838 tp839 Rp840 g1 (g5 S'\x85\xebQ\xb8\x1e\x85\x12\xc0' p841 tp842 Rp843 F-6.0 tp844 a(I1410000 g1 (g5 S'\xaeG\xe1z\x14\xae\x11\xc0' p845 tp846 Rp847 g1 (g5 S'\xaeG\xe1z\x14\xae\x11\xc0' p848 tp849 Rp850 F5.0 tp851 a(I1420000 g1 (g5 S'\x85\xebQ\xb8\x1e\x85\x0f\xc0' p852 tp853 Rp854 g1 (g5 S'\x85\xebQ\xb8\x1e\x85\x0f\xc0' p855 tp856 Rp857 F4.0 tp858 a(I1430000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\n\xc0' p859 tp860 Rp861 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\n\xc0' p862 tp863 Rp864 F2.0 tp865 a(I1440000 g1 (g5 S'\x14\xaeG\xe1z\x14\x08\xc0' p866 tp867 Rp868 g1 (g5 S'\x14\xaeG\xe1z\x14\x08\xc0' p869 tp870 Rp871 F4.0 tp872 a(I1450000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\x07\xc0' p873 tp874 Rp875 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\x07\xc0' p876 tp877 Rp878 F-5.0 tp879 a(I1460000 g1 (g5 S'\x14\xaeG\xe1z\x14\x04\xc0' p880 tp881 Rp882 g1 (g5 S'\x14\xaeG\xe1z\x14\x04\xc0' p883 tp884 Rp885 F1.0 tp886 a(I1470000 g1 (g5 S'ffffff\x00\xc0' p887 tp888 Rp889 g1 (g5 S'ffffff\x00\xc0' p890 tp891 Rp892 F2.0 tp893 a(I1480000 g1 (g5 S'q=\n\xd7\xa3p\xf9\xbf' p894 tp895 Rp896 g1 (g5 S'q=\n\xd7\xa3p\xf9\xbf' p897 tp898 Rp899 F4.0 tp900 a(I1490000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f\xf2\xbf' p901 tp902 Rp903 g1 (g5 S'\x8f\xc2\xf5(\\\x8f\xf2\xbf' p904 tp905 Rp906 F5.0 tp907 a(I1500000 g1 (g5 S'\n\xd7\xa3p=\n\xe7\xbf' p908 tp909 Rp910 g1 (g5 S'\n\xd7\xa3p=\n\xe7\xbf' p911 tp912 Rp913 F8.0 tp914 a(I1510000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xb9\xbf' p915 tp916 Rp917 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xb9\xbf' p918 tp919 Rp920 F8.0 tp921 a(I1520000 g1 (g5 S'R\xb8\x1e\x85\xebQ\xd8?' p922 tp923 Rp924 g1 (g5 S'R\xb8\x1e\x85\xebQ\xd8?' p925 tp926 Rp927 F8.0 tp928 a(I1530000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xe9?' p929 tp930 Rp931 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xe9?' p932 tp933 Rp934 F9.0 tp935 a(I1540000 g1 (g5 S'\xa4p=\n\xd7\xa3\xf0?' p936 tp937 Rp938 g1 (g5 S'\xa4p=\n\xd7\xa3\xf0?' p939 tp940 Rp941 F8.0 tp942 a(I1550000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xf5?' p943 tp944 Rp945 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xf5?' p946 tp947 Rp948 F6.0 tp949 a(I1560000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xfa?' p950 tp951 Rp952 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8\xfa?' p953 tp954 Rp955 F8.0 tp956 a(I1570000 g1 (g5 S'\xf6(\\\x8f\xc2\xf5\x00@' p957 tp958 Rp959 g1 (g5 S'\xf6(\\\x8f\xc2\xf5\x00@' p960 tp961 Rp962 F9.0 tp963 a(I1580000 g1 (g5 S'q=\n\xd7\xa3p\x05@' p964 tp965 Rp966 g1 (g5 S'q=\n\xd7\xa3p\x05@' p967 tp968 Rp969 F9.0 tp970 a(I1590000 g1 (g5 S'\n\xd7\xa3p=\n\t@' p971 tp972 Rp973 g1 (g5 S'\n\xd7\xa3p=\n\t@' p974 tp975 Rp976 F12.0 tp977 a(I1600000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\r@' p978 tp979 Rp980 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\r@' p981 tp982 Rp983 F15.0 tp984 a(I1610000 g1 (g5 S'\n\xd7\xa3p=\n\x12@' p985 tp986 Rp987 g1 (g5 S'\n\xd7\xa3p=\n\x12@' p988 tp989 Rp990 F18.0 tp991 a(I1620000 g1 (g5 S'\x85\xebQ\xb8\x1e\x85\x14@' p992 tp993 Rp994 g1 (g5 S'\x85\xebQ\xb8\x1e\x85\x14@' p995 tp996 Rp997 F19.0 tp998 a(I1630000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\x16@' p999 tp1000 Rp1001 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\x16@' p1002 tp1003 Rp1004 F12.0 tp1005 a(I1640000 g1 (g5 S'H\xe1z\x14\xaeG\x18@' p1006 tp1007 Rp1008 g1 (g5 S'H\xe1z\x14\xaeG\x18@' p1009 tp1010 Rp1011 F13.0 tp1012 a(I1650000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\x1a@' p1013 tp1014 Rp1015 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\x1a@' p1016 tp1017 Rp1018 F13.0 tp1019 a(I1660000 g1 (g5 S'\x14\xaeG\xe1z\x14\x1d@' p1020 tp1021 Rp1022 g1 (g5 S'\x14\xaeG\xe1z\x14\x1d@' p1023 tp1024 Rp1025 F14.0 tp1026 a(I1670000 g1 (g5 S'q=\n\xd7\xa3p @' p1027 tp1028 Rp1029 g1 (g5 S'q=\n\xd7\xa3p @' p1030 tp1031 Rp1032 F19.0 tp1033 a(I1680000 g1 (g5 S'\xf6(\\\x8f\xc2u!@' p1034 tp1035 Rp1036 g1 (g5 S'\xf6(\\\x8f\xc2u!@' p1037 tp1038 Rp1039 F16.0 tp1040 a(I1690000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8"@' p1041 tp1042 Rp1043 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8"@' p1044 tp1045 Rp1046 F15.0 tp1047 a(I1700000 g1 (g5 S'\\\x8f\xc2\xf5(\xdc#@' p1048 tp1049 Rp1050 g1 (g5 S'\\\x8f\xc2\xf5(\xdc#@' p1051 tp1052 Rp1053 F17.0 tp1054 a(I1710000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb$@' p1055 tp1056 Rp1057 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb$@' p1058 tp1059 Rp1060 F16.0 tp1061 a(I1720000 g1 (g5 S'\xaeG\xe1z\x14.&@' p1062 tp1063 Rp1064 g1 (g5 S'\xaeG\xe1z\x14.&@' p1065 tp1066 Rp1067 F16.0 tp1068 a(I1730000 g1 (g5 S"q=\n\xd7\xa3p'@" p1069 tp1070 Rp1071 g1 (g5 S"q=\n\xd7\xa3p'@" p1072 tp1073 Rp1074 F21.0 tp1075 a(I1740000 g1 (g5 S'{\x14\xaeG\xe1z(@' p1076 tp1077 Rp1078 g1 (g5 S'{\x14\xaeG\xe1z(@' p1079 tp1080 Rp1081 F18.0 tp1082 a(I1750000 g1 (g5 S'H\xe1z\x14\xaeG)@' p1083 tp1084 Rp1085 g1 (g5 S'H\xe1z\x14\xaeG)@' p1086 tp1087 Rp1088 F17.0 tp1089 a(I1760000 g1 (g5 S'\xa4p=\n\xd7#*@' p1090 tp1091 Rp1092 g1 (g5 S'\xa4p=\n\xd7#*@' p1093 tp1094 Rp1095 F18.0 tp1096 a(I1770000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc*@' p1097 tp1098 Rp1099 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc*@' p1100 tp1101 Rp1102 F20.0 tp1103 a(I1780000 g1 (g5 S'\n\xd7\xa3p=\n,@' p1104 tp1105 Rp1106 g1 (g5 S'\n\xd7\xa3p=\n,@' p1107 tp1108 Rp1109 F17.0 tp1110 a(I1790000 g1 (g5 S'q=\n\xd7\xa3\xf0,@' p1111 tp1112 Rp1113 g1 (g5 S'q=\n\xd7\xa3\xf0,@' p1114 tp1115 Rp1116 F17.0 tp1117 a(I1800000 g1 (g5 S'\xf6(\\\x8f\xc2\xf5-@' p1118 tp1119 Rp1120 g1 (g5 S'\xf6(\\\x8f\xc2\xf5-@' p1121 tp1122 Rp1123 F17.0 tp1124 a(I1810000 g1 (g5 S'\\\x8f\xc2\xf5(\\.@' p1125 tp1126 Rp1127 g1 (g5 S'\\\x8f\xc2\xf5(\\.@' p1128 tp1129 Rp1130 F18.0 tp1131 a(I1820000 g1 (g5 S'\xa4p=\n\xd7#/@' p1132 tp1133 Rp1134 g1 (g5 S'\xa4p=\n\xd7#/@' p1135 tp1136 Rp1137 F19.0 tp1138 a(I1830000 g1 (g5 S'{\x14\xaeG\xe1z/@' p1139 tp1140 Rp1141 g1 (g5 S'{\x14\xaeG\xe1z/@' p1142 tp1143 Rp1144 F19.0 tp1145 a(I1840000 g1 (g5 S'q=\n\xd7\xa3\xf0/@' p1146 tp1147 Rp1148 g1 (g5 S'q=\n\xd7\xa3\xf0/@' p1149 tp1150 Rp1151 F14.0 tp1152 a(I1850000 g1 (g5 S'q=\n\xd7\xa300@' p1153 tp1154 Rp1155 g1 (g5 S'q=\n\xd7\xa300@' p1156 tp1157 Rp1158 F18.0 tp1159 a(I1860000 g1 (g5 S'ffffff0@' p1160 tp1161 Rp1162 g1 (g5 S'ffffff0@' p1163 tp1164 Rp1165 F18.0 tp1166 a(I1870000 g1 (g5 S'\x00\x00\x00\x00\x00\x800@' p1167 tp1168 Rp1169 g1 (g5 S'H\xe1z\x14\xae\x870@' p1170 tp1171 Rp1172 F14.0 tp1173 a(I1880000 g1 (g5 S'=\n\xd7\xa3p}0@' p1174 tp1175 Rp1176 g1172 F17.0 tp1177 a(I1890000 g1 (g5 S'\xb8\x1e\x85\xebQx0@' p1178 tp1179 Rp1180 g1172 F19.0 tp1181 a(I1900000 g1 (g5 S'\xd7\xa3p=\n\x970@' p1182 tp1183 Rp1184 g1 (g5 S'\xd7\xa3p=\n\x970@' p1185 tp1186 Rp1187 F19.0 tp1188 a(I1910000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab0@' p1189 tp1190 Rp1191 g1 (g5 S'\xecQ\xb8\x1e\x85\xab0@' p1192 tp1193 Rp1194 F15.0 tp1195 a(I1920000 g1 (g5 S'\xe1z\x14\xaeG\xa10@' p1196 tp1197 Rp1198 g1194 F16.0 tp1199 a(I1930000 g1 (g5 S'fffff\xa60@' p1200 tp1201 Rp1202 g1 (g5 S'=\n\xd7\xa3p\xbd0@' p1203 tp1204 Rp1205 F18.0 tp1206 a(I1940000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x0c0@' p1207 tp1208 Rp1209 g1205 F-9.0 tp1210 a(I1950000 g1 (g5 S'\x9a\x99\x99\x99\x99\x190@' p1211 tp1212 Rp1213 g1205 F17.0 tp1214 a(I1960000 g1 (g5 S'\\\x8f\xc2\xf5(\x1c0@' p1215 tp1216 Rp1217 g1205 F14.0 tp1218 a(I1970000 g1 (g5 S'fffff&0@' p1219 tp1220 Rp1221 g1205 F19.0 tp1222 a(I1980000 g1 (g5 S'\x8f\xc2\xf5(\\\x0f0@' p1223 tp1224 Rp1225 g1205 F17.0 tp1226 a(I1990000 g1 (g5 S'\xb8\x1e\x85\xebQ80@' p1227 tp1228 Rp1229 g1205 F19.0 tp1230 a(I2000000 g1 (g5 S'\x8f\xc2\xf5(\\O0@' p1231 tp1232 Rp1233 g1205 F18.0 tp1234 a(I2010000 g1 (g5 S')\\\x8f\xc2\xf5h0@' p1235 tp1236 Rp1237 g1205 F18.0 tp1238 a(I2020000 g1 (g5 S'33333s0@' p1239 tp1240 Rp1241 g1205 F18.0 tp1242 a(I2030000 g1 (g5 S'\\\x8f\xc2\xf5(\\0@' p1243 tp1244 Rp1245 g1205 F11.0 tp1246 a(I2040000 g1 (g5 S'\xf6(\\\x8f\xc2u0@' p1247 tp1248 Rp1249 g1205 F20.0 tp1250 a(I2050000 g1 (g5 S'\xb8\x1e\x85\xebQx0@' p1251 tp1252 Rp1253 g1205 F16.0 tp1254 a(I2060000 g1 (g5 S'\xaeG\xe1z\x14n0@' p1255 tp1256 Rp1257 g1205 F15.0 tp1258 a(I2070000 g1 (g5 S'\xa4p=\n\xd7c0@' p1259 tp1260 Rp1261 g1205 F19.0 tp1262 a(I2080000 g1 (g5 S'33333s0@' p1263 tp1264 Rp1265 g1205 F17.0 tp1266 a(I2090000 g1 (g5 S'R\xb8\x1e\x85\xebQ/@' p1267 tp1268 Rp1269 g1205 F20.0 tp1270 a(I2100000 g1 (g5 S'\x85\xebQ\xb8\x1e\x85/@' p1271 tp1272 Rp1273 g1205 F18.0 tp1274 a(I2110000 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e/@' p1275 tp1276 Rp1277 g1205 F20.0 tp1278 a(I2120000 g1 (g5 S'\xc3\xf5(\\\x8f\xc2/@' p1279 tp1280 Rp1281 g1205 F18.0 tp1282 a(I2130000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99/@' p1283 tp1284 Rp1285 g1205 F20.0 tp1286 a(I2140000 g1 (g5 S'\xaeG\xe1z\x14.0@' p1287 tp1288 Rp1289 g1205 F19.0 tp1290 a(I2150000 g1 (g5 S'\x85\xebQ\xb8\x1e\x850@' p1291 tp1292 Rp1293 g1205 F18.0 tp1294 a(I2160000 g1 (g5 S'{\x14\xaeG\xe1z0@' p1295 tp1296 Rp1297 g1205 F17.0 tp1298 a(I2170000 g1 (g5 S'H\xe1z\x14\xae\x870@' p1299 tp1300 Rp1301 g1205 F16.0 tp1302 a(I2180000 g1 (g5 S'\x14\xaeG\xe1z\x940@' p1303 tp1304 Rp1305 g1205 F18.0 tp1306 a(I2190000 g1 (g5 S'\xb8\x1e\x85\xebQx0@' p1307 tp1308 Rp1309 g1205 F14.0 tp1310 a(I2200000 g1 (g5 S'\x9a\x99\x99\x99\x99Y0@' p1311 tp1312 Rp1313 g1205 F5.0 tp1314 a(I2210000 g1 (g5 S'\x14\xaeG\xe1zT0@' p1315 tp1316 Rp1317 g1205 F20.0 tp1318 a(I2220000 g1 (g5 S'R\xb8\x1e\x85\xebQ0@' p1319 tp1320 Rp1321 g1205 F18.0 tp1322 a(I2230000 g1 (g5 S'\x1f\x85\xebQ\xb8^0@' p1323 tp1324 Rp1325 g1205 F17.0 tp1326 a(I2240000 g1 (g5 S'\xa4p=\n\xd7c0@' p1327 tp1328 Rp1329 g1205 F17.0 tp1330 a(I2250000 g1 (g5 S'\x1f\x85\xebQ\xb8^0@' p1331 tp1332 Rp1333 g1205 F19.0 tp1334 a(I2260000 g1 (g5 S'\xaeG\xe1z\x14n0@' p1335 tp1336 Rp1337 g1205 F18.0 tp1338 a(I2270000 g1 (g5 S'\n\xd7\xa3p=\x8a0@' p1339 tp1340 Rp1341 g1205 F18.0 tp1342 a(I2280000 g1 (g5 S'H\xe1z\x14\xae\x870@' p1343 tp1344 Rp1345 g1205 F17.0 tp1346 a(I2290000 g1 (g5 S'\x8f\xc2\xf5(\\\xcf0@' p1347 tp1348 Rp1349 g1 (g5 S'\x8f\xc2\xf5(\\\xcf0@' p1350 tp1351 Rp1352 F18.0 tp1353 a(I2300000 g1 (g5 S'\xe1z\x14\xaeGa1@' p1354 tp1355 Rp1356 g1 (g5 S'33333s1@' p1357 tp1358 Rp1359 F13.0 tp1360 a(I2310000 g1 (g5 S'\x8f\xc2\xf5(\\O1@' p1361 tp1362 Rp1363 g1359 F19.0 tp1364 a(I2320000 g1 (g5 S'\xcd\xcc\xcc\xcc\xccL1@' p1365 tp1366 Rp1367 g1359 F18.0 tp1368 a(I2330000 g1 (g5 S'\xd7\xa3p=\nW1@' p1369 tp1370 Rp1371 g1359 F18.0 tp1372 a(I2340000 g1 (g5 S'\x1f\x85\xebQ\xb8^1@' p1373 tp1374 Rp1375 g1359 F18.0 tp1376 a(I2350000 g1 (g5 S'\xa4p=\n\xd7c1@' p1377 tp1378 Rp1379 g1359 F18.0 tp1380 a(I2360000 g1 (g5 S'=\n\xd7\xa3p}1@' p1381 tp1382 Rp1383 g1 (g5 S'=\n\xd7\xa3p}1@' p1384 tp1385 Rp1386 F19.0 tp1387 a(I2370000 g1 (g5 S'R\xb8\x1e\x85\xeb\x911@' p1388 tp1389 Rp1390 g1 (g5 S'R\xb8\x1e\x85\xeb\x911@' p1391 tp1392 Rp1393 F20.0 tp1394 a(I2380000 g1 (g5 S'q=\n\xd7\xa3\xb01@' p1395 tp1396 Rp1397 g1 (g5 S'q=\n\xd7\xa3\xb01@' p1398 tp1399 Rp1400 F18.0 tp1401 a(I2390000 g1 (g5 S'\x00\x00\x00\x00\x00\xc01@' p1402 tp1403 Rp1404 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc1@' p1405 tp1406 Rp1407 F16.0 tp1408 a(I2400000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc51@' p1409 tp1410 Rp1411 g1407 F18.0 tp1412 a(I2410000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb1@' p1413 tp1414 Rp1415 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb1@' p1416 tp1417 Rp1418 F19.0 tp1419 a(I2420000 g1 (g5 S'R\xb8\x1e\x85\xeb\xd11@' p1420 tp1421 Rp1422 g1418 F15.0 tp1423 a(I2430000 g1 (g5 S'fffff\xe61@' p1424 tp1425 Rp1426 g1418 F21.0 tp1427 a(I2440000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb1@' p1428 tp1429 Rp1430 g1 (g5 S'q=\n\xd7\xa3\xf01@' p1431 tp1432 Rp1433 F17.0 tp1434 a(I2450000 g1 (g5 S'\x1f\x85\xebQ\xb8\xde1@' p1435 tp1436 Rp1437 g1433 F20.0 tp1438 a(I2460000 g1 (g5 S'\\\x8f\xc2\xf5(\xdc1@' p1439 tp1440 Rp1441 g1433 F18.0 tp1442 a(I2470000 g1 (g5 S')\\\x8f\xc2\xf5\xe81@' p1443 tp1444 Rp1445 g1433 F18.0 tp1446 a(I2480000 g1 (g5 S'{\x14\xaeG\xe1\xfa1@' p1447 tp1448 Rp1449 g1 (g5 S'{\x14\xaeG\xe1\xfa1@' p1450 tp1451 Rp1452 F20.0 tp1453 a(I2490000 g1 (g5 S'\xb8\x1e\x85\xebQ\xf81@' p1454 tp1455 Rp1456 g1 (g5 S'\x00\x00\x00\x00\x00\x002@' p1457 tp1458 Rp1459 F18.0 tp1460 a(I2500000 g1 (g5 S'33333\xf31@' p1461 tp1462 Rp1463 g1459 F18.0 tp1464 a(I2510000 g1 (g5 S'\x8f\xc2\xf5(\\\x0f2@' p1465 tp1466 Rp1467 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e2@' p1468 tp1469 Rp1470 F17.0 tp1471 a(I2520000 g1 (g5 S'H\xe1z\x14\xae\x072@' p1472 tp1473 Rp1474 g1470 F20.0 tp1475 a(I2530000 g1 (g5 S'R\xb8\x1e\x85\xeb\x112@' p1476 tp1477 Rp1478 g1470 F20.0 tp1479 a(I2540000 g1 (g5 S'\xf6(\\\x8f\xc2\xb51@' p1480 tp1481 Rp1482 g1470 F19.0 tp1483 a(I2550000 g1 (g5 S'\\\x8f\xc2\xf5(\\1@' p1484 tp1485 Rp1486 g1470 F-18.0 tp1487 a(I2560000 g1 (g5 S'\x9a\x99\x99\x99\x99\x191@' p1488 tp1489 Rp1490 g1470 F20.0 tp1491 a(I2570000 g1 (g5 S'\x8f\xc2\xf5(\\\x0f1@' p1492 tp1493 Rp1494 g1470 F19.0 tp1495 a(I2580000 g1 (g5 S'33333\xf30@' p1496 tp1497 Rp1498 g1470 F16.0 tp1499 a(I2590000 g1 (g5 S'\xb8\x1e\x85\xebQ\xf80@' p1500 tp1501 Rp1502 g1470 F19.0 tp1503 a(I2600000 g1 (g5 S'H\xe1z\x14\xae\x071@' p1504 tp1505 Rp1506 g1470 F14.0 tp1507 a(I2610000 g1 (g5 S'R\xb8\x1e\x85\xeb\x111@' p1508 tp1509 Rp1510 g1470 F19.0 tp1511 a(I2620000 g1 (g5 S'q=\n\xd7\xa301@' p1512 tp1513 Rp1514 g1470 F20.0 tp1515 a(I2630000 g1 (g5 S'fffff&1@' p1516 tp1517 Rp1518 g1470 F18.0 tp1519 a(I2640000 g1 (g5 S')\\\x8f\xc2\xf5(1@' p1520 tp1521 Rp1522 g1470 F17.0 tp1523 a(I2650000 g1 (g5 S'\xa4p=\n\xd7#1@' p1524 tp1525 Rp1526 g1470 F17.0 tp1527 a(I2660000 g1 (g5 S'\xa4p=\n\xd7#1@' p1528 tp1529 Rp1530 g1470 F19.0 tp1531 a(I2670000 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e1@' p1532 tp1533 Rp1534 g1470 F21.0 tp1535 a(I2680000 g1 (g5 S'H\xe1z\x14\xae\x071@' p1536 tp1537 Rp1538 g1470 F21.0 tp1539 a(I2690000 g1 (g5 S'\n\xd7\xa3p=\n1@' p1540 tp1541 Rp1542 g1470 F20.0 tp1543 a(I2700000 g1 (g5 S'\xe1z\x14\xaeG!1@' p1544 tp1545 Rp1546 g1470 F19.0 tp1547 a(I2710000 g1 (g5 S'\xaeG\xe1z\x14.1@' p1548 tp1549 Rp1550 g1470 F18.0 tp1551 a(I2720000 g1 (g5 S'3333331@' p1552 tp1553 Rp1554 g1470 F20.0 tp1555 a(I2730000 g1 (g5 S'R\xb8\x1e\x85\xebQ1@' p1556 tp1557 Rp1558 g1470 F20.0 tp1559 a(I2740000 g1 (g5 S'\xf6(\\\x8f\xc2\xb51@' p1560 tp1561 Rp1562 g1470 F18.0 tp1563 a(I2750000 g1 (g5 S'\x14\xaeG\xe1z\x142@' p1564 tp1565 Rp1566 g1470 F18.0 tp1567 a(I2760000 g1 (g5 S'\x9a\x99\x99\x99\x99Y2@' p1568 tp1569 Rp1570 g1 (g5 S'\xe1z\x14\xaeGa2@' p1571 tp1572 Rp1573 F19.0 tp1574 a(I2770000 g1 (g5 S'\xecQ\xb8\x1e\x85k2@' p1575 tp1576 Rp1577 g1 (g5 S'\xecQ\xb8\x1e\x85k2@' p1578 tp1579 Rp1580 F20.0 tp1581 a(I2780000 g1 (g5 S'\xecQ\xb8\x1e\x85k2@' p1582 tp1583 Rp1584 g1 (g5 S'\xaeG\xe1z\x14n2@' p1585 tp1586 Rp1587 F17.0 tp1588 a(I2790000 g1 (g5 S'\xe1z\x14\xaeGa2@' p1589 tp1590 Rp1591 g1587 F19.0 tp1592 a(I2800000 g1 (g5 S'ffffff2@' p1593 tp1594 Rp1595 g1 (g5 S'q=\n\xd7\xa3p2@' p1596 tp1597 Rp1598 F17.0 tp1599 a(I2810000 g1 (g5 S'33333s2@' p1600 tp1601 Rp1602 g1 (g5 S'\xb8\x1e\x85\xebQx2@' p1603 tp1604 Rp1605 F18.0 tp1606 a(I2820000 g1 (g5 S'33333s2@' p1607 tp1608 Rp1609 g1605 F19.0 tp1610 a(I2830000 g1 (g5 S'{\x14\xaeG\xe1z2@' p1611 tp1612 Rp1613 g1 (g5 S'{\x14\xaeG\xe1z2@' p1614 tp1615 Rp1616 F20.0 tp1617 a(I2840000 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e2@' p1618 tp1619 Rp1620 g1 (g5 S'\xe1z\x14\xaeG\xa12@' p1621 tp1622 Rp1623 F18.0 tp1624 a(I2850000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab2@' p1625 tp1626 Rp1627 g1 (g5 S'q=\n\xd7\xa3\xb02@' p1628 tp1629 Rp1630 F17.0 tp1631 a(I2860000 g1 (g5 S'\xa4p=\n\xd7\xa32@' p1632 tp1633 Rp1634 g1630 F19.0 tp1635 a(I2870000 g1 (g5 S'fffff\xa62@' p1636 tp1637 Rp1638 g1630 F15.0 tp1639 a(I2880000 g1 (g5 S'33333\xb32@' p1640 tp1641 Rp1642 g1 (g5 S'{\x14\xaeG\xe1\xba2@' p1643 tp1644 Rp1645 F16.0 tp1646 a(I2890000 g1 (g5 S'{\x14\xaeG\xe1\xba2@' p1647 tp1648 Rp1649 g1645 F19.0 tp1650 a(I2900000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb82@' p1651 tp1652 Rp1653 g1645 F19.0 tp1654 a(I2910000 g1 (g5 S'=\n\xd7\xa3p\xbd2@' p1655 tp1656 Rp1657 g1 (g5 S'\x00\x00\x00\x00\x00\xc02@' p1658 tp1659 Rp1660 F18.0 tp1661 a(I2920000 g1 (g5 S'fffff\xa62@' p1662 tp1663 Rp1664 g1660 F21.0 tp1665 a(I2930000 g1 (g5 S'\x9a\x99\x99\x99\x99\x992@' p1666 tp1667 Rp1668 g1660 F19.0 tp1669 a(I2940000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab2@' p1670 tp1671 Rp1672 g1660 F19.0 tp1673 a(I2950000 g1 (g5 S'\xe1z\x14\xaeG\xa12@' p1674 tp1675 Rp1676 g1660 F18.0 tp1677 a(I2960000 g1 (g5 S'33333\xb32@' p1678 tp1679 Rp1680 g1660 F20.0 tp1681 a(I2970000 g1 (g5 S'\xf6(\\\x8f\xc2\xb52@' p1682 tp1683 Rp1684 g1660 F21.0 tp1685 a(I2980000 g1 (g5 S'=\n\xd7\xa3p\xbd2@' p1686 tp1687 Rp1688 g1660 F18.0 tp1689 a(I2990000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc52@' p1690 tp1691 Rp1692 g1 (g5 S'H\xe1z\x14\xae\xc72@' p1693 tp1694 Rp1695 F20.0 tp1696 a(I3000000 g1 (g5 S'{\x14\xaeG\xe1\xba2@' p1697 tp1698 Rp1699 g1 (g5 S'\x8f\xc2\xf5(\\\xcf2@' p1700 tp1701 Rp1702 F19.0 tp1703 a(I3010000 g1 (g5 S'\xf6(\\\x8f\xc2\xb52@' p1704 tp1705 Rp1706 g1702 F21.0 tp1707 a(I3020000 g1 (g5 S'=\n\xd7\xa3p\xbd2@' p1708 tp1709 Rp1710 g1702 F19.0 tp1711 a(I3030000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab2@' p1712 tp1713 Rp1714 g1702 F19.0 tp1715 a(I3040000 g1 (g5 S'\x00\x00\x00\x00\x00\xc02@' p1716 tp1717 Rp1718 g1702 F20.0 tp1719 a(I3050000 g1 (g5 S'=\n\xd7\xa3p\xbd2@' p1720 tp1721 Rp1722 g1702 F16.0 tp1723 a(I3060000 g1 (g5 S'\xc3\xf5(\\\x8f\xc22@' p1724 tp1725 Rp1726 g1702 F20.0 tp1727 a(I3070000 g1 (g5 S'\xa4p=\n\xd7\xe32@' p1728 tp1729 Rp1730 g1 (g5 S'\xa4p=\n\xd7\xe32@' p1731 tp1732 Rp1733 F21.0 tp1734 a(I3080000 g1 (g5 S'\xf6(\\\x8f\xc2\xf52@' p1735 tp1736 Rp1737 g1 (g5 S'\xf6(\\\x8f\xc2\xf52@' p1738 tp1739 Rp1740 F20.0 tp1741 a(I3090000 g1 (g5 S'\xb8\x1e\x85\xebQ\xf82@' p1742 tp1743 Rp1744 g1 (g5 S'\x00\x00\x00\x00\x00\x003@' p1745 tp1746 Rp1747 F20.0 tp1748 a(I3100000 g1 (g5 S'\x00\x00\x00\x00\x00\x003@' p1749 tp1750 Rp1751 g1747 F18.0 tp1752 a(I3110000 g1 (g5 S'R\xb8\x1e\x85\xeb\x113@' p1753 tp1754 Rp1755 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e3@' p1756 tp1757 Rp1758 F21.0 tp1759 a(I3120000 g1 (g5 S'\xf6(\\\x8f\xc2\xf52@' p1760 tp1761 Rp1762 g1758 F4.0 tp1763 a(I3130000 g1 (g5 S'=\n\xd7\xa3p\xfd2@' p1764 tp1765 Rp1766 g1758 F17.0 tp1767 a(I3140000 g1 (g5 S'{\x14\xaeG\xe1\xfa2@' p1768 tp1769 Rp1770 g1758 F18.0 tp1771 a(I3150000 g1 (g5 S'=\n\xd7\xa3p\xfd2@' p1772 tp1773 Rp1774 g1758 F19.0 tp1775 a(I3160000 g1 (g5 S'R\xb8\x1e\x85\xeb\x113@' p1776 tp1777 Rp1778 g1758 F20.0 tp1779 a(I3170000 g1 (g5 S'\\\x8f\xc2\xf5(\x1c3@' p1780 tp1781 Rp1782 g1758 F20.0 tp1783 a(I3180000 g1 (g5 S'\\\x8f\xc2\xf5(\x1c3@' p1784 tp1785 Rp1786 g1 (g5 S'fffff&3@' p1787 tp1788 Rp1789 F18.0 tp1790 a(I3190000 g1 (g5 S'q=\n\xd7\xa303@' p1791 tp1792 Rp1793 g1 (g5 S'q=\n\xd7\xa303@' p1794 tp1795 Rp1796 F20.0 tp1797 a(I3200000 g1 (g5 S'q=\n\xd7\xa303@' p1798 tp1799 Rp1800 g1 (g5 S'=\n\xd7\xa3p=3@' p1801 tp1802 Rp1803 F18.0 tp1804 a(I3210000 g1 (g5 S'fffff&3@' p1805 tp1806 Rp1807 g1803 F19.0 tp1808 a(I3220000 g1 (g5 S'q=\n\xd7\xa303@' p1809 tp1810 Rp1811 g1803 F18.0 tp1812 a(I3230000 g1 (g5 S'q=\n\xd7\xa303@' p1813 tp1814 Rp1815 g1803 F21.0 tp1816 a(I3240000 g1 (g5 S'\xcd\xcc\xcc\xcc\xccL3@' p1817 tp1818 Rp1819 g1 (g5 S'\xcd\xcc\xcc\xcc\xccL3@' p1820 tp1821 Rp1822 F20.0 tp1823 a(I3250000 g1 (g5 S'\x8f\xc2\xf5(\\O3@' p1824 tp1825 Rp1826 g1 (g5 S'\\\x8f\xc2\xf5(\\3@' p1827 tp1828 Rp1829 F18.0 tp1830 a(I3260000 g1 (g5 S'\n\xd7\xa3p=J3@' p1831 tp1832 Rp1833 g1829 F20.0 tp1834 a(I3270000 g1 (g5 S'\n\xd7\xa3p=J3@' p1835 tp1836 Rp1837 g1829 F21.0 tp1838 a(I3280000 g1 (g5 S'\x85\xebQ\xb8\x1eE3@' p1839 tp1840 Rp1841 g1829 F19.0 tp1842 a(I3290000 g1 (g5 S'\xe1z\x14\xaeGa3@' p1843 tp1844 Rp1845 g1 (g5 S'\xe1z\x14\xaeGa3@' p1846 tp1847 Rp1848 F20.0 tp1849 a(I3300000 g1 (g5 S'\x9a\x99\x99\x99\x99Y3@' p1850 tp1851 Rp1852 g1848 F18.0 tp1853 a(I3310000 g1 (g5 S'\xc3\xf5(\\\x8f\x823@' p1854 tp1855 Rp1856 g1 (g5 S'H\xe1z\x14\xae\x873@' p1857 tp1858 Rp1859 F20.0 tp1860 a(I3320000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x8c3@' p1861 tp1862 Rp1863 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x8c3@' p1864 tp1865 Rp1866 F21.0 tp1867 a(I3330000 g1 (g5 S'\x9a\x99\x99\x99\x99\x993@' p1868 tp1869 Rp1870 g1 (g5 S'\xa4p=\n\xd7\xa33@' p1871 tp1872 Rp1873 F21.0 tp1874 a(I3340000 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e3@' p1875 tp1876 Rp1877 g1873 F21.0 tp1878 a(I3350000 g1 (g5 S'\x9a\x99\x99\x99\x99\x993@' p1879 tp1880 Rp1881 g1873 F20.0 tp1882 a(I3360000 g1 (g5 S'R\xb8\x1e\x85\xeb\x913@' p1883 tp1884 Rp1885 g1873 F19.0 tp1886 a(I3370000 g1 (g5 S'\\\x8f\xc2\xf5(\x9c3@' p1887 tp1888 Rp1889 g1873 F21.0 tp1890 a(I3380000 g1 (g5 S'\\\x8f\xc2\xf5(\x9c3@' p1891 tp1892 Rp1893 g1873 F20.0 tp1894 a(I3390000 g1 (g5 S'\xc3\xf5(\\\x8f\xc23@' p1895 tp1896 Rp1897 g1 (g5 S'\xc3\xf5(\\\x8f\xc23@' p1898 tp1899 Rp1900 F21.0 tp1901 a(I3400000 g1 (g5 S'\xc3\xf5(\\\x8f\xc23@' p1902 tp1903 Rp1904 g1 (g5 S'\x85\xebQ\xb8\x1e\xc53@' p1905 tp1906 Rp1907 F19.0 tp1908 a(I3410000 g1 (g5 S'H\xe1z\x14\xae\xc73@' p1909 tp1910 Rp1911 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc3@' p1912 tp1913 Rp1914 F19.0 tp1915 a(I3420000 g1 (g5 S'\xc3\xf5(\\\x8f\xc23@' p1916 tp1917 Rp1918 g1914 F21.0 tp1919 a(I3430000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb83@' p1920 tp1921 Rp1922 g1914 F15.0 tp1923 a(I3440000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc53@' p1924 tp1925 Rp1926 g1914 F19.0 tp1927 a(I3450000 g1 (g5 S'\x00\x00\x00\x00\x00\xc03@' p1928 tp1929 Rp1930 g1914 F21.0 tp1931 a(I3460000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x8c3@' p1932 tp1933 Rp1934 g1914 F15.0 tp1935 a(I3470000 g1 (g5 S'\x9a\x99\x99\x99\x99\x993@' p1936 tp1937 Rp1938 g1914 F21.0 tp1939 a(I3480000 g1 (g5 S'R\xb8\x1e\x85\xeb\x913@' p1940 tp1941 Rp1942 g1914 F21.0 tp1943 a(I3490000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f3@' p1944 tp1945 Rp1946 g1914 F21.0 tp1947 a(I3500000 g1 (g5 S'\xd7\xa3p=\n\x973@' p1948 tp1949 Rp1950 g1914 F21.0 tp1951 a(I3510000 g1 (g5 S'\xa4p=\n\xd7\xa33@' p1952 tp1953 Rp1954 g1914 F19.0 tp1955 a(I3520000 g1 (g5 S'\\\x8f\xc2\xf5(\x9c3@' p1956 tp1957 Rp1958 g1914 F18.0 tp1959 a(I3530000 g1 (g5 S'fffff\xa63@' p1960 tp1961 Rp1962 g1914 F20.0 tp1963 a(I3540000 g1 (g5 S'\xe1z\x14\xaeG\xa13@' p1964 tp1965 Rp1966 g1914 F21.0 tp1967 a(I3550000 g1 (g5 S')\\\x8f\xc2\xf5\xa83@' p1968 tp1969 Rp1970 g1914 F21.0 tp1971 a(I3560000 g1 (g5 S'{\x14\xaeG\xe1\xba3@' p1972 tp1973 Rp1974 g1914 F21.0 tp1975 a(I3570000 g1 (g5 S'\x00\x00\x00\x00\x00\xc03@' p1976 tp1977 Rp1978 g1914 F20.0 tp1979 a(I3580000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc53@' p1980 tp1981 Rp1982 g1914 F21.0 tp1983 a(I3590000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc3@' p1984 tp1985 Rp1986 g1914 F20.0 tp1987 a(I3600000 g1 (g5 S'\xd7\xa3p=\n\xd73@' p1988 tp1989 Rp1990 g1 (g5 S'\xd7\xa3p=\n\xd73@' p1991 tp1992 Rp1993 F21.0 tp1994 a(I3610000 g1 (g5 S'\x1f\x85\xebQ\xb8\xde3@' p1995 tp1996 Rp1997 g1 (g5 S'\x1f\x85\xebQ\xb8\xde3@' p1998 tp1999 Rp2000 F17.0 tp2001 a(I3620000 g1 (g5 S'\xf6(\\\x8f\xc2\xf53@' p2002 tp2003 Rp2004 g1 (g5 S'\xf6(\\\x8f\xc2\xf53@' p2005 tp2006 Rp2007 F20.0 tp2008 a(I3630000 g1 (g5 S'=\n\xd7\xa3p\xfd3@' p2009 tp2010 Rp2011 g1 (g5 S'=\n\xd7\xa3p\xfd3@' p2012 tp2013 Rp2014 F21.0 tp2015 a(I3640000 g1 (g5 S'=\n\xd7\xa3p=4@' p2016 tp2017 Rp2018 g1 (g5 S'=\n\xd7\xa3p=4@' p2019 tp2020 Rp2021 F21.0 tp2022 a(I3650000 g1 (g5 S'\x00\x00\x00\x00\x00@4@' p2023 tp2024 Rp2025 g1 (g5 S'\x00\x00\x00\x00\x00@4@' p2026 tp2027 Rp2028 F21.0 tp2029 a(I3660000 g1 (g5 S'=\n\xd7\xa3p=4@' p2030 tp2031 Rp2032 g1 (g5 S'\x85\xebQ\xb8\x1eE4@' p2033 tp2034 Rp2035 F20.0 tp2036 a(I3670000 g1 (g5 S'\xc3\xf5(\\\x8fB4@' p2037 tp2038 Rp2039 g2035 F21.0 tp2040 a(I3680000 g1 (g5 S'H\xe1z\x14\xaeG4@' p2041 tp2042 Rp2043 g1 (g5 S'H\xe1z\x14\xaeG4@' p2044 tp2045 Rp2046 F21.0 tp2047 a(I3690000 g1 (g5 S'H\xe1z\x14\xaeG4@' p2048 tp2049 Rp2050 g2046 F21.0 tp2051 a(I3700000 g1 (g5 S'\x9a\x99\x99\x99\x99Y4@' p2052 tp2053 Rp2054 g1 (g5 S'\x9a\x99\x99\x99\x99Y4@' p2055 tp2056 Rp2057 F21.0 tp2058 a(I3710000 g1 (g5 S'\xe1z\x14\xaeGa4@' p2059 tp2060 Rp2061 g1 (g5 S'\xe1z\x14\xaeGa4@' p2062 tp2063 Rp2064 F21.0 tp2065 a(I3720000 g1 (g5 S'q=\n\xd7\xa3p4@' p2066 tp2067 Rp2068 g1 (g5 S'q=\n\xd7\xa3p4@' p2069 tp2070 Rp2071 F21.0 tp2072 a(I3730000 g1 (g5 S'\xe1z\x14\xaeGa4@' p2073 tp2074 Rp2075 g2071 F18.0 tp2076 a(I3740000 g1 (g5 S'\x14\xaeG\xe1zT4@' p2077 tp2078 Rp2079 g2071 F21.0 tp2080 a(I3750000 g1 (g5 S'\x1f\x85\xebQ\xb8^4@' p2081 tp2082 Rp2083 g2071 F21.0 tp2084 a(I3760000 g1 (g5 S'\xe1z\x14\xaeGa4@' p2085 tp2086 Rp2087 g2071 F20.0 tp2088 a(I3770000 g1 (g5 S')\\\x8f\xc2\xf5h4@' p2089 tp2090 Rp2091 g2071 F21.0 tp2092 a(I3780000 g1 (g5 S'\xa4p=\n\xd7c4@' p2093 tp2094 Rp2095 g2071 F21.0 tp2096 a(I3790000 g1 (g5 S'\xecQ\xb8\x1e\x85k4@' p2097 tp2098 Rp2099 g2071 F21.0 tp2100 a(I3800000 g1 (g5 S'\xecQ\xb8\x1e\x85k4@' p2101 tp2102 Rp2103 g2071 F21.0 tp2104 a(I3810000 g1 (g5 S'ffffff4@' p2105 tp2106 Rp2107 g2071 F20.0 tp2108 a(I3820000 g1 (g5 S'\xecQ\xb8\x1e\x85k4@' p2109 tp2110 Rp2111 g2071 F21.0 tp2112 a(I3830000 g1 (g5 S'q=\n\xd7\xa3p4@' p2113 tp2114 Rp2115 g2071 F19.0 tp2116 a(I3840000 g1 (g5 S'\xb8\x1e\x85\xebQx4@' p2117 tp2118 Rp2119 g1 (g5 S'{\x14\xaeG\xe1z4@' p2120 tp2121 Rp2122 F21.0 tp2123 a(I3850000 g1 (g5 S'33333s4@' p2124 tp2125 Rp2126 g2122 F21.0 tp2127 a(I3860000 g1 (g5 S'\xb8\x1e\x85\xebQx4@' p2128 tp2129 Rp2130 g2122 F20.0 tp2131 a(I3870000 g1 (g5 S'q=\n\xd7\xa3p4@' p2132 tp2133 Rp2134 g2122 F19.0 tp2135 a(I3880000 g1 (g5 S'\xf6(\\\x8f\xc2u4@' p2136 tp2137 Rp2138 g2122 F21.0 tp2139 a(I3890000 g1 (g5 S'\xaeG\xe1z\x14n4@' p2140 tp2141 Rp2142 g2122 F20.0 tp2143 a(I3900000 g1 (g5 S'33333s4@' p2144 tp2145 Rp2146 g2122 F21.0 tp2147 a(I3910000 g1 (g5 S'\x85\xebQ\xb8\x1e\x854@' p2148 tp2149 Rp2150 g1 (g5 S'\x85\xebQ\xb8\x1e\x854@' p2151 tp2152 Rp2153 F21.0 tp2154 a(I3920000 g1 (g5 S'\x00\x00\x00\x00\x00\x804@' p2155 tp2156 Rp2157 g2153 F19.0 tp2158 a(I3930000 g1 (g5 S')\\\x8f\xc2\xf5h4@' p2159 tp2160 Rp2161 g2153 F19.0 tp2162 a(I3940000 g1 (g5 S')\\\x8f\xc2\xf5h4@' p2163 tp2164 Rp2165 g2153 F20.0 tp2166 a(I3950000 g1 (g5 S')\\\x8f\xc2\xf5h4@' p2167 tp2168 Rp2169 g2153 F21.0 tp2170 a(I3960000 g1 (g5 S'q=\n\xd7\xa3p4@' p2171 tp2172 Rp2173 g2153 F21.0 tp2174 a(I3970000 g1 (g5 S'ffffff4@' p2175 tp2176 Rp2177 g2153 F19.0 tp2178 a(I3980000 g1 (g5 S'\xa4p=\n\xd7c4@' p2179 tp2180 Rp2181 g2153 F21.0 tp2182 a(I3990000 g1 (g5 S'\xaeG\xe1z\x14n4@' p2183 tp2184 Rp2185 g2153 F21.0 tp2186 a(I4000000 g1 (g5 S'\xe1z\x14\xaeGa4@' p2187 tp2188 Rp2189 g2153 F20.0 tp2190 a(I4010000 g1 (g5 S'\xa4p=\n\xd7c4@' p2191 tp2192 Rp2193 g2153 F20.0 tp2194 a(I4020000 g1 (g5 S'\xa4p=\n\xd7c4@' p2195 tp2196 Rp2197 g2153 F21.0 tp2198 a(I4030000 g1 (g5 S'\xa4p=\n\xd7c4@' p2199 tp2200 Rp2201 g2153 F21.0 tp2202 a(I4040000 g1 (g5 S'ffffff4@' p2203 tp2204 Rp2205 g2153 F20.0 tp2206 a(I4050000 g1 (g5 S'\xaeG\xe1z\x14n4@' p2207 tp2208 Rp2209 g2153 F21.0 tp2210 a(I4060000 g1 (g5 S'\xaeG\xe1z\x14n4@' p2211 tp2212 Rp2213 g2153 F21.0 tp2214 a(I4070000 g1 (g5 S')\\\x8f\xc2\xf5h4@' p2215 tp2216 Rp2217 g2153 F21.0 tp2218 a(I4080000 g1 (g5 S'ffffff4@' p2219 tp2220 Rp2221 g2153 F20.0 tp2222 a(I4090000 g1 (g5 S'\xa4p=\n\xd7c4@' p2223 tp2224 Rp2225 g2153 F21.0 tp2226 a(I4100000 g1 (g5 S'33333s4@' p2227 tp2228 Rp2229 g2153 F20.0 tp2230 a(I4110000 g1 (g5 S'\xb8\x1e\x85\xebQx4@' p2231 tp2232 Rp2233 g2153 F21.0 tp2234 a(I4120000 g1 (g5 S'{\x14\xaeG\xe1z4@' p2235 tp2236 Rp2237 g2153 F21.0 tp2238 a(I4130000 g1 (g5 S'\xf6(\\\x8f\xc2u4@' p2239 tp2240 Rp2241 g2153 F20.0 tp2242 a(I4140000 g1 (g5 S'33333s4@' p2243 tp2244 Rp2245 g2153 F21.0 tp2246 a(I4150000 g1 (g5 S'{\x14\xaeG\xe1z4@' p2247 tp2248 Rp2249 g2153 F21.0 tp2250 a(I4160000 g1 (g5 S'\xb8\x1e\x85\xebQx4@' p2251 tp2252 Rp2253 g2153 F21.0 tp2254 a(I4170000 g1 (g5 S'\x85\xebQ\xb8\x1e\x854@' p2255 tp2256 Rp2257 g2153 F21.0 tp2258 a(I4180000 g1 (g5 S'\n\xd7\xa3p=\x8a4@' p2259 tp2260 Rp2261 g1 (g5 S'\n\xd7\xa3p=\x8a4@' p2262 tp2263 Rp2264 F21.0 tp2265 a(I4190000 g1 (g5 S'\x14\xaeG\xe1z\x944@' p2266 tp2267 Rp2268 g1 (g5 S'\x14\xaeG\xe1z\x944@' p2269 tp2270 Rp2271 F21.0 tp2272 a(I4200000 g1 (g5 S'\x14\xaeG\xe1z\x944@' p2273 tp2274 Rp2275 g2271 F21.0 tp2276 a(I4210000 g1 (g5 S'\xd7\xa3p=\n\x974@' p2277 tp2278 Rp2279 g1 (g5 S'\xd7\xa3p=\n\x974@' p2280 tp2281 Rp2282 F21.0 tp2283 a(I4220000 g1 (g5 S'\xd7\xa3p=\n\x974@' p2284 tp2285 Rp2286 g1 (g5 S'\x9a\x99\x99\x99\x99\x994@' p2287 tp2288 Rp2289 F20.0 tp2290 a(I4230000 g1 (g5 S'\\\x8f\xc2\xf5(\x9c4@' p2291 tp2292 Rp2293 g1 (g5 S'\\\x8f\xc2\xf5(\x9c4@' p2294 tp2295 Rp2296 F21.0 tp2297 a(I4240000 g1 (g5 S'\xa4p=\n\xd7\xa34@' p2298 tp2299 Rp2300 g1 (g5 S'\xa4p=\n\xd7\xa34@' p2301 tp2302 Rp2303 F21.0 tp2304 a(I4250000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab4@' p2305 tp2306 Rp2307 g1 (g5 S'\xecQ\xb8\x1e\x85\xab4@' p2308 tp2309 Rp2310 F21.0 tp2311 a(I4260000 g1 (g5 S'\xaeG\xe1z\x14\xae4@' p2312 tp2313 Rp2314 g1 (g5 S'q=\n\xd7\xa3\xb04@' p2315 tp2316 Rp2317 F21.0 tp2318 a(I4270000 g1 (g5 S'\xf6(\\\x8f\xc2\xb54@' p2319 tp2320 Rp2321 g1 (g5 S'\xf6(\\\x8f\xc2\xb54@' p2322 tp2323 Rp2324 F21.0 tp2325 a(I4280000 g1 (g5 S'33333\xb34@' p2326 tp2327 Rp2328 g2324 F21.0 tp2329 a(I4290000 g1 (g5 S'\xf6(\\\x8f\xc2\xb54@' p2330 tp2331 Rp2332 g2324 F21.0 tp2333 a(I4300000 g1 (g5 S'=\n\xd7\xa3p\xbd4@' p2334 tp2335 Rp2336 g1 (g5 S'=\n\xd7\xa3p\xbd4@' p2337 tp2338 Rp2339 F21.0 tp2340 a(I4310000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p2341 tp2342 Rp2343 g1 (g5 S'\xc3\xf5(\\\x8f\xc24@' p2344 tp2345 Rp2346 F20.0 tp2347 a(I4320000 g1 (g5 S'=\n\xd7\xa3p\xbd4@' p2348 tp2349 Rp2350 g2346 F20.0 tp2351 a(I4330000 g1 (g5 S'\xc3\xf5(\\\x8f\xc24@' p2352 tp2353 Rp2354 g1 (g5 S'\x85\xebQ\xb8\x1e\xc54@' p2355 tp2356 Rp2357 F20.0 tp2358 a(I4340000 g1 (g5 S'\x00\x00\x00\x00\x00\xc04@' p2359 tp2360 Rp2361 g2357 F21.0 tp2362 a(I4350000 g1 (g5 S'=\n\xd7\xa3p\xbd4@' p2363 tp2364 Rp2365 g2357 F21.0 tp2366 a(I4360000 g1 (g5 S'\x00\x00\x00\x00\x00\xc04@' p2367 tp2368 Rp2369 g2357 F21.0 tp2370 a(I4370000 g1 (g5 S'=\n\xd7\xa3p\xbd4@' p2371 tp2372 Rp2373 g2357 F20.0 tp2374 a(I4380000 g1 (g5 S'\x00\x00\x00\x00\x00\xc04@' p2375 tp2376 Rp2377 g2357 F21.0 tp2378 a(I4390000 g1 (g5 S'\x00\x00\x00\x00\x00\xc04@' p2379 tp2380 Rp2381 g2357 F21.0 tp2382 a(I4400000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p2383 tp2384 Rp2385 g2357 F21.0 tp2386 a(I4410000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p2387 tp2388 Rp2389 g2357 F21.0 tp2390 a(I4420000 g1 (g5 S'33333\xb34@' p2391 tp2392 Rp2393 g2357 F21.0 tp2394 a(I4430000 g1 (g5 S'\xaeG\xe1z\x14\xae4@' p2395 tp2396 Rp2397 g2357 F21.0 tp2398 a(I4440000 g1 (g5 S'\xaeG\xe1z\x14\xae4@' p2399 tp2400 Rp2401 g2357 F21.0 tp2402 a(I4450000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab4@' p2403 tp2404 Rp2405 g2357 F21.0 tp2406 a(I4460000 g1 (g5 S')\\\x8f\xc2\xf5\xa84@' p2407 tp2408 Rp2409 g2357 F20.0 tp2410 a(I4470000 g1 (g5 S'\xa4p=\n\xd7\xa34@' p2411 tp2412 Rp2413 g2357 F21.0 tp2414 a(I4480000 g1 (g5 S'\xaeG\xe1z\x14\xae4@' p2415 tp2416 Rp2417 g2357 F21.0 tp2418 a(I4490000 g1 (g5 S'fffff\xa64@' p2419 tp2420 Rp2421 g2357 F20.0 tp2422 a(I4500000 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e4@' p2423 tp2424 Rp2425 g2357 F21.0 tp2426 a(I4510000 g1 (g5 S'fffff\xa64@' p2427 tp2428 Rp2429 g2357 F21.0 tp2430 a(I4520000 g1 (g5 S'fffff\xa64@' p2431 tp2432 Rp2433 g2357 F21.0 tp2434 a(I4530000 g1 (g5 S'\xe1z\x14\xaeG\xa14@' p2435 tp2436 Rp2437 g2357 F21.0 tp2438 a(I4540000 g1 (g5 S'fffff\xa64@' p2439 tp2440 Rp2441 g2357 F21.0 tp2442 a(I4550000 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e4@' p2443 tp2444 Rp2445 g2357 F21.0 tp2446 a(I4560000 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e4@' p2447 tp2448 Rp2449 g2357 F21.0 tp2450 a(I4570000 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e4@' p2451 tp2452 Rp2453 g2357 F20.0 tp2454 a(I4580000 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e4@' p2455 tp2456 Rp2457 g2357 F20.0 tp2458 a(I4590000 g1 (g5 S')\\\x8f\xc2\xf5\xa84@' p2459 tp2460 Rp2461 g2357 F21.0 tp2462 a(I4600000 g1 (g5 S'\xe1z\x14\xaeG\xa14@' p2463 tp2464 Rp2465 g2357 F21.0 tp2466 a(I4610000 g1 (g5 S'\\\x8f\xc2\xf5(\x9c4@' p2467 tp2468 Rp2469 g2357 F21.0 tp2470 a(I4620000 g1 (g5 S'\\\x8f\xc2\xf5(\x9c4@' p2471 tp2472 Rp2473 g2357 F21.0 tp2474 a(I4630000 g1 (g5 S'\\\x8f\xc2\xf5(\x9c4@' p2475 tp2476 Rp2477 g2357 F21.0 tp2478 a(I4640000 g1 (g5 S'\x9a\x99\x99\x99\x99\x994@' p2479 tp2480 Rp2481 g2357 F21.0 tp2482 a(I4650000 g1 (g5 S'\x14\xaeG\xe1z\x944@' p2483 tp2484 Rp2485 g2357 F20.0 tp2486 a(I4660000 g1 (g5 S'\x9a\x99\x99\x99\x99\x994@' p2487 tp2488 Rp2489 g2357 F21.0 tp2490 a(I4670000 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e4@' p2491 tp2492 Rp2493 g2357 F21.0 tp2494 a(I4680000 g1 (g5 S'\x14\xaeG\xe1z\x944@' p2495 tp2496 Rp2497 g2357 F20.0 tp2498 a(I4690000 g1 (g5 S'\x14\xaeG\xe1z\x944@' p2499 tp2500 Rp2501 g2357 F20.0 tp2502 a(I4700000 g1 (g5 S'R\xb8\x1e\x85\xeb\x914@' p2503 tp2504 Rp2505 g2357 F21.0 tp2506 a(I4710000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x8c4@' p2507 tp2508 Rp2509 g2357 F21.0 tp2510 a(I4720000 g1 (g5 S'R\xb8\x1e\x85\xeb\x914@' p2511 tp2512 Rp2513 g2357 F21.0 tp2514 a(I4730000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x8c4@' p2515 tp2516 Rp2517 g2357 F21.0 tp2518 a(I4740000 g1 (g5 S'R\xb8\x1e\x85\xeb\x914@' p2519 tp2520 Rp2521 g2357 F21.0 tp2522 a(I4750000 g1 (g5 S'\x14\xaeG\xe1z\x944@' p2523 tp2524 Rp2525 g2357 F21.0 tp2526 a(I4760000 g1 (g5 S'\x14\xaeG\xe1z\x944@' p2527 tp2528 Rp2529 g2357 F21.0 tp2530 a(I4770000 g1 (g5 S'\x9a\x99\x99\x99\x99\x994@' p2531 tp2532 Rp2533 g2357 F21.0 tp2534 a(I4780000 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e4@' p2535 tp2536 Rp2537 g2357 F21.0 tp2538 a(I4790000 g1 (g5 S'\xe1z\x14\xaeG\xa14@' p2539 tp2540 Rp2541 g2357 F21.0 tp2542 a(I4800000 g1 (g5 S'\xa4p=\n\xd7\xa34@' p2543 tp2544 Rp2545 g2357 F21.0 tp2546 a(I4810000 g1 (g5 S')\\\x8f\xc2\xf5\xa84@' p2547 tp2548 Rp2549 g2357 F21.0 tp2550 a(I4820000 g1 (g5 S')\\\x8f\xc2\xf5\xa84@' p2551 tp2552 Rp2553 g2357 F21.0 tp2554 a(I4830000 g1 (g5 S'q=\n\xd7\xa3\xb04@' p2555 tp2556 Rp2557 g2357 F21.0 tp2558 a(I4840000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab4@' p2559 tp2560 Rp2561 g2357 F21.0 tp2562 a(I4850000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab4@' p2563 tp2564 Rp2565 g2357 F21.0 tp2566 a(I4860000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab4@' p2567 tp2568 Rp2569 g2357 F20.0 tp2570 a(I4870000 g1 (g5 S'\xaeG\xe1z\x14\xae4@' p2571 tp2572 Rp2573 g2357 F20.0 tp2574 a(I4880000 g1 (g5 S'33333\xb34@' p2575 tp2576 Rp2577 g2357 F21.0 tp2578 a(I4890000 g1 (g5 S'\xf6(\\\x8f\xc2\xb54@' p2579 tp2580 Rp2581 g2357 F21.0 tp2582 a(I4900000 g1 (g5 S'\xa4p=\n\xd7\xa34@' p2583 tp2584 Rp2585 g2357 F13.0 tp2586 a(I4910000 g1 (g5 S'\xe1z\x14\xaeG\xa14@' p2587 tp2588 Rp2589 g2357 F20.0 tp2590 a(I4920000 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e4@' p2591 tp2592 Rp2593 g2357 F20.0 tp2594 a(I4930000 g1 (g5 S'\\\x8f\xc2\xf5(\x9c4@' p2595 tp2596 Rp2597 g2357 F21.0 tp2598 a(I4940000 g1 (g5 S'\xe1z\x14\xaeG\xa14@' p2599 tp2600 Rp2601 g2357 F21.0 tp2602 a(I4950000 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e4@' p2603 tp2604 Rp2605 g2357 F20.0 tp2606 a(I4960000 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e4@' p2607 tp2608 Rp2609 g2357 F21.0 tp2610 a(I4970000 g1 (g5 S'\\\x8f\xc2\xf5(\x9c4@' p2611 tp2612 Rp2613 g2357 F21.0 tp2614 a(I4980000 g1 (g5 S'\\\x8f\xc2\xf5(\x9c4@' p2615 tp2616 Rp2617 g2357 F21.0 tp2618 a(I4990000 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e4@' p2619 tp2620 Rp2621 g2357 F21.0 tp2622 a(I5000000 g1 (g5 S'R\xb8\x1e\x85\xeb\x914@' p2623 tp2624 Rp2625 g2357 F19.0 tp2626 a(I5010000 g1 (g5 S'\xd7\xa3p=\n\x974@' p2627 tp2628 Rp2629 g2357 F21.0 tp2630 a(I5020000 g1 (g5 S'\x9a\x99\x99\x99\x99\x994@' p2631 tp2632 Rp2633 g2357 F21.0 tp2634 a(I5030000 g1 (g5 S'\xd7\xa3p=\n\x974@' p2635 tp2636 Rp2637 g2357 F21.0 tp2638 a(I5040000 g1 (g5 S'\xd7\xa3p=\n\x974@' p2639 tp2640 Rp2641 g2357 F20.0 tp2642 a(I5050000 g1 (g5 S'\x9a\x99\x99\x99\x99\x994@' p2643 tp2644 Rp2645 g2357 F20.0 tp2646 a(I5060000 g1 (g5 S'\x14\xaeG\xe1z\x944@' p2647 tp2648 Rp2649 g2357 F21.0 tp2650 a(I5070000 g1 (g5 S'\xd7\xa3p=\n\x974@' p2651 tp2652 Rp2653 g2357 F21.0 tp2654 a(I5080000 g1 (g5 S')\\\x8f\xc2\xf5\xa84@' p2655 tp2656 Rp2657 g2357 F21.0 tp2658 a(I5090000 g1 (g5 S'\xa4p=\n\xd7\xa34@' p2659 tp2660 Rp2661 g2357 F21.0 tp2662 a(I5100000 g1 (g5 S'fffff\xa64@' p2663 tp2664 Rp2665 g2357 F21.0 tp2666 a(I5110000 g1 (g5 S'fffff\xa64@' p2667 tp2668 Rp2669 g2357 F21.0 tp2670 a(I5120000 g1 (g5 S'fffff\xa64@' p2671 tp2672 Rp2673 g2357 F21.0 tp2674 a(I5130000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab4@' p2675 tp2676 Rp2677 g2357 F21.0 tp2678 a(I5140000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab4@' p2679 tp2680 Rp2681 g2357 F21.0 tp2682 a(I5150000 g1 (g5 S'q=\n\xd7\xa3\xb04@' p2683 tp2684 Rp2685 g2357 F21.0 tp2686 a(I5160000 g1 (g5 S'33333\xb34@' p2687 tp2688 Rp2689 g2357 F21.0 tp2690 a(I5170000 g1 (g5 S'\x00\x00\x00\x00\x00\xc04@' p2691 tp2692 Rp2693 g2357 F21.0 tp2694 a(I5180000 g1 (g5 S'\xc3\xf5(\\\x8f\xc24@' p2695 tp2696 Rp2697 g2357 F21.0 tp2698 a(I5190000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc54@' p2699 tp2700 Rp2701 g1 (g5 S'H\xe1z\x14\xae\xc74@' p2702 tp2703 Rp2704 F20.0 tp2705 a(I5200000 g1 (g5 S'\x00\x00\x00\x00\x00\xc04@' p2706 tp2707 Rp2708 g2704 F21.0 tp2709 a(I5210000 g1 (g5 S'\xc3\xf5(\\\x8f\xc24@' p2710 tp2711 Rp2712 g2704 F21.0 tp2713 a(I5220000 g1 (g5 S'\xc3\xf5(\\\x8f\xc24@' p2714 tp2715 Rp2716 g2704 F21.0 tp2717 a(I5230000 g1 (g5 S'\xc3\xf5(\\\x8f\xc24@' p2718 tp2719 Rp2720 g2704 F21.0 tp2721 a(I5240000 g1 (g5 S'\x00\x00\x00\x00\x00\xc04@' p2722 tp2723 Rp2724 g2704 F21.0 tp2725 a(I5250000 g1 (g5 S'\xc3\xf5(\\\x8f\xc24@' p2726 tp2727 Rp2728 g2704 F20.0 tp2729 a(I5260000 g1 (g5 S'\n\xd7\xa3p=\xca4@' p2730 tp2731 Rp2732 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc4@' p2733 tp2734 Rp2735 F20.0 tp2736 a(I5270000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc4@' p2737 tp2738 Rp2739 g1 (g5 S'\x8f\xc2\xf5(\\\xcf4@' p2740 tp2741 Rp2742 F20.0 tp2743 a(I5280000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc4@' p2744 tp2745 Rp2746 g2742 F21.0 tp2747 a(I5290000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc4@' p2748 tp2749 Rp2750 g2742 F21.0 tp2751 a(I5300000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc4@' p2752 tp2753 Rp2754 g2742 F21.0 tp2755 a(I5310000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc4@' p2756 tp2757 Rp2758 g1 (g5 S'R\xb8\x1e\x85\xeb\xd14@' p2759 tp2760 Rp2761 F20.0 tp2762 a(I5320000 g1 (g5 S'\xc3\xf5(\\\x8f\xc24@' p2763 tp2764 Rp2765 g2761 F21.0 tp2766 a(I5330000 g1 (g5 S'\x00\x00\x00\x00\x00\xc04@' p2767 tp2768 Rp2769 g2761 F20.0 tp2770 a(I5340000 g1 (g5 S'\x00\x00\x00\x00\x00\xc04@' p2771 tp2772 Rp2773 g2761 F21.0 tp2774 a(I5350000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p2775 tp2776 Rp2777 g2761 F20.0 tp2778 a(I5360000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p2779 tp2780 Rp2781 g2761 F21.0 tp2782 a(I5370000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p2783 tp2784 Rp2785 g2761 F21.0 tp2786 a(I5380000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p2787 tp2788 Rp2789 g2761 F21.0 tp2790 a(I5390000 g1 (g5 S'33333\xb34@' p2791 tp2792 Rp2793 g2761 F19.0 tp2794 a(I5400000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p2795 tp2796 Rp2797 g2761 F20.0 tp2798 a(I5410000 g1 (g5 S'\xf6(\\\x8f\xc2\xb54@' p2799 tp2800 Rp2801 g2761 F21.0 tp2802 a(I5420000 g1 (g5 S'\xf6(\\\x8f\xc2\xb54@' p2803 tp2804 Rp2805 g2761 F20.0 tp2806 a(I5430000 g1 (g5 S'\xf6(\\\x8f\xc2\xb54@' p2807 tp2808 Rp2809 g2761 F21.0 tp2810 a(I5440000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p2811 tp2812 Rp2813 g2761 F21.0 tp2814 a(I5450000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p2815 tp2816 Rp2817 g2761 F21.0 tp2818 a(I5460000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p2819 tp2820 Rp2821 g2761 F21.0 tp2822 a(I5470000 g1 (g5 S'=\n\xd7\xa3p\xbd4@' p2823 tp2824 Rp2825 g2761 F21.0 tp2826 a(I5480000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc54@' p2827 tp2828 Rp2829 g2761 F20.0 tp2830 a(I5490000 g1 (g5 S'H\xe1z\x14\xae\xc74@' p2831 tp2832 Rp2833 g2761 F21.0 tp2834 a(I5500000 g1 (g5 S'\x00\x00\x00\x00\x00\xc04@' p2835 tp2836 Rp2837 g2761 F18.0 tp2838 a(I5510000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p2839 tp2840 Rp2841 g2761 F20.0 tp2842 a(I5520000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p2843 tp2844 Rp2845 g2761 F21.0 tp2846 a(I5530000 g1 (g5 S'\xf6(\\\x8f\xc2\xb54@' p2847 tp2848 Rp2849 g2761 F20.0 tp2850 a(I5540000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p2851 tp2852 Rp2853 g2761 F21.0 tp2854 a(I5550000 g1 (g5 S'33333\xb34@' p2855 tp2856 Rp2857 g2761 F21.0 tp2858 a(I5560000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p2859 tp2860 Rp2861 g2761 F21.0 tp2862 a(I5570000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p2863 tp2864 Rp2865 g2761 F20.0 tp2866 a(I5580000 g1 (g5 S'=\n\xd7\xa3p\xbd4@' p2867 tp2868 Rp2869 g2761 F20.0 tp2870 a(I5590000 g1 (g5 S'=\n\xd7\xa3p\xbd4@' p2871 tp2872 Rp2873 g2761 F21.0 tp2874 a(I5600000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p2875 tp2876 Rp2877 g2761 F20.0 tp2878 a(I5610000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p2879 tp2880 Rp2881 g2761 F21.0 tp2882 a(I5620000 g1 (g5 S'q=\n\xd7\xa3\xb04@' p2883 tp2884 Rp2885 g2761 F20.0 tp2886 a(I5630000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab4@' p2887 tp2888 Rp2889 g2761 F21.0 tp2890 a(I5640000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab4@' p2891 tp2892 Rp2893 g2761 F21.0 tp2894 a(I5650000 g1 (g5 S'\xaeG\xe1z\x14\xae4@' p2895 tp2896 Rp2897 g2761 F21.0 tp2898 a(I5660000 g1 (g5 S'\xaeG\xe1z\x14\xae4@' p2899 tp2900 Rp2901 g2761 F21.0 tp2902 a(I5670000 g1 (g5 S'\xf6(\\\x8f\xc2\xb54@' p2903 tp2904 Rp2905 g2761 F21.0 tp2906 a(I5680000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p2907 tp2908 Rp2909 g2761 F21.0 tp2910 a(I5690000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p2911 tp2912 Rp2913 g2761 F20.0 tp2914 a(I5700000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p2915 tp2916 Rp2917 g2761 F21.0 tp2918 a(I5710000 g1 (g5 S'=\n\xd7\xa3p\xbd4@' p2919 tp2920 Rp2921 g2761 F21.0 tp2922 a(I5720000 g1 (g5 S'\xc3\xf5(\\\x8f\xc24@' p2923 tp2924 Rp2925 g2761 F20.0 tp2926 a(I5730000 g1 (g5 S'=\n\xd7\xa3p\xbd4@' p2927 tp2928 Rp2929 g2761 F20.0 tp2930 a(I5740000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p2931 tp2932 Rp2933 g2761 F20.0 tp2934 a(I5750000 g1 (g5 S'33333\xb34@' p2935 tp2936 Rp2937 g2761 F21.0 tp2938 a(I5760000 g1 (g5 S'\xf6(\\\x8f\xc2\xb54@' p2939 tp2940 Rp2941 g2761 F21.0 tp2942 a(I5770000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p2943 tp2944 Rp2945 g2761 F21.0 tp2946 a(I5780000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p2947 tp2948 Rp2949 g2761 F21.0 tp2950 a(I5790000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p2951 tp2952 Rp2953 g2761 F21.0 tp2954 a(I5800000 g1 (g5 S'\xf6(\\\x8f\xc2\xb54@' p2955 tp2956 Rp2957 g2761 F21.0 tp2958 a(I5810000 g1 (g5 S')\\\x8f\xc2\xf5\xa84@' p2959 tp2960 Rp2961 g2761 F20.0 tp2962 a(I5820000 g1 (g5 S'\xe1z\x14\xaeG\xa14@' p2963 tp2964 Rp2965 g2761 F21.0 tp2966 a(I5830000 g1 (g5 S'\xe1z\x14\xaeG\xa14@' p2967 tp2968 Rp2969 g2761 F21.0 tp2970 a(I5840000 g1 (g5 S'\\\x8f\xc2\xf5(\x9c4@' p2971 tp2972 Rp2973 g2761 F18.0 tp2974 a(I5850000 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e4@' p2975 tp2976 Rp2977 g2761 F21.0 tp2978 a(I5860000 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e4@' p2979 tp2980 Rp2981 g2761 F20.0 tp2982 a(I5870000 g1 (g5 S'\xe1z\x14\xaeG\xa14@' p2983 tp2984 Rp2985 g2761 F20.0 tp2986 a(I5880000 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e4@' p2987 tp2988 Rp2989 g2761 F21.0 tp2990 a(I5890000 g1 (g5 S'\xe1z\x14\xaeG\xa14@' p2991 tp2992 Rp2993 g2761 F21.0 tp2994 a(I5900000 g1 (g5 S'\xe1z\x14\xaeG\xa14@' p2995 tp2996 Rp2997 g2761 F20.0 tp2998 a(I5910000 g1 (g5 S'fffff\xa64@' p2999 tp3000 Rp3001 g2761 F21.0 tp3002 a(I5920000 g1 (g5 S'q=\n\xd7\xa3\xb04@' p3003 tp3004 Rp3005 g2761 F21.0 tp3006 a(I5930000 g1 (g5 S'\xaeG\xe1z\x14\xae4@' p3007 tp3008 Rp3009 g2761 F21.0 tp3010 a(I5940000 g1 (g5 S'\xaeG\xe1z\x14\xae4@' p3011 tp3012 Rp3013 g2761 F21.0 tp3014 a(I5950000 g1 (g5 S')\\\x8f\xc2\xf5\xa84@' p3015 tp3016 Rp3017 g2761 F20.0 tp3018 a(I5960000 g1 (g5 S')\\\x8f\xc2\xf5\xa84@' p3019 tp3020 Rp3021 g2761 F20.0 tp3022 a(I5970000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab4@' p3023 tp3024 Rp3025 g2761 F21.0 tp3026 a(I5980000 g1 (g5 S'33333\xb34@' p3027 tp3028 Rp3029 g2761 F21.0 tp3030 a(I5990000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p3031 tp3032 Rp3033 g2761 F21.0 tp3034 a(I6000000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p3035 tp3036 Rp3037 g2761 F21.0 tp3038 a(I6010000 g1 (g5 S'\x00\x00\x00\x00\x00\xc04@' p3039 tp3040 Rp3041 g2761 F21.0 tp3042 a(I6020000 g1 (g5 S'=\n\xd7\xa3p\xbd4@' p3043 tp3044 Rp3045 g2761 F20.0 tp3046 a(I6030000 g1 (g5 S'\x00\x00\x00\x00\x00\xc04@' p3047 tp3048 Rp3049 g2761 F21.0 tp3050 a(I6040000 g1 (g5 S'\x00\x00\x00\x00\x00\xc04@' p3051 tp3052 Rp3053 g2761 F21.0 tp3054 a(I6050000 g1 (g5 S'\x00\x00\x00\x00\x00\xc04@' p3055 tp3056 Rp3057 g2761 F21.0 tp3058 a(I6060000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p3059 tp3060 Rp3061 g2761 F21.0 tp3062 a(I6070000 g1 (g5 S'=\n\xd7\xa3p\xbd4@' p3063 tp3064 Rp3065 g2761 F21.0 tp3066 a(I6080000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p3067 tp3068 Rp3069 g2761 F21.0 tp3070 a(I6090000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p3071 tp3072 Rp3073 g2761 F20.0 tp3074 a(I6100000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p3075 tp3076 Rp3077 g2761 F21.0 tp3078 a(I6110000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p3079 tp3080 Rp3081 g2761 F21.0 tp3082 a(I6120000 g1 (g5 S'=\n\xd7\xa3p\xbd4@' p3083 tp3084 Rp3085 g2761 F21.0 tp3086 a(I6130000 g1 (g5 S'=\n\xd7\xa3p\xbd4@' p3087 tp3088 Rp3089 g2761 F20.0 tp3090 a(I6140000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc54@' p3091 tp3092 Rp3093 g2761 F21.0 tp3094 a(I6150000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p3095 tp3096 Rp3097 g2761 F19.0 tp3098 a(I6160000 g1 (g5 S'=\n\xd7\xa3p\xbd4@' p3099 tp3100 Rp3101 g2761 F20.0 tp3102 a(I6170000 g1 (g5 S'\x00\x00\x00\x00\x00\xc04@' p3103 tp3104 Rp3105 g2761 F21.0 tp3106 a(I6180000 g1 (g5 S'\x00\x00\x00\x00\x00\xc04@' p3107 tp3108 Rp3109 g2761 F21.0 tp3110 a(I6190000 g1 (g5 S'H\xe1z\x14\xae\xc74@' p3111 tp3112 Rp3113 g2761 F21.0 tp3114 a(I6200000 g1 (g5 S'H\xe1z\x14\xae\xc74@' p3115 tp3116 Rp3117 g2761 F21.0 tp3118 a(I6210000 g1 (g5 S'\n\xd7\xa3p=\xca4@' p3119 tp3120 Rp3121 g2761 F20.0 tp3122 a(I6220000 g1 (g5 S'R\xb8\x1e\x85\xeb\xd14@' p3123 tp3124 Rp3125 g2761 F21.0 tp3126 a(I6230000 g1 (g5 S'\x14\xaeG\xe1z\xd44@' p3127 tp3128 Rp3129 g1 (g5 S'\x14\xaeG\xe1z\xd44@' p3130 tp3131 Rp3132 F21.0 tp3133 a(I6240000 g1 (g5 S'R\xb8\x1e\x85\xeb\xd14@' p3134 tp3135 Rp3136 g3132 F21.0 tp3137 a(I6250000 g1 (g5 S'R\xb8\x1e\x85\xeb\xd14@' p3138 tp3139 Rp3140 g3132 F21.0 tp3141 a(I6260000 g1 (g5 S'\xd7\xa3p=\n\xd74@' p3142 tp3143 Rp3144 g1 (g5 S'\xd7\xa3p=\n\xd74@' p3145 tp3146 Rp3147 F21.0 tp3148 a(I6270000 g1 (g5 S'\xd7\xa3p=\n\xd74@' p3149 tp3150 Rp3151 g3147 F21.0 tp3152 a(I6280000 g1 (g5 S'R\xb8\x1e\x85\xeb\xd14@' p3153 tp3154 Rp3155 g1 (g5 S'\x9a\x99\x99\x99\x99\xd94@' p3156 tp3157 Rp3158 F20.0 tp3159 a(I6290000 g1 (g5 S'\x9a\x99\x99\x99\x99\xd94@' p3160 tp3161 Rp3162 g3158 F21.0 tp3163 a(I6300000 g1 (g5 S'\\\x8f\xc2\xf5(\xdc4@' p3164 tp3165 Rp3166 g1 (g5 S'\\\x8f\xc2\xf5(\xdc4@' p3167 tp3168 Rp3169 F21.0 tp3170 a(I6310000 g1 (g5 S'\x9a\x99\x99\x99\x99\xd94@' p3171 tp3172 Rp3173 g3169 F20.0 tp3174 a(I6320000 g1 (g5 S'\\\x8f\xc2\xf5(\xdc4@' p3175 tp3176 Rp3177 g1 (g5 S'\x1f\x85\xebQ\xb8\xde4@' p3178 tp3179 Rp3180 F21.0 tp3181 a(I6330000 g1 (g5 S'\xd7\xa3p=\n\xd74@' p3182 tp3183 Rp3184 g3180 F21.0 tp3185 a(I6340000 g1 (g5 S'\x14\xaeG\xe1z\xd44@' p3186 tp3187 Rp3188 g3180 F21.0 tp3189 a(I6350000 g1 (g5 S'\x8f\xc2\xf5(\\\xcf4@' p3190 tp3191 Rp3192 g3180 F21.0 tp3193 a(I6360000 g1 (g5 S'\x8f\xc2\xf5(\\\xcf4@' p3194 tp3195 Rp3196 g3180 F20.0 tp3197 a(I6370000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc4@' p3198 tp3199 Rp3200 g3180 F21.0 tp3201 a(I6380000 g1 (g5 S'H\xe1z\x14\xae\xc74@' p3202 tp3203 Rp3204 g3180 F20.0 tp3205 a(I6390000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc54@' p3206 tp3207 Rp3208 g3180 F21.0 tp3209 a(I6400000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc54@' p3210 tp3211 Rp3212 g3180 F21.0 tp3213 a(I6410000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc54@' p3214 tp3215 Rp3216 g3180 F21.0 tp3217 a(I6420000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc54@' p3218 tp3219 Rp3220 g3180 F21.0 tp3221 a(I6430000 g1 (g5 S'\xc3\xf5(\\\x8f\xc24@' p3222 tp3223 Rp3224 g3180 F21.0 tp3225 a(I6440000 g1 (g5 S'\x00\x00\x00\x00\x00\xc04@' p3226 tp3227 Rp3228 g3180 F21.0 tp3229 a(I6450000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc54@' p3230 tp3231 Rp3232 g3180 F21.0 tp3233 a(I6460000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc54@' p3234 tp3235 Rp3236 g3180 F21.0 tp3237 a(I6470000 g1 (g5 S'\xc3\xf5(\\\x8f\xc24@' p3238 tp3239 Rp3240 g3180 F21.0 tp3241 a(I6480000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p3242 tp3243 Rp3244 g3180 F21.0 tp3245 a(I6490000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc54@' p3246 tp3247 Rp3248 g3180 F21.0 tp3249 a(I6500000 g1 (g5 S'\xc3\xf5(\\\x8f\xc24@' p3250 tp3251 Rp3252 g3180 F21.0 tp3253 a(I6510000 g1 (g5 S'H\xe1z\x14\xae\xc74@' p3254 tp3255 Rp3256 g3180 F21.0 tp3257 a(I6520000 g1 (g5 S'H\xe1z\x14\xae\xc74@' p3258 tp3259 Rp3260 g3180 F21.0 tp3261 a(I6530000 g1 (g5 S'H\xe1z\x14\xae\xc74@' p3262 tp3263 Rp3264 g3180 F21.0 tp3265 a(I6540000 g1 (g5 S'H\xe1z\x14\xae\xc74@' p3266 tp3267 Rp3268 g3180 F20.0 tp3269 a(I6550000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc4@' p3270 tp3271 Rp3272 g3180 F21.0 tp3273 a(I6560000 g1 (g5 S'\n\xd7\xa3p=\xca4@' p3274 tp3275 Rp3276 g3180 F21.0 tp3277 a(I6570000 g1 (g5 S'H\xe1z\x14\xae\xc74@' p3278 tp3279 Rp3280 g3180 F21.0 tp3281 a(I6580000 g1 (g5 S'\n\xd7\xa3p=\xca4@' p3282 tp3283 Rp3284 g3180 F21.0 tp3285 a(I6590000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc54@' p3286 tp3287 Rp3288 g3180 F20.0 tp3289 a(I6600000 g1 (g5 S'=\n\xd7\xa3p\xbd4@' p3290 tp3291 Rp3292 g3180 F21.0 tp3293 a(I6610000 g1 (g5 S'=\n\xd7\xa3p\xbd4@' p3294 tp3295 Rp3296 g3180 F21.0 tp3297 a(I6620000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p3298 tp3299 Rp3300 g3180 F21.0 tp3301 a(I6630000 g1 (g5 S'=\n\xd7\xa3p\xbd4@' p3302 tp3303 Rp3304 g3180 F21.0 tp3305 a(I6640000 g1 (g5 S'=\n\xd7\xa3p\xbd4@' p3306 tp3307 Rp3308 g3180 F21.0 tp3309 a(I6650000 g1 (g5 S'H\xe1z\x14\xae\xc74@' p3310 tp3311 Rp3312 g3180 F21.0 tp3313 a(I6660000 g1 (g5 S'H\xe1z\x14\xae\xc74@' p3314 tp3315 Rp3316 g3180 F21.0 tp3317 a(I6670000 g1 (g5 S'\x8f\xc2\xf5(\\\xcf4@' p3318 tp3319 Rp3320 g3180 F21.0 tp3321 a(I6680000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc4@' p3322 tp3323 Rp3324 g3180 F20.0 tp3325 a(I6690000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc54@' p3326 tp3327 Rp3328 g3180 F20.0 tp3329 a(I6700000 g1 (g5 S'H\xe1z\x14\xae\xc74@' p3330 tp3331 Rp3332 g3180 F21.0 tp3333 a(I6710000 g1 (g5 S'\n\xd7\xa3p=\xca4@' p3334 tp3335 Rp3336 g3180 F21.0 tp3337 a(I6720000 g1 (g5 S'H\xe1z\x14\xae\xc74@' p3338 tp3339 Rp3340 g3180 F20.0 tp3341 a(I6730000 g1 (g5 S'H\xe1z\x14\xae\xc74@' p3342 tp3343 Rp3344 g3180 F21.0 tp3345 a(I6740000 g1 (g5 S'H\xe1z\x14\xae\xc74@' p3346 tp3347 Rp3348 g3180 F21.0 tp3349 a(I6750000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc4@' p3350 tp3351 Rp3352 g3180 F21.0 tp3353 a(I6760000 g1 (g5 S'R\xb8\x1e\x85\xeb\xd14@' p3354 tp3355 Rp3356 g3180 F21.0 tp3357 a(I6770000 g1 (g5 S'R\xb8\x1e\x85\xeb\xd14@' p3358 tp3359 Rp3360 g3180 F20.0 tp3361 a(I6780000 g1 (g5 S'\x8f\xc2\xf5(\\\xcf4@' p3362 tp3363 Rp3364 g3180 F20.0 tp3365 a(I6790000 g1 (g5 S'R\xb8\x1e\x85\xeb\xd14@' p3366 tp3367 Rp3368 g3180 F20.0 tp3369 a(I6800000 g1 (g5 S'\x8f\xc2\xf5(\\\xcf4@' p3370 tp3371 Rp3372 g3180 F21.0 tp3373 a(I6810000 g1 (g5 S'\x8f\xc2\xf5(\\\xcf4@' p3374 tp3375 Rp3376 g3180 F20.0 tp3377 a(I6820000 g1 (g5 S'\x8f\xc2\xf5(\\\xcf4@' p3378 tp3379 Rp3380 g3180 F21.0 tp3381 a(I6830000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc4@' p3382 tp3383 Rp3384 g3180 F21.0 tp3385 a(I6840000 g1 (g5 S'\n\xd7\xa3p=\xca4@' p3386 tp3387 Rp3388 g3180 F21.0 tp3389 a(I6850000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc4@' p3390 tp3391 Rp3392 g3180 F21.0 tp3393 a(I6860000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc4@' p3394 tp3395 Rp3396 g3180 F20.0 tp3397 a(I6870000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc4@' p3398 tp3399 Rp3400 g3180 F21.0 tp3401 a(I6880000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc4@' p3402 tp3403 Rp3404 g3180 F21.0 tp3405 a(I6890000 g1 (g5 S'R\xb8\x1e\x85\xeb\xd14@' p3406 tp3407 Rp3408 g3180 F21.0 tp3409 a(I6900000 g1 (g5 S'R\xb8\x1e\x85\xeb\xd14@' p3410 tp3411 Rp3412 g3180 F21.0 tp3413 a(I6910000 g1 (g5 S'\n\xd7\xa3p=\xca4@' p3414 tp3415 Rp3416 g3180 F21.0 tp3417 a(I6920000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc54@' p3418 tp3419 Rp3420 g3180 F21.0 tp3421 a(I6930000 g1 (g5 S'=\n\xd7\xa3p\xbd4@' p3422 tp3423 Rp3424 g3180 F20.0 tp3425 a(I6940000 g1 (g5 S'=\n\xd7\xa3p\xbd4@' p3426 tp3427 Rp3428 g3180 F21.0 tp3429 a(I6950000 g1 (g5 S'\xc3\xf5(\\\x8f\xc24@' p3430 tp3431 Rp3432 g3180 F21.0 tp3433 a(I6960000 g1 (g5 S'\x00\x00\x00\x00\x00\xc04@' p3434 tp3435 Rp3436 g3180 F21.0 tp3437 a(I6970000 g1 (g5 S'\x00\x00\x00\x00\x00\xc04@' p3438 tp3439 Rp3440 g3180 F21.0 tp3441 a(I6980000 g1 (g5 S'\x00\x00\x00\x00\x00\xc04@' p3442 tp3443 Rp3444 g3180 F21.0 tp3445 a(I6990000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p3446 tp3447 Rp3448 g3180 F21.0 tp3449 a(I7000000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p3450 tp3451 Rp3452 g3180 F21.0 tp3453 a(I7010000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p3454 tp3455 Rp3456 g3180 F21.0 tp3457 a(I7020000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p3458 tp3459 Rp3460 g3180 F21.0 tp3461 a(I7030000 g1 (g5 S'\xf6(\\\x8f\xc2\xb54@' p3462 tp3463 Rp3464 g3180 F20.0 tp3465 a(I7040000 g1 (g5 S'\xf6(\\\x8f\xc2\xb54@' p3466 tp3467 Rp3468 g3180 F21.0 tp3469 a(I7050000 g1 (g5 S'33333\xb34@' p3470 tp3471 Rp3472 g3180 F20.0 tp3473 a(I7060000 g1 (g5 S'\xaeG\xe1z\x14\xae4@' p3474 tp3475 Rp3476 g3180 F21.0 tp3477 a(I7070000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab4@' p3478 tp3479 Rp3480 g3180 F21.0 tp3481 a(I7080000 g1 (g5 S'fffff\xa64@' p3482 tp3483 Rp3484 g3180 F20.0 tp3485 a(I7090000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab4@' p3486 tp3487 Rp3488 g3180 F21.0 tp3489 a(I7100000 g1 (g5 S'q=\n\xd7\xa3\xb04@' p3490 tp3491 Rp3492 g3180 F21.0 tp3493 a(I7110000 g1 (g5 S'33333\xb34@' p3494 tp3495 Rp3496 g3180 F20.0 tp3497 a(I7120000 g1 (g5 S'33333\xb34@' p3498 tp3499 Rp3500 g3180 F21.0 tp3501 a(I7130000 g1 (g5 S'\xf6(\\\x8f\xc2\xb54@' p3502 tp3503 Rp3504 g3180 F21.0 tp3505 a(I7140000 g1 (g5 S'q=\n\xd7\xa3\xb04@' p3506 tp3507 Rp3508 g3180 F21.0 tp3509 a(I7150000 g1 (g5 S'q=\n\xd7\xa3\xb04@' p3510 tp3511 Rp3512 g3180 F21.0 tp3513 a(I7160000 g1 (g5 S'33333\xb34@' p3514 tp3515 Rp3516 g3180 F21.0 tp3517 a(I7170000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p3518 tp3519 Rp3520 g3180 F20.0 tp3521 a(I7180000 g1 (g5 S'33333\xb34@' p3522 tp3523 Rp3524 g3180 F20.0 tp3525 a(I7190000 g1 (g5 S'\xaeG\xe1z\x14\xae4@' p3526 tp3527 Rp3528 g3180 F21.0 tp3529 a(I7200000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p3530 tp3531 Rp3532 g3180 F21.0 tp3533 a(I7210000 g1 (g5 S'=\n\xd7\xa3p\xbd4@' p3534 tp3535 Rp3536 g3180 F21.0 tp3537 a(I7220000 g1 (g5 S'\x00\x00\x00\x00\x00\xc04@' p3538 tp3539 Rp3540 g3180 F21.0 tp3541 a(I7230000 g1 (g5 S'\xc3\xf5(\\\x8f\xc24@' p3542 tp3543 Rp3544 g3180 F21.0 tp3545 a(I7240000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc54@' p3546 tp3547 Rp3548 g3180 F20.0 tp3549 a(I7250000 g1 (g5 S'\n\xd7\xa3p=\xca4@' p3550 tp3551 Rp3552 g3180 F21.0 tp3553 a(I7260000 g1 (g5 S'H\xe1z\x14\xae\xc74@' p3554 tp3555 Rp3556 g3180 F21.0 tp3557 a(I7270000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc54@' p3558 tp3559 Rp3560 g3180 F21.0 tp3561 a(I7280000 g1 (g5 S'\n\xd7\xa3p=\xca4@' p3562 tp3563 Rp3564 g3180 F21.0 tp3565 a(I7290000 g1 (g5 S'\n\xd7\xa3p=\xca4@' p3566 tp3567 Rp3568 g3180 F21.0 tp3569 a(I7300000 g1 (g5 S'H\xe1z\x14\xae\xc74@' p3570 tp3571 Rp3572 g3180 F21.0 tp3573 a(I7310000 g1 (g5 S'\n\xd7\xa3p=\xca4@' p3574 tp3575 Rp3576 g3180 F21.0 tp3577 a(I7320000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc54@' p3578 tp3579 Rp3580 g3180 F21.0 tp3581 a(I7330000 g1 (g5 S'\xc3\xf5(\\\x8f\xc24@' p3582 tp3583 Rp3584 g3180 F21.0 tp3585 a(I7340000 g1 (g5 S'\xc3\xf5(\\\x8f\xc24@' p3586 tp3587 Rp3588 g3180 F21.0 tp3589 a(I7350000 g1 (g5 S'H\xe1z\x14\xae\xc74@' p3590 tp3591 Rp3592 g3180 F21.0 tp3593 a(I7360000 g1 (g5 S'\n\xd7\xa3p=\xca4@' p3594 tp3595 Rp3596 g3180 F21.0 tp3597 a(I7370000 g1 (g5 S'H\xe1z\x14\xae\xc74@' p3598 tp3599 Rp3600 g3180 F21.0 tp3601 a(I7380000 g1 (g5 S'H\xe1z\x14\xae\xc74@' p3602 tp3603 Rp3604 g3180 F21.0 tp3605 a(I7390000 g1 (g5 S'H\xe1z\x14\xae\xc74@' p3606 tp3607 Rp3608 g3180 F21.0 tp3609 a(I7400000 g1 (g5 S'H\xe1z\x14\xae\xc74@' p3610 tp3611 Rp3612 g3180 F21.0 tp3613 a(I7410000 g1 (g5 S'\n\xd7\xa3p=\xca4@' p3614 tp3615 Rp3616 g3180 F21.0 tp3617 a(I7420000 g1 (g5 S'\x8f\xc2\xf5(\\\xcf4@' p3618 tp3619 Rp3620 g3180 F21.0 tp3621 a(I7430000 g1 (g5 S'\xd7\xa3p=\n\xd74@' p3622 tp3623 Rp3624 g3180 F21.0 tp3625 a(I7440000 g1 (g5 S'\x9a\x99\x99\x99\x99\xd94@' p3626 tp3627 Rp3628 g3180 F21.0 tp3629 a(I7450000 g1 (g5 S'\xd7\xa3p=\n\xd74@' p3630 tp3631 Rp3632 g3180 F21.0 tp3633 a(I7460000 g1 (g5 S'R\xb8\x1e\x85\xeb\xd14@' p3634 tp3635 Rp3636 g3180 F21.0 tp3637 a(I7470000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc4@' p3638 tp3639 Rp3640 g3180 F21.0 tp3641 a(I7476493 g1 (g5 S'\x85\xebQ\xb8\x1e\xc54@' p3642 tp3643 Rp3644 g3180 F21.0 tp3645 a.(lp0 F-21.0 aF-20.0 aF-21.0 aF-21.0 aF-20.0 aF-20.0 aF-20.0 aF-20.0 aF-21.0 aF-21.0 aF-20.0 aF-21.0 aF-21.0 aF-20.0 aF-21.0 aF-20.0 aF-18.0 aF-19.0 aF-20.0 aF-21.0 aF-21.0 aF-20.0 aF-21.0 aF-20.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-20.0 aF-20.0 aF-19.0 aF-21.0 aF-20.0 aF-19.0 aF-19.0 aF-21.0 aF-21.0 aF-20.0 aF-20.0 aF-21.0 aF-21.0 aF-18.0 aF-20.0 aF-21.0 aF-19.0 aF-21.0 aF-20.0 aF-21.0 aF-20.0 aF-20.0 aF-21.0 aF-21.0 aF-19.0 aF-20.0 aF-18.0 aF-20.0 aF-21.0 aF-21.0 aF-21.0 aF-20.0 aF-21.0 aF-20.0 aF-19.0 aF-21.0 aF-20.0 aF-20.0 aF-20.0 aF-21.0 aF-20.0 aF-21.0 aF-17.0 aF-20.0 aF-20.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-18.0 aF-21.0 aF-21.0 aF-20.0 aF-21.0 aF-21.0 aF-20.0 aF-20.0 aF-20.0 aF-19.0 aF-19.0 aF-21.0 aF-20.0 aF-21.0 aF-19.0 aF-20.0 aF-21.0 aF-20.0 aF-20.0 aF-19.0 aF-21.0 aF-20.0 aF-19.0 aF-21.0 aF-19.0 aF-21.0 aF-20.0 aF-21.0 aF-21.0 aF-21.0 aF-20.0 aF-20.0 aF-21.0 aF-20.0 aF-19.0 aF-21.0 aF-21.0 aF-20.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-19.0 aF-21.0 aF-21.0 aF-21.0 aF-20.0 aF-19.0 aF-21.0 aF-19.0 aF-21.0 aF-21.0 aF-21.0 aF-19.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-20.0 aF-21.0 aF-21.0 aF-21.0 aF-20.0 aF-19.0 aF-20.0 aF-21.0 aF-20.0 aF-20.0 aF-20.0 aF-20.0 aF-21.0 aF-19.0 aF-21.0 aF-20.0 aF-21.0 aF-21.0 aF-21.0 aF-19.0 aF-21.0 aF-20.0 aF-20.0 aF-20.0 aF-20.0 aF-19.0 aF-21.0 aF-21.0 aF-20.0 aF-20.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-19.0 aF-20.0 aF-18.0 aF-21.0 aF-21.0 aF-20.0 aF-21.0 aF-19.0 aF-19.0 aF-20.0 aF-18.0 aF-20.0 aF-20.0 aF-21.0 aF-21.0 aF-19.0 aF-21.0 aF-19.0 aF-19.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-20.0 aF-20.0 aF-20.0 aF-21.0 aF-19.0 aF-20.0 aF-21.0 aF-20.0 aF-19.0 aF-19.0 aF-20.0 aF-20.0 aF-20.0 aF-21.0 aF-21.0 aF-18.0 aF-21.0 aF-21.0 aF-20.0 aF-20.0 aF-21.0 aF-21.0 aF-18.0 aF-19.0 aF-21.0 aF-21.0 aF-20.0 aF-20.0 aF-20.0 aF-19.0 aF-19.0 aF-21.0 aF-20.0 aF-20.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-20.0 aF-19.0 aF-20.0 aF-21.0 aF-20.0 aF-21.0 aF-19.0 aF-21.0 aF-21.0 aF-20.0 aF-18.0 aF-20.0 aF-20.0 aF-21.0 aF-19.0 aF-21.0 aF-19.0 aF-19.0 aF-21.0 aF-21.0 aF-21.0 aF-20.0 aF-21.0 aF-20.0 aF-21.0 aF-21.0 aF-20.0 aF-21.0 aF-19.0 aF-21.0 aF-21.0 aF-21.0 aF-20.0 aF-20.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-20.0 aF-20.0 aF-19.0 aF-21.0 aF-18.0 aF-20.0 aF-20.0 aF-17.0 aF-21.0 aF-21.0 aF-21.0 aF-19.0 aF-21.0 aF-21.0 aF-20.0 aF-20.0 aF-21.0 aF-21.0 aF-21.0 aF-20.0 aF-21.0 aF-21.0 aF-18.0 aF-21.0 aF-21.0 aF-21.0 aF-20.0 aF-20.0 aF-21.0 aF-21.0 aF-19.0 aF-20.0 aF-19.0 aF-21.0 aF-21.0 aF-20.0 aF-21.0 aF-21.0 aF-20.0 aF-21.0 aF-20.0 aF-19.0 aF-21.0 aF-21.0 aF-20.0 aF-21.0 aF-20.0 aF-20.0 aF-20.0 aF-20.0 aF-19.0 aF-20.0 aF-21.0 aF-20.0 aF-21.0 aF-20.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-20.0 aF-19.0 aF-20.0 aF-20.0 aF-20.0 aF-21.0 aF-20.0 aF-20.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-20.0 aF-21.0 aF-19.0 aF-21.0 aF-20.0 aF-20.0 aF-19.0 aF-20.0 aF-19.0 aF-21.0 aF-20.0 aF-21.0 aF-20.0 aF-17.0 aF-20.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 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F-19.0 tp23 a(I100000 g1 (g5 S'\x85\xebQ\xb8\x1eE4\xc0' p24 tp25 Rp26 g1 (g5 S'\x00\x00\x00\x00\x00@4\xc0' p27 tp28 Rp29 F-21.0 tp30 a(I110000 g1 (g5 S'{\x14\xaeG\xe1:4\xc0' p31 tp32 Rp33 g1 (g5 S'{\x14\xaeG\xe1:4\xc0' p34 tp35 Rp36 F-21.0 tp37 a(I120000 g1 (g5 S'\xc3\xf5(\\\x8fB4\xc0' p38 tp39 Rp40 g36 F-19.0 tp41 a(I130000 g1 (g5 S'{\x14\xaeG\xe1:4\xc0' p42 tp43 Rp44 g36 F-19.0 tp45 a(I140000 g1 (g5 S'\xc3\xf5(\\\x8fB4\xc0' p46 tp47 Rp48 g1 (g5 S'\xb8\x1e\x85\xebQ84\xc0' p49 tp50 Rp51 F-21.0 tp52 a(I150000 g1 (g5 S'H\xe1z\x14\xaeG4\xc0' p53 tp54 Rp55 g51 F-21.0 tp56 a(I160000 g1 (g5 S'R\xb8\x1e\x85\xebQ4\xc0' p57 tp58 Rp59 g51 F-20.0 tp60 a(I170000 g1 (g5 S'\x00\x00\x00\x00\x00@4\xc0' p61 tp62 Rp63 g51 F-20.0 tp64 a(I180000 g1 (g5 S'\n\xd7\xa3p=J4\xc0' p65 tp66 Rp67 g51 F-21.0 tp68 a(I190000 g1 (g5 S'\xd7\xa3p=\nW4\xc0' p69 tp70 Rp71 g51 F-21.0 tp72 a(I200000 g1 (g5 S'R\xb8\x1e\x85\xebQ4\xc0' p73 tp74 Rp75 g51 F-21.0 tp76 a(I210000 g1 (g5 S'\x14\xaeG\xe1zT4\xc0' p77 tp78 Rp79 g51 F-19.0 tp80 a(I220000 g1 (g5 S'\xd7\xa3p=\nW4\xc0' p81 tp82 Rp83 g51 F-20.0 tp84 a(I230000 g1 (g5 S'\x14\xaeG\xe1zT4\xc0' p85 tp86 Rp87 g51 F-20.0 tp88 a(I240000 g1 (g5 S'\x8f\xc2\xf5(\\O4\xc0' p89 tp90 Rp91 g51 F-20.0 tp92 a(I250000 g1 (g5 S'\xc3\xf5(\\\x8fB4\xc0' p93 tp94 Rp95 g51 F-18.0 tp96 a(I260000 g1 (g5 S'\x85\xebQ\xb8\x1eE4\xc0' p97 tp98 Rp99 g51 F-21.0 tp100 a(I270000 g1 (g5 S'=\n\xd7\xa3p=4\xc0' p101 tp102 Rp103 g51 F-20.0 tp104 a(I280000 g1 (g5 S'\x00\x00\x00\x00\x00@4\xc0' p105 tp106 Rp107 g51 F-20.0 tp108 a(I290000 g1 (g5 S'\xf6(\\\x8f\xc254\xc0' p109 tp110 Rp111 g1 (g5 S'3333334\xc0' p112 tp113 Rp114 F-20.0 tp115 a(I300000 g1 (g5 S')\\\x8f\xc2\xf5(4\xc0' p116 tp117 Rp118 g1 (g5 S')\\\x8f\xc2\xf5(4\xc0' p119 tp120 Rp121 F-17.0 tp122 a(I310000 g1 (g5 S'\xb8\x1e\x85\xebQ84\xc0' p123 tp124 Rp125 g121 F-21.0 tp126 a(I320000 g1 (g5 S'q=\n\xd7\xa304\xc0' p127 tp128 Rp129 g121 F-20.0 tp130 a(I330000 g1 (g5 S'\xc3\xf5(\\\x8fB4\xc0' p131 tp132 Rp133 g121 F-20.0 tp134 a(I340000 g1 (g5 S'\xf6(\\\x8f\xc254\xc0' p135 tp136 Rp137 g121 F-21.0 tp138 a(I350000 g1 (g5 S'\xc3\xf5(\\\x8fB4\xc0' p139 tp140 Rp141 g121 F-21.0 tp142 a(I360000 g1 (g5 S'\xecQ\xb8\x1e\x85+4\xc0' p143 tp144 Rp145 g121 F-19.0 tp146 a(I370000 g1 (g5 S'R\xb8\x1e\x85\xeb\x114\xc0' p147 tp148 Rp149 g1 (g5 S'R\xb8\x1e\x85\xeb\x114\xc0' p150 tp151 Rp152 F-19.0 tp153 a(I380000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x0c4\xc0' p154 tp155 Rp156 g1 (g5 S'\n\xd7\xa3p=\n4\xc0' p157 tp158 Rp159 F-19.0 tp160 a(I390000 g1 (g5 S'\xc3\xf5(\\\x8f\x024\xc0' p161 tp162 Rp163 g1 (g5 S'\xc3\xf5(\\\x8f\x024\xc0' p164 tp165 Rp166 F-19.0 tp167 a(I400000 g1 (g5 S')\\\x8f\xc2\xf5\xe83\xc0' p168 tp169 Rp170 g1 (g5 S')\\\x8f\xc2\xf5\xe83\xc0' p171 tp172 Rp173 F-18.0 tp174 a(I410000 g1 (g5 S'R\xb8\x1e\x85\xeb\xd13\xc0' p175 tp176 Rp177 g1 (g5 S'R\xb8\x1e\x85\xeb\xd13\xc0' p178 tp179 Rp180 F-19.0 tp181 a(I420000 g1 (g5 S')\\\x8f\xc2\xf5\xa83\xc0' p182 tp183 Rp184 g1 (g5 S')\\\x8f\xc2\xf5\xa83\xc0' p185 tp186 Rp187 F-20.0 tp188 a(I430000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f3\xc0' p189 tp190 Rp191 g1 (g5 S'\x8f\xc2\xf5(\\\x8f3\xc0' p192 tp193 Rp194 F-15.0 tp195 a(I440000 g1 (g5 S'\x1f\x85\xebQ\xb8^3\xc0' p196 tp197 Rp198 g1 (g5 S'\x1f\x85\xebQ\xb8^3\xc0' p199 tp200 Rp201 F-17.0 tp202 a(I450000 g1 (g5 S'\xaeG\xe1z\x14.3\xc0' p203 tp204 Rp205 g1 (g5 S'\xaeG\xe1z\x14.3\xc0' p206 tp207 Rp208 F-19.0 tp209 a(I460000 g1 (g5 S'\xd7\xa3p=\n\x173\xc0' p210 tp211 Rp212 g1 (g5 S'\xd7\xa3p=\n\x173\xc0' p213 tp214 Rp215 F-19.0 tp216 a(I470000 g1 (g5 S'fffff\xe62\xc0' p217 tp218 Rp219 g1 (g5 S'\xa4p=\n\xd7\xe32\xc0' p220 tp221 Rp222 F-20.0 tp223 a(I480000 g1 (g5 S'H\xe1z\x14\xae\xc72\xc0' p224 tp225 Rp226 g1 (g5 S'H\xe1z\x14\xae\xc72\xc0' p227 tp228 Rp229 F-18.0 tp230 a(I490000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb82\xc0' p231 tp232 Rp233 g1 (g5 S'\xf6(\\\x8f\xc2\xb52\xc0' p234 tp235 Rp236 F-19.0 tp237 a(I500000 g1 (g5 S'\xc3\xf5(\\\x8f\x822\xc0' p238 tp239 Rp240 g1 (g5 S'\xc3\xf5(\\\x8f\x822\xc0' p241 tp242 Rp243 F-17.0 tp244 a(I510000 g1 (g5 S'q=\n\xd7\xa3p2\xc0' p245 tp246 Rp247 g1 (g5 S'q=\n\xd7\xa3p2\xc0' p248 tp249 Rp250 F-21.0 tp251 a(I520000 g1 (g5 S'\xaeG\xe1z\x14.2\xc0' p252 tp253 Rp254 g1 (g5 S'\xaeG\xe1z\x14.2\xc0' p255 tp256 Rp257 F-14.0 tp258 a(I530000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x0c2\xc0' p259 tp260 Rp261 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x0c2\xc0' p262 tp263 Rp264 F-19.0 tp265 a(I540000 g1 (g5 S'q=\n\xd7\xa3\xf01\xc0' p266 tp267 Rp268 g1 (g5 S'q=\n\xd7\xa3\xf01\xc0' p269 tp270 Rp271 F-18.0 tp272 a(I550000 g1 (g5 S'\xe1z\x14\xaeG\xe11\xc0' p273 tp274 Rp275 g1 (g5 S'\xe1z\x14\xaeG\xe11\xc0' p276 tp277 Rp278 F-17.0 tp279 a(I560000 g1 (g5 S'\x1f\x85\xebQ\xb8\xde1\xc0' p280 tp281 Rp282 g1 (g5 S'\\\x8f\xc2\xf5(\xdc1\xc0' p283 tp284 Rp285 F-18.0 tp286 a(I570000 g1 (g5 S'H\xe1z\x14\xae\xc71\xc0' p287 tp288 Rp289 g1 (g5 S'H\xe1z\x14\xae\xc71\xc0' p290 tp291 Rp292 F-16.0 tp293 a(I580000 g1 (g5 S'\xe1z\x14\xaeG\xa11\xc0' p294 tp295 Rp296 g1 (g5 S'\xe1z\x14\xaeG\xa11\xc0' p297 tp298 Rp299 F-18.0 tp300 a(I590000 g1 (g5 S'{\x14\xaeG\xe1\xba1\xc0' p301 tp302 Rp303 g299 F-18.0 tp304 a(I600000 g1 (g5 S')\\\x8f\xc2\xf5\xa81\xc0' p305 tp306 Rp307 g299 F-17.0 tp308 a(I610000 g1 (g5 S'\xd7\xa3p=\n\x971\xc0' p309 tp310 Rp311 g1 (g5 S'\xd7\xa3p=\n\x971\xc0' p312 tp313 Rp314 F-16.0 tp315 a(I620000 g1 (g5 S'=\n\xd7\xa3p}1\xc0' p316 tp317 Rp318 g1 (g5 S'=\n\xd7\xa3p}1\xc0' p319 tp320 Rp321 F-15.0 tp322 a(I630000 g1 (g5 S'\xb8\x1e\x85\xebQx1\xc0' p323 tp324 Rp325 g1 (g5 S'\xaeG\xe1z\x14n1\xc0' p326 tp327 Rp328 F-17.0 tp329 a(I640000 g1 (g5 S'\x9a\x99\x99\x99\x99Y1\xc0' p330 tp331 Rp332 g1 (g5 S'\x9a\x99\x99\x99\x99Y1\xc0' p333 tp334 Rp335 F-16.0 tp336 a(I650000 g1 (g5 S'H\xe1z\x14\xaeG1\xc0' p337 tp338 Rp339 g1 (g5 S'H\xe1z\x14\xaeG1\xc0' p340 tp341 Rp342 F-16.0 tp343 a(I660000 g1 (g5 S'\xaeG\xe1z\x14.1\xc0' p344 tp345 Rp346 g1 (g5 S'\xa4p=\n\xd7#1\xc0' p347 tp348 Rp349 F-15.0 tp350 a(I670000 g1 (g5 S'\\\x8f\xc2\xf5(\x1c1\xc0' p351 tp352 Rp353 g1 (g5 S'\\\x8f\xc2\xf5(\x1c1\xc0' p354 tp355 Rp356 F-18.0 tp357 a(I680000 g1 (g5 S'=\n\xd7\xa3p\xfd0\xc0' p358 tp359 Rp360 g1 (g5 S'=\n\xd7\xa3p\xfd0\xc0' p361 tp362 Rp363 F-16.0 tp364 a(I690000 g1 (g5 S'\x8f\xc2\xf5(\\\x0f1\xc0' p365 tp366 Rp367 g363 F-18.0 tp368 a(I700000 g1 (g5 S'q=\n\xd7\xa3\xf00\xc0' p369 tp370 Rp371 g1 (g5 S'q=\n\xd7\xa3\xf00\xc0' p372 tp373 Rp374 F-15.0 tp375 a(I710000 g1 (g5 S'\xd7\xa3p=\n\xd70\xc0' p376 tp377 Rp378 g1 (g5 S'\xd7\xa3p=\n\xd70\xc0' p379 tp380 Rp381 F-14.0 tp382 a(I720000 g1 (g5 S'H\xe1z\x14\xae\xc70\xc0' p383 tp384 Rp385 g1 (g5 S'H\xe1z\x14\xae\xc70\xc0' p386 tp387 Rp388 F-14.0 tp389 a(I730000 g1 (g5 S'=\n\xd7\xa3p\xbd0\xc0' p390 tp391 Rp392 g1 (g5 S'\xb8\x1e\x85\xebQ\xb80\xc0' p393 tp394 Rp395 F-19.0 tp396 a(I740000 g1 (g5 S'fffff\xa60\xc0' p397 tp398 Rp399 g1 (g5 S'fffff\xa60\xc0' p400 tp401 Rp402 F-15.0 tp403 a(I750000 g1 (g5 S'\\\x8f\xc2\xf5(\x9c0\xc0' p404 tp405 Rp406 g1 (g5 S'\\\x8f\xc2\xf5(\x9c0\xc0' p407 tp408 Rp409 F-14.0 tp410 a(I760000 g1 (g5 S'\xc3\xf5(\\\x8f\x820\xc0' p411 tp412 Rp413 g1 (g5 S'\xc3\xf5(\\\x8f\x820\xc0' p414 tp415 Rp416 F-15.0 tp417 a(I770000 g1 (g5 S'\x14\xaeG\xe1zT0\xc0' p418 tp419 Rp420 g1 (g5 S'\xcd\xcc\xcc\xcc\xccL0\xc0' p421 tp422 Rp423 F-20.0 tp424 a(I780000 g1 (g5 S'\x1f\x85\xebQ\xb8^0\xc0' p425 tp426 Rp427 g423 F-17.0 tp428 a(I790000 g1 (g5 S'\x8f\xc2\xf5(\\O0\xc0' p429 tp430 Rp431 g423 F-12.0 tp432 a(I800000 g1 (g5 S'\xecQ\xb8\x1e\x85+0\xc0' p433 tp434 Rp435 g1 (g5 S'\xecQ\xb8\x1e\x85+0\xc0' p436 tp437 Rp438 F-14.0 tp439 a(I810000 g1 (g5 S'\x85\xebQ\xb8\x1e\x050\xc0' p440 tp441 Rp442 g1 (g5 S'\x85\xebQ\xb8\x1e\x050\xc0' p443 tp444 Rp445 F-10.0 tp446 a(I820000 g1 (g5 S'\x00\x00\x00\x00\x00\x000\xc0' p447 tp448 Rp449 g1 (g5 S'\\\x8f\xc2\xf5(\xdc/\xc0' p450 tp451 Rp452 F-19.0 tp453 a(I830000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8/\xc0' p454 tp455 Rp456 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8/\xc0' p457 tp458 Rp459 F-7.0 tp460 a(I840000 g1 (g5 S'\n\xd7\xa3p=\x8a/\xc0' p461 tp462 Rp463 g1 (g5 S'\n\xd7\xa3p=\x8a/\xc0' p464 tp465 Rp466 F-17.0 tp467 a(I850000 g1 (g5 S'\xb8\x1e\x85\xebQ8/\xc0' p468 tp469 Rp470 g1 (g5 S'\xa4p=\n\xd7#/\xc0' p471 tp472 Rp473 F-17.0 tp474 a(I860000 g1 (g5 S'\xa4p=\n\xd7#/\xc0' p475 tp476 Rp477 g1 (g5 S'\x9a\x99\x99\x99\x99\x19/\xc0' p478 tp479 Rp480 F-17.0 tp481 a(I870000 g1 (g5 S'\\\x8f\xc2\xf5(\xdc.\xc0' p482 tp483 Rp484 g1 (g5 S'\\\x8f\xc2\xf5(\xdc.\xc0' p485 tp486 Rp487 F-18.0 tp488 a(I880000 g1 (g5 S'\xc3\xf5(\\\x8f\xc2.\xc0' p489 tp490 Rp491 g1 (g5 S'\xc3\xf5(\\\x8f\xc2.\xc0' p492 tp493 Rp494 F-16.0 tp495 a(I890000 g1 (g5 S'\x9a\x99\x99\x99\x99\x19.\xc0' p496 tp497 Rp498 g1 (g5 S'\x9a\x99\x99\x99\x99\x19.\xc0' p499 tp500 Rp501 F-11.0 tp502 a(I900000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb-\xc0' p503 tp504 Rp505 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb-\xc0' p506 tp507 Rp508 F-11.0 tp509 a(I910000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8-\xc0' p510 tp511 Rp512 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8-\xc0' p513 tp514 Rp515 F-15.0 tp516 a(I920000 g1 (g5 S'\xd7\xa3p=\nW-\xc0' p517 tp518 Rp519 g1 (g5 S'\xd7\xa3p=\nW-\xc0' p520 tp521 Rp522 F-12.0 tp523 a(I930000 g1 (g5 S'\xe1z\x14\xaeG\xe1,\xc0' p524 tp525 Rp526 g1 (g5 S'\xd7\xa3p=\n\xd7,\xc0' p527 tp528 Rp529 F-17.0 tp530 a(I940000 g1 (g5 S'\n\xd7\xa3p=\x8a,\xc0' p531 tp532 Rp533 g1 (g5 S'\n\xd7\xa3p=\x8a,\xc0' p534 tp535 Rp536 F-17.0 tp537 a(I950000 g1 (g5 S'\x00\x00\x00\x00\x00\x00,\xc0' p538 tp539 Rp540 g1 (g5 S'\x00\x00\x00\x00\x00\x00,\xc0' p541 tp542 Rp543 F-7.0 tp544 a(I960000 g1 (g5 S'\xa4p=\n\xd7\xa3+\xc0' p545 tp546 Rp547 g1 (g5 S'\xa4p=\n\xd7\xa3+\xc0' p548 tp549 Rp550 F-11.0 tp551 a(I970000 g1 (g5 S'{\x14\xaeG\xe1\xfa*\xc0' p552 tp553 Rp554 g1 (g5 S'{\x14\xaeG\xe1\xfa*\xc0' p555 tp556 Rp557 F-8.0 tp558 a(I980000 g1 (g5 S'\xaeG\xe1z\x14.*\xc0' p559 tp560 Rp561 g1 (g5 S'\xaeG\xe1z\x14.*\xc0' p562 tp563 Rp564 F-9.0 tp565 a(I990000 g1 (g5 S'=\n\xd7\xa3p\xbd)\xc0' p566 tp567 Rp568 g1 (g5 S'=\n\xd7\xa3p\xbd)\xc0' p569 tp570 Rp571 F-6.0 tp572 a(I1000000 g1 (g5 S'H\xe1z\x14\xaeG)\xc0' p573 tp574 Rp575 g1 (g5 S'H\xe1z\x14\xaeG)\xc0' p576 tp577 Rp578 F-13.0 tp579 a(I1010000 g1 (g5 S'\xc3\xf5(\\\x8f\xc2(\xc0' p580 tp581 Rp582 g1 (g5 S'\xc3\xf5(\\\x8f\xc2(\xc0' p583 tp584 Rp585 F-11.0 tp586 a(I1020000 g1 (g5 S'333333(\xc0' p587 tp588 Rp589 g1 (g5 S'333333(\xc0' p590 tp591 Rp592 F-1.0 tp593 a(I1030000 g1 (g5 S"q=\n\xd7\xa3p'\xc0" p594 tp595 Rp596 g1 (g5 S"q=\n\xd7\xa3p'\xc0" p597 tp598 Rp599 F5.0 tp600 a(I1040000 g1 (g5 S'\xe1z\x14\xaeG\xe1&\xc0' p601 tp602 Rp603 g1 (g5 S'\xe1z\x14\xaeG\xe1&\xc0' p604 tp605 Rp606 F-8.0 tp607 a(I1050000 g1 (g5 S'ffffff&\xc0' p608 tp609 Rp610 g1 (g5 S'ffffff&\xc0' p611 tp612 Rp613 F2.0 tp614 a(I1060000 g1 (g5 S'\xa4p=\n\xd7#&\xc0' p615 tp616 Rp617 g1 (g5 S'\xa4p=\n\xd7#&\xc0' p618 tp619 Rp620 F-8.0 tp621 a(I1070000 g1 (g5 S')\\\x8f\xc2\xf5\xa8%\xc0' p622 tp623 Rp624 g1 (g5 S')\\\x8f\xc2\xf5\xa8%\xc0' p625 tp626 Rp627 F-11.0 tp628 a(I1080000 g1 (g5 S'\xcd\xcc\xcc\xcc\xccL%\xc0' p629 tp630 Rp631 g1 (g5 S'\xcd\xcc\xcc\xcc\xccL%\xc0' p632 tp633 Rp634 F-2.0 tp635 a(I1090000 g1 (g5 S'\xc3\xf5(\\\x8fB$\xc0' p636 tp637 Rp638 g1 (g5 S'\xc3\xf5(\\\x8fB$\xc0' p639 tp640 Rp641 F-5.0 tp642 a(I1100000 g1 (g5 S'\x00\x00\x00\x00\x00\x80#\xc0' p643 tp644 Rp645 g1 (g5 S'\x00\x00\x00\x00\x00\x80#\xc0' p646 tp647 Rp648 F-5.0 tp649 a(I1110000 g1 (g5 S'\xb8\x1e\x85\xebQ8#\xc0' p650 tp651 Rp652 g1 (g5 S'\xb8\x1e\x85\xebQ8#\xc0' p653 tp654 Rp655 F4.0 tp656 a(I1120000 g1 (g5 S'\x14\xaeG\xe1z\x94"\xc0' p657 tp658 Rp659 g1 (g5 S'\x14\xaeG\xe1z\x94"\xc0' p660 tp661 Rp662 F-9.0 tp663 a(I1130000 g1 (g5 S'{\x14\xaeG\xe1z"\xc0' p664 tp665 Rp666 g1 (g5 S'{\x14\xaeG\xe1z"\xc0' p667 tp668 Rp669 F-2.0 tp670 a(I1140000 g1 (g5 S'\xd7\xa3p=\nW"\xc0' p671 tp672 Rp673 g1 (g5 S'\xd7\xa3p=\nW"\xc0' p674 tp675 Rp676 F-10.0 tp677 a(I1150000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f!\xc0' p678 tp679 Rp680 g1 (g5 S'\x8f\xc2\xf5(\\\x8f!\xc0' p681 tp682 Rp683 F4.0 tp684 a(I1160000 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e!\xc0' p685 tp686 Rp687 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e!\xc0' p688 tp689 Rp690 F-12.0 tp691 a(I1170000 g1 (g5 S'333333 \xc0' p692 tp693 Rp694 g1 (g5 S'333333 \xc0' p695 tp696 Rp697 F2.0 tp698 a(I1180000 g1 (g5 S'\xaeG\xe1z\x14\xae\x1e\xc0' p699 tp700 Rp701 g1 (g5 S'\xaeG\xe1z\x14\xae\x1e\xc0' p702 tp703 Rp704 F7.0 tp705 a(I1190000 g1 (g5 S'H\xe1z\x14\xaeG\x1d\xc0' p706 tp707 Rp708 g1 (g5 S'H\xe1z\x14\xaeG\x1d\xc0' p709 tp710 Rp711 F3.0 tp712 a(I1200000 g1 (g5 S'{\x14\xaeG\xe1z\x1c\xc0' p713 tp714 Rp715 g1 (g5 S'{\x14\xaeG\xe1z\x1c\xc0' p716 tp717 Rp718 F4.0 tp719 a(I1210000 g1 (g5 S'q=\n\xd7\xa3p\x1a\xc0' p720 tp721 Rp722 g1 (g5 S'q=\n\xd7\xa3p\x1a\xc0' p723 tp724 Rp725 F5.0 tp726 a(I1220000 g1 (g5 S'H\xe1z\x14\xaeG\x19\xc0' p727 tp728 Rp729 g1 (g5 S'H\xe1z\x14\xaeG\x19\xc0' p730 tp731 Rp732 F-6.0 tp733 a(I1230000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x18\xc0' p734 tp735 Rp736 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x18\xc0' p737 tp738 Rp739 F8.0 tp740 a(I1240000 g1 (g5 S'\n\xd7\xa3p=\n\x16\xc0' p741 tp742 Rp743 g1 (g5 S'\n\xd7\xa3p=\n\x16\xc0' p744 tp745 Rp746 F1.0 tp747 a(I1250000 g1 (g5 S'\x14\xaeG\xe1z\x14\x15\xc0' p748 tp749 Rp750 g1 (g5 S'\x14\xaeG\xe1z\x14\x15\xc0' p751 tp752 Rp753 F-3.0 tp754 a(I1260000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\x13\xc0' p755 tp756 Rp757 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\x13\xc0' p758 tp759 Rp760 F1.0 tp761 a(I1270000 g1 (g5 S'H\xe1z\x14\xaeG\x12\xc0' p762 tp763 Rp764 g1 (g5 S'H\xe1z\x14\xaeG\x12\xc0' p765 tp766 Rp767 F-7.0 tp768 a(I1280000 g1 (g5 S'\x14\xaeG\xe1z\x14\x11\xc0' p769 tp770 Rp771 g1 (g5 S'\x14\xaeG\xe1z\x14\x11\xc0' p772 tp773 Rp774 F1.0 tp775 a(I1290000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f\x10\xc0' p776 tp777 Rp778 g1 (g5 S'\x85\xebQ\xb8\x1e\x85\x10\xc0' p779 tp780 Rp781 F-10.0 tp782 a(I1300000 g1 (g5 S'\xf6(\\\x8f\xc2\xf5\x0e\xc0' p783 tp784 Rp785 g1 (g5 S'\xf6(\\\x8f\xc2\xf5\x0e\xc0' p786 tp787 Rp788 F9.0 tp789 a(I1310000 g1 (g5 S'q=\n\xd7\xa3p\r\xc0' p790 tp791 Rp792 g1 (g5 S'q=\n\xd7\xa3p\r\xc0' p793 tp794 Rp795 F4.0 tp796 a(I1320000 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e\x0b\xc0' p797 tp798 Rp799 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e\x0b\xc0' p800 tp801 Rp802 F4.0 tp803 a(I1330000 g1 (g5 S'\xe1z\x14\xaeG\xe1\x08\xc0' p804 tp805 Rp806 g1 (g5 S'\xe1z\x14\xaeG\xe1\x08\xc0' p807 tp808 Rp809 F-5.0 tp810 a(I1340000 g1 (g5 S'\x14\xaeG\xe1z\x14\x08\xc0' p811 tp812 Rp813 g1 (g5 S'\xc3\xf5(\\\x8f\xc2\x07\xc0' p814 tp815 Rp816 F-6.0 tp817 a(I1350000 g1 (g5 S'\x14\xaeG\xe1z\x14\x06\xc0' p818 tp819 Rp820 g1 (g5 S'\x14\xaeG\xe1z\x14\x06\xc0' p821 tp822 Rp823 F7.0 tp824 a(I1360000 g1 (g5 S'\x85\xebQ\xb8\x1e\x85\x03\xc0' p825 tp826 Rp827 g1 (g5 S'\x85\xebQ\xb8\x1e\x85\x03\xc0' p828 tp829 Rp830 F7.0 tp831 a(I1370000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\xc0' p832 tp833 Rp834 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x00\xc0' p835 tp836 Rp837 F7.0 tp838 a(I1380000 g1 (g5 S'R\xb8\x1e\x85\xebQ\xfc\xbf' p839 tp840 Rp841 g1 (g5 S'R\xb8\x1e\x85\xebQ\xfc\xbf' p842 tp843 Rp844 F3.0 tp845 a(I1390000 g1 (g5 S'333333\xf7\xbf' p846 tp847 Rp848 g1 (g5 S'333333\xf7\xbf' p849 tp850 Rp851 F6.0 tp852 a(I1400000 g1 (g5 S'H\xe1z\x14\xaeG\xf5\xbf' p853 tp854 Rp855 g1 (g5 S'H\xe1z\x14\xaeG\xf5\xbf' p856 tp857 Rp858 F-2.0 tp859 a(I1410000 g1 (g5 S'ffffff\xf2\xbf' p860 tp861 Rp862 g1 (g5 S'ffffff\xf2\xbf' p863 tp864 Rp865 F5.0 tp866 a(I1420000 g1 (g5 S'R\xb8\x1e\x85\xebQ\xe8\xbf' p867 tp868 Rp869 g1 (g5 S'R\xb8\x1e\x85\xebQ\xe8\xbf' p870 tp871 Rp872 F2.0 tp873 a(I1430000 g1 (g5 S'{\x14\xaeG\xe1z\xe4\xbf' p874 tp875 Rp876 g1 (g5 S'{\x14\xaeG\xe1z\xe4\xbf' p877 tp878 Rp879 F3.0 tp880 a(I1440000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xd9\xbf' p881 tp882 Rp883 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xd9\xbf' p884 tp885 Rp886 F9.0 tp887 a(I1450000 g1 (g5 S'=\n\xd7\xa3p=\xda\xbf' p888 tp889 Rp890 g1 (g5 S'\xc3\xf5(\\\x8f\xc2\xd5\xbf' p891 tp892 Rp893 F-4.0 tp894 a(I1460000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xb9\xbf' p895 tp896 Rp897 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xb9\xbf' p898 tp899 Rp900 F5.0 tp901 a(I1470000 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xc9?' p902 tp903 Rp904 g1 (g5 S'\x9a\x99\x99\x99\x99\x99\xc9?' p905 tp906 Rp907 F5.0 tp908 a(I1480000 g1 (g5 S'=\n\xd7\xa3p=\xda?' p909 tp910 Rp911 g1 (g5 S'=\n\xd7\xa3p=\xda?' p912 tp913 Rp914 F6.0 tp915 a(I1490000 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xf1?' p916 tp917 Rp918 g1 (g5 S'\xecQ\xb8\x1e\x85\xeb\xf1?' p919 tp920 Rp921 F12.0 tp922 a(I1500000 g1 (g5 S')\\\x8f\xc2\xf5(\xf8?' p923 tp924 Rp925 g1 (g5 S')\\\x8f\xc2\xf5(\xf8?' p926 tp927 Rp928 F-5.0 tp929 a(I1510000 g1 (g5 S'ffffff\xfe?' p930 tp931 Rp932 g1 (g5 S'ffffff\xfe?' p933 tp934 Rp935 F-2.0 tp936 a(I1520000 g1 (g5 S'\xd7\xa3p=\n\xd7\x01@' p937 tp938 Rp939 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x02@' p940 tp941 Rp942 F2.0 tp943 a(I1530000 g1 (g5 S'{\x14\xaeG\xe1z\x04@' p944 tp945 Rp946 g1 (g5 S'{\x14\xaeG\xe1z\x04@' p947 tp948 Rp949 F5.0 tp950 a(I1540000 g1 (g5 S')\\\x8f\xc2\xf5(\x06@' p951 tp952 Rp953 g1 (g5 S')\\\x8f\xc2\xf5(\x06@' p954 tp955 Rp956 F10.0 tp957 a(I1550000 g1 (g5 S'\xe1z\x14\xaeG\xe1\x08@' p958 tp959 Rp960 g1 (g5 S'\xe1z\x14\xaeG\xe1\x08@' p961 tp962 Rp963 F16.0 tp964 a(I1560000 g1 (g5 S'\xa4p=\n\xd7\xa3\x0c@' p965 tp966 Rp967 g1 (g5 S'\xa4p=\n\xd7\xa3\x0c@' p968 tp969 Rp970 F6.0 tp971 a(I1570000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc\x0e@' p972 tp973 Rp974 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc\x0e@' p975 tp976 Rp977 F14.0 tp978 a(I1580000 g1 (g5 S'\xe1z\x14\xaeG\xe1\x10@' p979 tp980 Rp981 g1 (g5 S'\xe1z\x14\xaeG\xe1\x10@' p982 tp983 Rp984 F9.0 tp985 a(I1590000 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x13@' p986 tp987 Rp988 g1 (g5 S'\x00\x00\x00\x00\x00\x00\x13@' p989 tp990 Rp991 F12.0 tp992 a(I1600000 g1 (g5 S'H\xe1z\x14\xaeG\x14@' p993 tp994 Rp995 g1 (g5 S'H\xe1z\x14\xaeG\x14@' p996 tp997 Rp998 F12.0 tp999 a(I1610000 g1 (g5 S'\xd7\xa3p=\n\xd7\x16@' p1000 tp1001 Rp1002 g1 (g5 S'\xd7\xa3p=\n\xd7\x16@' p1003 tp1004 Rp1005 F14.0 tp1006 a(I1620000 g1 (g5 S'\xe1z\x14\xaeG\xe1\x17@' p1007 tp1008 Rp1009 g1 (g5 S'\xe1z\x14\xaeG\xe1\x17@' p1010 tp1011 Rp1012 F10.0 tp1013 a(I1630000 g1 (g5 S'\\\x8f\xc2\xf5(\\\x19@' p1014 tp1015 Rp1016 g1 (g5 S'\\\x8f\xc2\xf5(\\\x19@' p1017 tp1018 Rp1019 F10.0 tp1020 a(I1640000 g1 (g5 S'\n\xd7\xa3p=\n\x1c@' p1021 tp1022 Rp1023 g1 (g5 S'\n\xd7\xa3p=\n\x1c@' p1024 tp1025 Rp1026 F15.0 tp1027 a(I1650000 g1 (g5 S'\\\x8f\xc2\xf5(\\\x1e@' p1028 tp1029 Rp1030 g1 (g5 S'\\\x8f\xc2\xf5(\\\x1e@' p1031 tp1032 Rp1033 F17.0 tp1034 a(I1660000 g1 (g5 S'\xe1z\x14\xaeGa @' p1035 tp1036 Rp1037 g1 (g5 S'\xe1z\x14\xaeGa @' p1038 tp1039 Rp1040 F17.0 tp1041 a(I1670000 g1 (g5 S'fffff\xe6!@' p1042 tp1043 Rp1044 g1 (g5 S'fffff\xe6!@' p1045 tp1046 Rp1047 F14.0 tp1048 a(I1680000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8"@' p1049 tp1050 Rp1051 g1 (g5 S'\xb8\x1e\x85\xebQ\xb8"@' p1052 tp1053 Rp1054 F19.0 tp1055 a(I1690000 g1 (g5 S'\xe1z\x14\xaeG\xe1#@' p1056 tp1057 Rp1058 g1 (g5 S'\xe1z\x14\xaeG\xe1#@' p1059 tp1060 Rp1061 F14.0 tp1062 a(I1700000 g1 (g5 S'q=\n\xd7\xa3\xf0$@' p1063 tp1064 Rp1065 g1 (g5 S'q=\n\xd7\xa3\xf0$@' p1066 tp1067 Rp1068 F17.0 tp1069 a(I1710000 g1 (g5 S'H\xe1z\x14\xaeG&@' p1070 tp1071 Rp1072 g1 (g5 S'H\xe1z\x14\xaeG&@' p1073 tp1074 Rp1075 F20.0 tp1076 a(I1720000 g1 (g5 S")\\\x8f\xc2\xf5('@" p1077 tp1078 Rp1079 g1 (g5 S")\\\x8f\xc2\xf5('@" p1080 tp1081 Rp1082 F16.0 tp1083 a(I1730000 g1 (g5 S'\xd7\xa3p=\nW(@' p1084 tp1085 Rp1086 g1 (g5 S'\xd7\xa3p=\nW(@' p1087 tp1088 Rp1089 F16.0 tp1090 a(I1740000 g1 (g5 S')\\\x8f\xc2\xf5()@' p1091 tp1092 Rp1093 g1 (g5 S')\\\x8f\xc2\xf5()@' p1094 tp1095 Rp1096 F16.0 tp1097 a(I1750000 g1 (g5 S'\xcd\xcc\xcc\xcc\xccL*@' p1098 tp1099 Rp1100 g1 (g5 S'\xcd\xcc\xcc\xcc\xccL*@' p1101 tp1102 Rp1103 F18.0 tp1104 a(I1760000 g1 (g5 S'333333+@' p1105 tp1106 Rp1107 g1 (g5 S'333333+@' p1108 tp1109 Rp1110 F18.0 tp1111 a(I1770000 g1 (g5 S'q=\n\xd7\xa3p,@' p1112 tp1113 Rp1114 g1 (g5 S'q=\n\xd7\xa3p,@' p1115 tp1116 Rp1117 F14.0 tp1118 a(I1780000 g1 (g5 S')\\\x8f\xc2\xf5(-@' p1119 tp1120 Rp1121 g1 (g5 S')\\\x8f\xc2\xf5(-@' p1122 tp1123 Rp1124 F17.0 tp1125 a(I1790000 g1 (g5 S'\xecQ\xb8\x1e\x85k-@' p1126 tp1127 Rp1128 g1 (g5 S'q=\n\xd7\xa3p-@' p1129 tp1130 Rp1131 F15.0 tp1132 a(I1800000 g1 (g5 S'H\xe1z\x14\xaeG.@' p1133 tp1134 Rp1135 g1 (g5 S'H\xe1z\x14\xaeG.@' p1136 tp1137 Rp1138 F18.0 tp1139 a(I1810000 g1 (g5 S'\xe1z\x14\xaeGa.@' p1140 tp1141 Rp1142 g1 (g5 S'\xe1z\x14\xaeGa.@' p1143 tp1144 Rp1145 F20.0 tp1146 a(I1820000 g1 (g5 S'=\n\xd7\xa3p\xbd.@' p1147 tp1148 Rp1149 g1 (g5 S'=\n\xd7\xa3p\xbd.@' p1150 tp1151 Rp1152 F17.0 tp1153 a(I1830000 g1 (g5 S'R\xb8\x1e\x85\xebQ/@' p1154 tp1155 Rp1156 g1 (g5 S'\xd7\xa3p=\nW/@' p1157 tp1158 Rp1159 F13.0 tp1160 a(I1840000 g1 (g5 S'\x14\xaeG\xe1z\x94/@' p1161 tp1162 Rp1163 g1 (g5 S'\x14\xaeG\xe1z\x94/@' p1164 tp1165 Rp1166 F15.0 tp1167 a(I1850000 g1 (g5 S'\n\xd7\xa3p=\n0@' p1168 tp1169 Rp1170 g1 (g5 S'\n\xd7\xa3p=\n0@' p1171 tp1172 Rp1173 F18.0 tp1174 a(I1860000 g1 (g5 S'=\n\xd7\xa3p=0@' p1175 tp1176 Rp1177 g1 (g5 S'=\n\xd7\xa3p=0@' p1178 tp1179 Rp1180 F16.0 tp1181 a(I1870000 g1 (g5 S'\xecQ\xb8\x1e\x85+0@' p1182 tp1183 Rp1184 g1180 F15.0 tp1185 a(I1880000 g1 (g5 S'3333330@' p1186 tp1187 Rp1188 g1 (g5 S'\x00\x00\x00\x00\x00@0@' p1189 tp1190 Rp1191 F12.0 tp1192 a(I1890000 g1 (g5 S'=\n\xd7\xa3p=0@' p1193 tp1194 Rp1195 g1191 F16.0 tp1196 a(I1900000 g1 (g5 S'\n\xd7\xa3p=J0@' p1197 tp1198 Rp1199 g1 (g5 S'\x9a\x99\x99\x99\x99Y0@' p1200 tp1201 Rp1202 F13.0 tp1203 a(I1910000 g1 (g5 S'\xcd\xcc\xcc\xcc\xccL0@' p1204 tp1205 Rp1206 g1202 F12.0 tp1207 a(I1920000 g1 (g5 S'\x00\x00\x00\x00\x00@0@' p1208 tp1209 Rp1210 g1202 F14.0 tp1211 a(I1930000 g1 (g5 S'\xcd\xcc\xcc\xcc\xccL0@' p1212 tp1213 Rp1214 g1202 F19.0 tp1215 a(I1940000 g1 (g5 S'ffffff0@' p1216 tp1217 Rp1218 g1 (g5 S'ffffff0@' p1219 tp1220 Rp1221 F18.0 tp1222 a(I1950000 g1 (g5 S'\xecQ\xb8\x1e\x85k0@' p1223 tp1224 Rp1225 g1 (g5 S'\xecQ\xb8\x1e\x85k0@' p1226 tp1227 Rp1228 F19.0 tp1229 a(I1960000 g1 (g5 S'\x00\x00\x00\x00\x00\x800@' p1230 tp1231 Rp1232 g1 (g5 S'\x00\x00\x00\x00\x00\x800@' p1233 tp1234 Rp1235 F18.0 tp1236 a(I1970000 g1 (g5 S'\xb8\x1e\x85\xebQx0@' p1237 tp1238 Rp1239 g1235 F18.0 tp1240 a(I1980000 g1 (g5 S'\xf6(\\\x8f\xc2u0@' p1241 tp1242 Rp1243 g1235 F20.0 tp1244 a(I1990000 g1 (g5 S'33333s0@' p1245 tp1246 Rp1247 g1235 F20.0 tp1248 a(I2000000 g1 (g5 S'\xf6(\\\x8f\xc2u0@' p1249 tp1250 Rp1251 g1235 F14.0 tp1252 a(I2010000 g1 (g5 S'\xc3\xf5(\\\x8f\x820@' p1253 tp1254 Rp1255 g1 (g5 S'\xc3\xf5(\\\x8f\x820@' p1256 tp1257 Rp1258 F17.0 tp1259 a(I2020000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f0@' p1260 tp1261 Rp1262 g1 (g5 S'R\xb8\x1e\x85\xeb\x910@' p1263 tp1264 Rp1265 F16.0 tp1266 a(I2030000 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e0@' p1267 tp1268 Rp1269 g1 (g5 S'\xe1z\x14\xaeG\xa10@' p1270 tp1271 Rp1272 F17.0 tp1273 a(I2040000 g1 (g5 S'\xa4p=\n\xd7\xa30@' p1274 tp1275 Rp1276 g1 (g5 S')\\\x8f\xc2\xf5\xa80@' p1277 tp1278 Rp1279 F20.0 tp1280 a(I2050000 g1 (g5 S'\xaeG\xe1z\x14\xae0@' p1281 tp1282 Rp1283 g1 (g5 S'\xaeG\xe1z\x14\xae0@' p1284 tp1285 Rp1286 F17.0 tp1287 a(I2060000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc50@' p1288 tp1289 Rp1290 g1 (g5 S'\x85\xebQ\xb8\x1e\xc50@' p1291 tp1292 Rp1293 F18.0 tp1294 a(I2070000 g1 (g5 S'\xc3\xf5(\\\x8f\xc20@' p1295 tp1296 Rp1297 g1 (g5 S'\x8f\xc2\xf5(\\\xcf0@' p1298 tp1299 Rp1300 F17.0 tp1301 a(I2080000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc50@' p1302 tp1303 Rp1304 g1300 F17.0 tp1305 a(I2090000 g1 (g5 S'\n\xd7\xa3p=\xca0@' p1306 tp1307 Rp1308 g1 (g5 S'\x14\xaeG\xe1z\xd40@' p1309 tp1310 Rp1311 F20.0 tp1312 a(I2100000 g1 (g5 S'\xaeG\xe1z\x14\xee0@' p1313 tp1314 Rp1315 g1 (g5 S'\xaeG\xe1z\x14\xee0@' p1316 tp1317 Rp1318 F18.0 tp1319 a(I2110000 g1 (g5 S'33333\xf30@' p1320 tp1321 Rp1322 g1 (g5 S'\xf6(\\\x8f\xc2\xf50@' p1323 tp1324 Rp1325 F16.0 tp1326 a(I2120000 g1 (g5 S'=\n\xd7\xa3p\xfd0@' p1327 tp1328 Rp1329 g1 (g5 S'=\n\xd7\xa3p\xfd0@' p1330 tp1331 Rp1332 F17.0 tp1333 a(I2130000 g1 (g5 S'\xd7\xa3p=\n\x171@' p1334 tp1335 Rp1336 g1 (g5 S'\xd7\xa3p=\n\x171@' p1337 tp1338 Rp1339 F18.0 tp1340 a(I2140000 g1 (g5 S'{\x14\xaeG\xe1:1@' p1341 tp1342 Rp1343 g1 (g5 S'{\x14\xaeG\xe1:1@' p1344 tp1345 Rp1346 F18.0 tp1347 a(I2150000 g1 (g5 S'\xe1z\x14\xaeG!1@' p1348 tp1349 Rp1350 g1346 F18.0 tp1351 a(I2160000 g1 (g5 S'q=\n\xd7\xa301@' p1352 tp1353 Rp1354 g1346 F16.0 tp1355 a(I2170000 g1 (g5 S'\xf6(\\\x8f\xc251@' p1356 tp1357 Rp1358 g1346 F20.0 tp1359 a(I2180000 g1 (g5 S'=\n\xd7\xa3p=1@' p1360 tp1361 Rp1362 g1 (g5 S'\x85\xebQ\xb8\x1eE1@' p1363 tp1364 Rp1365 F13.0 tp1366 a(I2190000 g1 (g5 S'H\xe1z\x14\xaeG1@' p1367 tp1368 Rp1369 g1 (g5 S'\xcd\xcc\xcc\xcc\xccL1@' p1370 tp1371 Rp1372 F19.0 tp1373 a(I2200000 g1 (g5 S'\x8f\xc2\xf5(\\O1@' p1374 tp1375 Rp1376 g1 (g5 S'\x14\xaeG\xe1zT1@' p1377 tp1378 Rp1379 F16.0 tp1380 a(I2210000 g1 (g5 S'\\\x8f\xc2\xf5(\\1@' p1381 tp1382 Rp1383 g1 (g5 S'\\\x8f\xc2\xf5(\\1@' p1384 tp1385 Rp1386 F19.0 tp1387 a(I2220000 g1 (g5 S'\xa4p=\n\xd7c1@' p1388 tp1389 Rp1390 g1 (g5 S')\\\x8f\xc2\xf5h1@' p1391 tp1392 Rp1393 F17.0 tp1394 a(I2230000 g1 (g5 S')\\\x8f\xc2\xf5h1@' p1395 tp1396 Rp1397 g1393 F19.0 tp1398 a(I2240000 g1 (g5 S'\x00\x00\x00\x00\x00@1@' p1399 tp1400 Rp1401 g1393 F16.0 tp1402 a(I2250000 g1 (g5 S'\xcd\xcc\xcc\xcc\xccL1@' p1403 tp1404 Rp1405 g1393 F20.0 tp1406 a(I2260000 g1 (g5 S'\xcd\xcc\xcc\xcc\xccL1@' p1407 tp1408 Rp1409 g1393 F18.0 tp1410 a(I2270000 g1 (g5 S'\xd7\xa3p=\nW1@' p1411 tp1412 Rp1413 g1393 F14.0 tp1414 a(I2280000 g1 (g5 S'\\\x8f\xc2\xf5(\\1@' p1415 tp1416 Rp1417 g1393 F19.0 tp1418 a(I2290000 g1 (g5 S'ffffff1@' p1419 tp1420 Rp1421 g1393 F20.0 tp1422 a(I2300000 g1 (g5 S'\xe1z\x14\xaeGa1@' p1423 tp1424 Rp1425 g1 (g5 S'\xb8\x1e\x85\xebQx1@' p1426 tp1427 Rp1428 F18.0 tp1429 a(I2310000 g1 (g5 S'\xecQ\xb8\x1e\x85k1@' p1430 tp1431 Rp1432 g1428 F16.0 tp1433 a(I2320000 g1 (g5 S'\x85\xebQ\xb8\x1e\x851@' p1434 tp1435 Rp1436 g1 (g5 S'\x85\xebQ\xb8\x1e\x851@' p1437 tp1438 Rp1439 F20.0 tp1440 a(I2330000 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e1@' p1441 tp1442 Rp1443 g1 (g5 S'\x1f\x85\xebQ\xb8\x9e1@' p1444 tp1445 Rp1446 F19.0 tp1447 a(I2340000 g1 (g5 S'\\\x8f\xc2\xf5(\x9c1@' p1448 tp1449 Rp1450 g1 (g5 S'\xe1z\x14\xaeG\xa11@' p1451 tp1452 Rp1453 F21.0 tp1454 a(I2350000 g1 (g5 S'{\x14\xaeG\xe1\xba1@' p1455 tp1456 Rp1457 g1 (g5 S'{\x14\xaeG\xe1\xba1@' p1458 tp1459 Rp1460 F19.0 tp1461 a(I2360000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc51@' p1462 tp1463 Rp1464 g1 (g5 S'\x85\xebQ\xb8\x1e\xc51@' p1465 tp1466 Rp1467 F20.0 tp1468 a(I2370000 g1 (g5 S'R\xb8\x1e\x85\xeb\xd11@' p1469 tp1470 Rp1471 g1 (g5 S'R\xb8\x1e\x85\xeb\xd11@' p1472 tp1473 Rp1474 F18.0 tp1475 a(I2380000 g1 (g5 S'\xf6(\\\x8f\xc2\xb51@' p1476 tp1477 Rp1478 g1474 F11.0 tp1479 a(I2390000 g1 (g5 S'\xc3\xf5(\\\x8f\xc21@' p1480 tp1481 Rp1482 g1474 F19.0 tp1483 a(I2400000 g1 (g5 S')\\\x8f\xc2\xf5\xa81@' p1484 tp1485 Rp1486 g1474 F19.0 tp1487 a(I2410000 g1 (g5 S'33333\xb31@' p1488 tp1489 Rp1490 g1474 F17.0 tp1491 a(I2420000 g1 (g5 S'{\x14\xaeG\xe1\xba1@' p1492 tp1493 Rp1494 g1474 F17.0 tp1495 a(I2430000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc1@' p1496 tp1497 Rp1498 g1474 F20.0 tp1499 a(I2440000 g1 (g5 S'\\\x8f\xc2\xf5(\xdc1@' p1500 tp1501 Rp1502 g1 (g5 S'\\\x8f\xc2\xf5(\xdc1@' p1503 tp1504 Rp1505 F20.0 tp1506 a(I2450000 g1 (g5 S'\xc3\xf5(\\\x8f\x022@' p1507 tp1508 Rp1509 g1 (g5 S'\xc3\xf5(\\\x8f\x022@' p1510 tp1511 Rp1512 F21.0 tp1513 a(I2460000 g1 (g5 S'{\x14\xaeG\xe1\xfa1@' p1514 tp1515 Rp1516 g1512 F19.0 tp1517 a(I2470000 g1 (g5 S'\xaeG\xe1z\x14\xee1@' p1518 tp1519 Rp1520 g1512 F18.0 tp1521 a(I2480000 g1 (g5 S'R\xb8\x1e\x85\xeb\x112@' p1522 tp1523 Rp1524 g1 (g5 S'R\xb8\x1e\x85\xeb\x112@' p1525 tp1526 Rp1527 F19.0 tp1528 a(I2490000 g1 (g5 S'\x8f\xc2\xf5(\\\x0f2@' p1529 tp1530 Rp1531 g1527 F19.0 tp1532 a(I2500000 g1 (g5 S'\xd7\xa3p=\n\x172@' p1533 tp1534 Rp1535 g1 (g5 S'\xd7\xa3p=\n\x172@' p1536 tp1537 Rp1538 F19.0 tp1539 a(I2510000 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e2@' p1540 tp1541 Rp1542 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e2@' p1543 tp1544 Rp1545 F18.0 tp1546 a(I2520000 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e2@' p1547 tp1548 Rp1549 g1 (g5 S'\xa4p=\n\xd7#2@' p1550 tp1551 Rp1552 F17.0 tp1553 a(I2530000 g1 (g5 S'R\xb8\x1e\x85\xeb\x112@' p1554 tp1555 Rp1556 g1552 F19.0 tp1557 a(I2540000 g1 (g5 S'\x14\xaeG\xe1z\x142@' p1558 tp1559 Rp1560 g1552 F20.0 tp1561 a(I2550000 g1 (g5 S'\xaeG\xe1z\x14\xee1@' p1562 tp1563 Rp1564 g1552 F13.0 tp1565 a(I2560000 g1 (g5 S'33333\xf31@' p1566 tp1567 Rp1568 g1552 F19.0 tp1569 a(I2570000 g1 (g5 S'\xf6(\\\x8f\xc2\xf51@' p1570 tp1571 Rp1572 g1552 F19.0 tp1573 a(I2580000 g1 (g5 S')\\\x8f\xc2\xf5\xe81@' p1574 tp1575 Rp1576 g1552 F17.0 tp1577 a(I2590000 g1 (g5 S'\xb8\x1e\x85\xebQ\xf81@' p1578 tp1579 Rp1580 g1552 F19.0 tp1581 a(I2600000 g1 (g5 S'\x85\xebQ\xb8\x1e\x052@' p1582 tp1583 Rp1584 g1552 F19.0 tp1585 a(I2610000 g1 (g5 S'{\x14\xaeG\xe1\xfa1@' p1586 tp1587 Rp1588 g1552 F16.0 tp1589 a(I2620000 g1 (g5 S'33333\xf31@' p1590 tp1591 Rp1592 g1552 F18.0 tp1593 a(I2630000 g1 (g5 S'\xb8\x1e\x85\xebQ\xf81@' p1594 tp1595 Rp1596 g1552 F18.0 tp1597 a(I2640000 g1 (g5 S'33333\xf31@' p1598 tp1599 Rp1600 g1552 F19.0 tp1601 a(I2650000 g1 (g5 S'\xf6(\\\x8f\xc2\xf51@' p1602 tp1603 Rp1604 g1552 F20.0 tp1605 a(I2660000 g1 (g5 S'R\xb8\x1e\x85\xeb\x112@' p1606 tp1607 Rp1608 g1552 F18.0 tp1609 a(I2670000 g1 (g5 S'\xd7\xa3p=\n\x172@' p1610 tp1611 Rp1612 g1552 F20.0 tp1613 a(I2680000 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e2@' p1614 tp1615 Rp1616 g1552 F19.0 tp1617 a(I2690000 g1 (g5 S'\x85\xebQ\xb8\x1eE2@' p1618 tp1619 Rp1620 g1 (g5 S'\x85\xebQ\xb8\x1eE2@' p1621 tp1622 Rp1623 F21.0 tp1624 a(I2700000 g1 (g5 S'\xcd\xcc\xcc\xcc\xccL2@' p1625 tp1626 Rp1627 g1 (g5 S'\xcd\xcc\xcc\xcc\xccL2@' p1628 tp1629 Rp1630 F20.0 tp1631 a(I2710000 g1 (g5 S'=\n\xd7\xa3p=2@' p1632 tp1633 Rp1634 g1630 F15.0 tp1635 a(I2720000 g1 (g5 S'\x85\xebQ\xb8\x1eE2@' p1636 tp1637 Rp1638 g1630 F19.0 tp1639 a(I2730000 g1 (g5 S'\x8f\xc2\xf5(\\O2@' p1640 tp1641 Rp1642 g1 (g5 S'\x8f\xc2\xf5(\\O2@' p1643 tp1644 Rp1645 F21.0 tp1646 a(I2740000 g1 (g5 S'\x9a\x99\x99\x99\x99Y2@' p1647 tp1648 Rp1649 g1 (g5 S'\x9a\x99\x99\x99\x99Y2@' p1650 tp1651 Rp1652 F19.0 tp1653 a(I2750000 g1 (g5 S'\x1f\x85\xebQ\xb8^2@' p1654 tp1655 Rp1656 g1 (g5 S'\x1f\x85\xebQ\xb8^2@' p1657 tp1658 Rp1659 F20.0 tp1660 a(I2760000 g1 (g5 S'R\xb8\x1e\x85\xebQ2@' p1661 tp1662 Rp1663 g1 (g5 S'\xe1z\x14\xaeGa2@' p1664 tp1665 Rp1666 F20.0 tp1667 a(I2770000 g1 (g5 S'\\\x8f\xc2\xf5(\\2@' p1668 tp1669 Rp1670 g1666 F18.0 tp1671 a(I2780000 g1 (g5 S'33333s2@' p1672 tp1673 Rp1674 g1 (g5 S'33333s2@' p1675 tp1676 Rp1677 F19.0 tp1678 a(I2790000 g1 (g5 S'H\xe1z\x14\xae\x872@' p1679 tp1680 Rp1681 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x8c2@' p1682 tp1683 Rp1684 F18.0 tp1685 a(I2800000 g1 (g5 S'\x14\xaeG\xe1z\x942@' p1686 tp1687 Rp1688 g1 (g5 S'\x9a\x99\x99\x99\x99\x992@' p1689 tp1690 Rp1691 F19.0 tp1692 a(I2810000 g1 (g5 S'\xaeG\xe1z\x14\xae2@' p1693 tp1694 Rp1695 g1 (g5 S'\xaeG\xe1z\x14\xae2@' p1696 tp1697 Rp1698 F19.0 tp1699 a(I2820000 g1 (g5 S'\xaeG\xe1z\x14\xae2@' p1700 tp1701 Rp1702 g1698 F19.0 tp1703 a(I2830000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc52@' p1704 tp1705 Rp1706 g1 (g5 S'\x85\xebQ\xb8\x1e\xc52@' p1707 tp1708 Rp1709 F20.0 tp1710 a(I2840000 g1 (g5 S'\xc3\xf5(\\\x8f\xc22@' p1711 tp1712 Rp1713 g1 (g5 S'\xd7\xa3p=\n\xd72@' p1714 tp1715 Rp1716 F19.0 tp1717 a(I2850000 g1 (g5 S'\n\xd7\xa3p=\xca2@' p1718 tp1719 Rp1720 g1716 F19.0 tp1721 a(I2860000 g1 (g5 S'\xe1z\x14\xaeG\xe12@' p1722 tp1723 Rp1724 g1 (g5 S'\xe1z\x14\xaeG\xe12@' p1725 tp1726 Rp1727 F19.0 tp1728 a(I2870000 g1 (g5 S'\x14\xaeG\xe1z\xd42@' p1729 tp1730 Rp1731 g1727 F16.0 tp1732 a(I2880000 g1 (g5 S'\n\xd7\xa3p=\xca2@' p1733 tp1734 Rp1735 g1727 F18.0 tp1736 a(I2890000 g1 (g5 S'\xaeG\xe1z\x14\xae2@' p1737 tp1738 Rp1739 g1727 F17.0 tp1740 a(I2900000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb82@' p1741 tp1742 Rp1743 g1727 F19.0 tp1744 a(I2910000 g1 (g5 S'H\xe1z\x14\xae\xc72@' p1745 tp1746 Rp1747 g1727 F20.0 tp1748 a(I2920000 g1 (g5 S'H\xe1z\x14\xae\xc72@' p1749 tp1750 Rp1751 g1727 F19.0 tp1752 a(I2930000 g1 (g5 S'\x14\xaeG\xe1z\xd42@' p1753 tp1754 Rp1755 g1727 F21.0 tp1756 a(I2940000 g1 (g5 S')\\\x8f\xc2\xf5\xe82@' p1757 tp1758 Rp1759 g1 (g5 S'q=\n\xd7\xa3\xf02@' p1760 tp1761 Rp1762 F17.0 tp1763 a(I2950000 g1 (g5 S'=\n\xd7\xa3p\xfd2@' p1764 tp1765 Rp1766 g1 (g5 S'\x00\x00\x00\x00\x00\x003@' p1767 tp1768 Rp1769 F19.0 tp1770 a(I2960000 g1 (g5 S'\x14\xaeG\xe1z\x143@' p1771 tp1772 Rp1773 g1 (g5 S'\x14\xaeG\xe1z\x143@' p1774 tp1775 Rp1776 F20.0 tp1777 a(I2970000 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e3@' p1778 tp1779 Rp1780 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e3@' p1781 tp1782 Rp1783 F20.0 tp1784 a(I2980000 g1 (g5 S'\x9a\x99\x99\x99\x99\x193@' p1785 tp1786 Rp1787 g1 (g5 S'\xa4p=\n\xd7#3@' p1788 tp1789 Rp1790 F15.0 tp1791 a(I2990000 g1 (g5 S'\xe1z\x14\xaeG!3@' p1792 tp1793 Rp1794 g1 (g5 S'fffff&3@' p1795 tp1796 Rp1797 F19.0 tp1798 a(I3000000 g1 (g5 S'\xd7\xa3p=\n\x173@' p1799 tp1800 Rp1801 g1797 F19.0 tp1802 a(I3010000 g1 (g5 S'\x9a\x99\x99\x99\x99\x193@' p1803 tp1804 Rp1805 g1797 F19.0 tp1806 a(I3020000 g1 (g5 S'\\\x8f\xc2\xf5(\x1c3@' p1807 tp1808 Rp1809 g1797 F20.0 tp1810 a(I3030000 g1 (g5 S'\xa4p=\n\xd7#3@' p1811 tp1812 Rp1813 g1 (g5 S')\\\x8f\xc2\xf5(3@' p1814 tp1815 Rp1816 F19.0 tp1817 a(I3040000 g1 (g5 S'H\xe1z\x14\xae\x073@' p1818 tp1819 Rp1820 g1816 F17.0 tp1821 a(I3050000 g1 (g5 S'=\n\xd7\xa3p\xfd2@' p1822 tp1823 Rp1824 g1816 F19.0 tp1825 a(I3060000 g1 (g5 S'\xc3\xf5(\\\x8f\x023@' p1826 tp1827 Rp1828 g1816 F19.0 tp1829 a(I3070000 g1 (g5 S'\x8f\xc2\xf5(\\\x0f3@' p1830 tp1831 Rp1832 g1816 F20.0 tp1833 a(I3080000 g1 (g5 S'\x8f\xc2\xf5(\\\x0f3@' p1834 tp1835 Rp1836 g1816 F17.0 tp1837 a(I3090000 g1 (g5 S'\x9a\x99\x99\x99\x99\x193@' p1838 tp1839 Rp1840 g1816 F19.0 tp1841 a(I3100000 g1 (g5 S'fffff&3@' p1842 tp1843 Rp1844 g1816 F20.0 tp1845 a(I3110000 g1 (g5 S'\xecQ\xb8\x1e\x85+3@' p1846 tp1847 Rp1848 g1 (g5 S'\xaeG\xe1z\x14.3@' p1849 tp1850 Rp1851 F20.0 tp1852 a(I3120000 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e3@' p1853 tp1854 Rp1855 g1851 F19.0 tp1856 a(I3130000 g1 (g5 S'\\\x8f\xc2\xf5(\x1c3@' p1857 tp1858 Rp1859 g1851 F20.0 tp1860 a(I3140000 g1 (g5 S'\x14\xaeG\xe1z\x143@' p1861 tp1862 Rp1863 g1851 F21.0 tp1864 a(I3150000 g1 (g5 S'\x9a\x99\x99\x99\x99\x193@' p1865 tp1866 Rp1867 g1851 F21.0 tp1868 a(I3160000 g1 (g5 S'R\xb8\x1e\x85\xeb\x113@' p1869 tp1870 Rp1871 g1851 F18.0 tp1872 a(I3170000 g1 (g5 S'\x8f\xc2\xf5(\\\x0f3@' p1873 tp1874 Rp1875 g1851 F20.0 tp1876 a(I3180000 g1 (g5 S'\xd7\xa3p=\n\x173@' p1877 tp1878 Rp1879 g1851 F20.0 tp1880 a(I3190000 g1 (g5 S'fffff&3@' p1881 tp1882 Rp1883 g1851 F20.0 tp1884 a(I3200000 g1 (g5 S'fffff&3@' p1885 tp1886 Rp1887 g1 (g5 S'q=\n\xd7\xa303@' p1888 tp1889 Rp1890 F18.0 tp1891 a(I3210000 g1 (g5 S'\xa4p=\n\xd7#3@' p1892 tp1893 Rp1894 g1890 F19.0 tp1895 a(I3220000 g1 (g5 S'\xf6(\\\x8f\xc253@' p1896 tp1897 Rp1898 g1 (g5 S'\xf6(\\\x8f\xc253@' p1899 tp1900 Rp1901 F20.0 tp1902 a(I3230000 g1 (g5 S'\n\xd7\xa3p=J3@' p1903 tp1904 Rp1905 g1 (g5 S'\n\xd7\xa3p=J3@' p1906 tp1907 Rp1908 F20.0 tp1909 a(I3240000 g1 (g5 S'H\xe1z\x14\xaeG3@' p1910 tp1911 Rp1912 g1908 F18.0 tp1913 a(I3250000 g1 (g5 S'=\n\xd7\xa3p=3@' p1914 tp1915 Rp1916 g1 (g5 S'\xcd\xcc\xcc\xcc\xccL3@' p1917 tp1918 Rp1919 F20.0 tp1920 a(I3260000 g1 (g5 S'\xc3\xf5(\\\x8fB3@' p1921 tp1922 Rp1923 g1919 F20.0 tp1924 a(I3270000 g1 (g5 S'\n\xd7\xa3p=J3@' p1925 tp1926 Rp1927 g1919 F18.0 tp1928 a(I3280000 g1 (g5 S'\xf6(\\\x8f\xc253@' p1929 tp1930 Rp1931 g1919 F19.0 tp1932 a(I3290000 g1 (g5 S'\xf6(\\\x8f\xc253@' p1933 tp1934 Rp1935 g1919 F19.0 tp1936 a(I3300000 g1 (g5 S'\xb8\x1e\x85\xebQ83@' p1937 tp1938 Rp1939 g1919 F20.0 tp1940 a(I3310000 g1 (g5 S'H\xe1z\x14\xaeG3@' p1941 tp1942 Rp1943 g1919 F19.0 tp1944 a(I3320000 g1 (g5 S'\x9a\x99\x99\x99\x99Y3@' p1945 tp1946 Rp1947 g1 (g5 S'\x9a\x99\x99\x99\x99Y3@' p1948 tp1949 Rp1950 F19.0 tp1951 a(I3330000 g1 (g5 S'\xcd\xcc\xcc\xcc\xccL3@' p1952 tp1953 Rp1954 g1950 F21.0 tp1955 a(I3340000 g1 (g5 S'\n\xd7\xa3p=J3@' p1956 tp1957 Rp1958 g1950 F20.0 tp1959 a(I3350000 g1 (g5 S'\x1f\x85\xebQ\xb8^3@' p1960 tp1961 Rp1962 g1 (g5 S'\x1f\x85\xebQ\xb8^3@' p1963 tp1964 Rp1965 F20.0 tp1966 a(I3360000 g1 (g5 S')\\\x8f\xc2\xf5h3@' p1967 tp1968 Rp1969 g1 (g5 S')\\\x8f\xc2\xf5h3@' p1970 tp1971 Rp1972 F21.0 tp1973 a(I3370000 g1 (g5 S'\xf6(\\\x8f\xc2u3@' p1974 tp1975 Rp1976 g1 (g5 S'\xb8\x1e\x85\xebQx3@' p1977 tp1978 Rp1979 F20.0 tp1980 a(I3380000 g1 (g5 S'\xaeG\xe1z\x14n3@' p1981 tp1982 Rp1983 g1979 F19.0 tp1984 a(I3390000 g1 (g5 S'\x00\x00\x00\x00\x00\x803@' p1985 tp1986 Rp1987 g1 (g5 S'\x00\x00\x00\x00\x00\x803@' p1988 tp1989 Rp1990 F21.0 tp1991 a(I3400000 g1 (g5 S'H\xe1z\x14\xae\x873@' p1992 tp1993 Rp1994 g1 (g5 S'\n\xd7\xa3p=\x8a3@' p1995 tp1996 Rp1997 F20.0 tp1998 a(I3410000 g1 (g5 S'R\xb8\x1e\x85\xeb\x913@' p1999 tp2000 Rp2001 g1 (g5 S'R\xb8\x1e\x85\xeb\x913@' p2002 tp2003 Rp2004 F20.0 tp2005 a(I3420000 g1 (g5 S'\xd7\xa3p=\n\x973@' p2006 tp2007 Rp2008 g1 (g5 S'\xd7\xa3p=\n\x973@' p2009 tp2010 Rp2011 F20.0 tp2012 a(I3430000 g1 (g5 S'q=\n\xd7\xa3\xb03@' p2013 tp2014 Rp2015 g1 (g5 S'q=\n\xd7\xa3\xb03@' p2016 tp2017 Rp2018 F20.0 tp2019 a(I3440000 g1 (g5 S'{\x14\xaeG\xe1\xba3@' p2020 tp2021 Rp2022 g1 (g5 S'{\x14\xaeG\xe1\xba3@' p2023 tp2024 Rp2025 F20.0 tp2026 a(I3450000 g1 (g5 S'\x14\xaeG\xe1z\xd43@' p2027 tp2028 Rp2029 g1 (g5 S'\x14\xaeG\xe1z\xd43@' p2030 tp2031 Rp2032 F21.0 tp2033 a(I3460000 g1 (g5 S'fffff\xe63@' p2034 tp2035 Rp2036 g1 (g5 S')\\\x8f\xc2\xf5\xe83@' p2037 tp2038 Rp2039 F20.0 tp2040 a(I3470000 g1 (g5 S'33333\xf33@' p2041 tp2042 Rp2043 g1 (g5 S'33333\xf33@' p2044 tp2045 Rp2046 F20.0 tp2047 a(I3480000 g1 (g5 S'\xf6(\\\x8f\xc2\xf53@' p2048 tp2049 Rp2050 g1 (g5 S'\xf6(\\\x8f\xc2\xf53@' p2051 tp2052 Rp2053 F19.0 tp2054 a(I3490000 g1 (g5 S'33333\xf33@' p2055 tp2056 Rp2057 g1 (g5 S'\xb8\x1e\x85\xebQ\xf83@' p2058 tp2059 Rp2060 F21.0 tp2061 a(I3500000 g1 (g5 S'\xb8\x1e\x85\xebQ\xf83@' p2062 tp2063 Rp2064 g2060 F20.0 tp2065 a(I3510000 g1 (g5 S'\xf6(\\\x8f\xc2\xf53@' p2066 tp2067 Rp2068 g1 (g5 S'\x00\x00\x00\x00\x00\x004@' p2069 tp2070 Rp2071 F18.0 tp2072 a(I3520000 g1 (g5 S'{\x14\xaeG\xe1\xfa3@' p2073 tp2074 Rp2075 g2071 F19.0 tp2076 a(I3530000 g1 (g5 S'\x85\xebQ\xb8\x1e\x054@' p2077 tp2078 Rp2079 g1 (g5 S'\x85\xebQ\xb8\x1e\x054@' p2080 tp2081 Rp2082 F21.0 tp2083 a(I3540000 g1 (g5 S'\x85\xebQ\xb8\x1e\x054@' p2084 tp2085 Rp2086 g1 (g5 S'H\xe1z\x14\xae\x074@' p2087 tp2088 Rp2089 F21.0 tp2090 a(I3550000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x0c4@' p2091 tp2092 Rp2093 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x0c4@' p2094 tp2095 Rp2096 F20.0 tp2097 a(I3560000 g1 (g5 S'\x14\xaeG\xe1z\x144@' p2098 tp2099 Rp2100 g1 (g5 S'\x14\xaeG\xe1z\x144@' p2101 tp2102 Rp2103 F21.0 tp2104 a(I3570000 g1 (g5 S'\xd7\xa3p=\n\x174@' p2105 tp2106 Rp2107 g1 (g5 S'\xd7\xa3p=\n\x174@' p2108 tp2109 Rp2110 F21.0 tp2111 a(I3580000 g1 (g5 S'\\\x8f\xc2\xf5(\x1c4@' p2112 tp2113 Rp2114 g1 (g5 S'\x1f\x85\xebQ\xb8\x1e4@' p2115 tp2116 Rp2117 F20.0 tp2118 a(I3590000 g1 (g5 S'\xd7\xa3p=\n\x174@' p2119 tp2120 Rp2121 g2117 F21.0 tp2122 a(I3600000 g1 (g5 S'\\\x8f\xc2\xf5(\x1c4@' p2123 tp2124 Rp2125 g2117 F20.0 tp2126 a(I3610000 g1 (g5 S'\x14\xaeG\xe1z\x144@' p2127 tp2128 Rp2129 g2117 F18.0 tp2130 a(I3620000 g1 (g5 S'\x8f\xc2\xf5(\\\x0f4@' p2131 tp2132 Rp2133 g2117 F20.0 tp2134 a(I3630000 g1 (g5 S'\x00\x00\x00\x00\x00\x004@' p2135 tp2136 Rp2137 g2117 F19.0 tp2138 a(I3640000 g1 (g5 S'H\xe1z\x14\xae\x074@' p2139 tp2140 Rp2141 g2117 F21.0 tp2142 a(I3650000 g1 (g5 S'\n\xd7\xa3p=\n4@' p2143 tp2144 Rp2145 g2117 F20.0 tp2146 a(I3660000 g1 (g5 S'\x8f\xc2\xf5(\\\x0f4@' p2147 tp2148 Rp2149 g2117 F21.0 tp2150 a(I3670000 g1 (g5 S'\x9a\x99\x99\x99\x99\x194@' p2151 tp2152 Rp2153 g2117 F20.0 tp2154 a(I3680000 g1 (g5 S'\xe1z\x14\xaeG!4@' p2155 tp2156 Rp2157 g1 (g5 S'\xe1z\x14\xaeG!4@' p2158 tp2159 Rp2160 F20.0 tp2161 a(I3690000 g1 (g5 S'fffff&4@' p2162 tp2163 Rp2164 g1 (g5 S'fffff&4@' p2165 tp2166 Rp2167 F21.0 tp2168 a(I3700000 g1 (g5 S')\\\x8f\xc2\xf5(4@' p2169 tp2170 Rp2171 g1 (g5 S')\\\x8f\xc2\xf5(4@' p2172 tp2173 Rp2174 F21.0 tp2175 a(I3710000 g1 (g5 S'\xa4p=\n\xd7#4@' p2176 tp2177 Rp2178 g2174 F20.0 tp2179 a(I3720000 g1 (g5 S'\xa4p=\n\xd7#4@' p2180 tp2181 Rp2182 g2174 F20.0 tp2183 a(I3730000 g1 (g5 S'q=\n\xd7\xa304@' p2184 tp2185 Rp2186 g1 (g5 S'\xf6(\\\x8f\xc254@' p2187 tp2188 Rp2189 F19.0 tp2190 a(I3740000 g1 (g5 S'3333334@' p2191 tp2192 Rp2193 g2189 F20.0 tp2194 a(I3750000 g1 (g5 S'q=\n\xd7\xa304@' p2195 tp2196 Rp2197 g2189 F20.0 tp2198 a(I3760000 g1 (g5 S'\xf6(\\\x8f\xc254@' p2199 tp2200 Rp2201 g2189 F21.0 tp2202 a(I3770000 g1 (g5 S'\x85\xebQ\xb8\x1eE4@' p2203 tp2204 Rp2205 g1 (g5 S'\x85\xebQ\xb8\x1eE4@' p2206 tp2207 Rp2208 F20.0 tp2209 a(I3780000 g1 (g5 S'H\xe1z\x14\xaeG4@' p2210 tp2211 Rp2212 g1 (g5 S'H\xe1z\x14\xaeG4@' p2213 tp2214 Rp2215 F21.0 tp2216 a(I3790000 g1 (g5 S'\x8f\xc2\xf5(\\O4@' p2217 tp2218 Rp2219 g1 (g5 S'\x8f\xc2\xf5(\\O4@' p2220 tp2221 Rp2222 F21.0 tp2223 a(I3800000 g1 (g5 S'\x1f\x85\xebQ\xb8^4@' p2224 tp2225 Rp2226 g1 (g5 S'\x1f\x85\xebQ\xb8^4@' p2227 tp2228 Rp2229 F21.0 tp2230 a(I3810000 g1 (g5 S'\xa4p=\n\xd7c4@' p2231 tp2232 Rp2233 g1 (g5 S'\xa4p=\n\xd7c4@' p2234 tp2235 Rp2236 F19.0 tp2237 a(I3820000 g1 (g5 S'\xe1z\x14\xaeGa4@' p2238 tp2239 Rp2240 g1 (g5 S'ffffff4@' p2241 tp2242 Rp2243 F19.0 tp2244 a(I3830000 g1 (g5 S')\\\x8f\xc2\xf5h4@' p2245 tp2246 Rp2247 g1 (g5 S')\\\x8f\xc2\xf5h4@' p2248 tp2249 Rp2250 F20.0 tp2251 a(I3840000 g1 (g5 S'\x1f\x85\xebQ\xb8^4@' p2252 tp2253 Rp2254 g2250 F20.0 tp2255 a(I3850000 g1 (g5 S'\x9a\x99\x99\x99\x99Y4@' p2256 tp2257 Rp2258 g2250 F19.0 tp2259 a(I3860000 g1 (g5 S'ffffff4@' p2260 tp2261 Rp2262 g2250 F20.0 tp2263 a(I3870000 g1 (g5 S'\xecQ\xb8\x1e\x85k4@' p2264 tp2265 Rp2266 g1 (g5 S'\xecQ\xb8\x1e\x85k4@' p2267 tp2268 Rp2269 F21.0 tp2270 a(I3880000 g1 (g5 S'\xb8\x1e\x85\xebQx4@' p2271 tp2272 Rp2273 g1 (g5 S'\xb8\x1e\x85\xebQx4@' p2274 tp2275 Rp2276 F21.0 tp2277 a(I3890000 g1 (g5 S'33333s4@' p2278 tp2279 Rp2280 g2276 F20.0 tp2281 a(I3900000 g1 (g5 S'33333s4@' p2282 tp2283 Rp2284 g2276 F20.0 tp2285 a(I3910000 g1 (g5 S'q=\n\xd7\xa3p4@' p2286 tp2287 Rp2288 g2276 F20.0 tp2289 a(I3920000 g1 (g5 S'33333s4@' p2290 tp2291 Rp2292 g2276 F21.0 tp2293 a(I3930000 g1 (g5 S'33333s4@' p2294 tp2295 Rp2296 g2276 F21.0 tp2297 a(I3940000 g1 (g5 S'\xf6(\\\x8f\xc2u4@' p2298 tp2299 Rp2300 g2276 F21.0 tp2301 a(I3950000 g1 (g5 S'\xf6(\\\x8f\xc2u4@' p2302 tp2303 Rp2304 g2276 F21.0 tp2305 a(I3960000 g1 (g5 S'\xb8\x1e\x85\xebQx4@' p2306 tp2307 Rp2308 g1 (g5 S'{\x14\xaeG\xe1z4@' p2309 tp2310 Rp2311 F20.0 tp2312 a(I3970000 g1 (g5 S'\xaeG\xe1z\x14n4@' p2313 tp2314 Rp2315 g2311 F21.0 tp2316 a(I3980000 g1 (g5 S'33333s4@' p2317 tp2318 Rp2319 g2311 F21.0 tp2320 a(I3990000 g1 (g5 S'33333s4@' p2321 tp2322 Rp2323 g2311 F21.0 tp2324 a(I4000000 g1 (g5 S'q=\n\xd7\xa3p4@' p2325 tp2326 Rp2327 g1 (g5 S'=\n\xd7\xa3p}4@' p2328 tp2329 Rp2330 F19.0 tp2331 a(I4010000 g1 (g5 S'\xf6(\\\x8f\xc2u4@' p2332 tp2333 Rp2334 g2330 F21.0 tp2335 a(I4020000 g1 (g5 S'=\n\xd7\xa3p}4@' p2336 tp2337 Rp2338 g2330 F21.0 tp2339 a(I4030000 g1 (g5 S'\xc3\xf5(\\\x8f\x824@' p2340 tp2341 Rp2342 g1 (g5 S'\x85\xebQ\xb8\x1e\x854@' p2343 tp2344 Rp2345 F21.0 tp2346 a(I4040000 g1 (g5 S'=\n\xd7\xa3p}4@' p2347 tp2348 Rp2349 g2345 F20.0 tp2350 a(I4050000 g1 (g5 S'{\x14\xaeG\xe1z4@' p2351 tp2352 Rp2353 g2345 F21.0 tp2354 a(I4060000 g1 (g5 S'\x85\xebQ\xb8\x1e\x854@' p2355 tp2356 Rp2357 g2345 F21.0 tp2358 a(I4070000 g1 (g5 S'\x00\x00\x00\x00\x00\x804@' p2359 tp2360 Rp2361 g2345 F21.0 tp2362 a(I4080000 g1 (g5 S'\xc3\xf5(\\\x8f\x824@' p2363 tp2364 Rp2365 g2345 F21.0 tp2366 a(I4090000 g1 (g5 S'\xc3\xf5(\\\x8f\x824@' p2367 tp2368 Rp2369 g2345 F21.0 tp2370 a(I4100000 g1 (g5 S'\x85\xebQ\xb8\x1e\x854@' p2371 tp2372 Rp2373 g1 (g5 S'H\xe1z\x14\xae\x874@' p2374 tp2375 Rp2376 F20.0 tp2377 a(I4110000 g1 (g5 S'{\x14\xaeG\xe1z4@' p2378 tp2379 Rp2380 g2376 F20.0 tp2381 a(I4120000 g1 (g5 S'{\x14\xaeG\xe1z4@' p2382 tp2383 Rp2384 g2376 F21.0 tp2385 a(I4130000 g1 (g5 S'=\n\xd7\xa3p}4@' p2386 tp2387 Rp2388 g2376 F21.0 tp2389 a(I4140000 g1 (g5 S'H\xe1z\x14\xae\x874@' p2390 tp2391 Rp2392 g2376 F21.0 tp2393 a(I4150000 g1 (g5 S'\x85\xebQ\xb8\x1e\x854@' p2394 tp2395 Rp2396 g2376 F21.0 tp2397 a(I4160000 g1 (g5 S'\x00\x00\x00\x00\x00\x804@' p2398 tp2399 Rp2400 g2376 F19.0 tp2401 a(I4170000 g1 (g5 S'\n\xd7\xa3p=\x8a4@' p2402 tp2403 Rp2404 g1 (g5 S'\n\xd7\xa3p=\x8a4@' p2405 tp2406 Rp2407 F21.0 tp2408 a(I4180000 g1 (g5 S'R\xb8\x1e\x85\xeb\x914@' p2409 tp2410 Rp2411 g1 (g5 S'R\xb8\x1e\x85\xeb\x914@' p2412 tp2413 Rp2414 F21.0 tp2415 a(I4190000 g1 (g5 S'R\xb8\x1e\x85\xeb\x914@' p2416 tp2417 Rp2418 g2414 F21.0 tp2419 a(I4200000 g1 (g5 S'H\xe1z\x14\xae\x874@' p2420 tp2421 Rp2422 g2414 F19.0 tp2423 a(I4210000 g1 (g5 S'\n\xd7\xa3p=\x8a4@' p2424 tp2425 Rp2426 g2414 F21.0 tp2427 a(I4220000 g1 (g5 S'\x00\x00\x00\x00\x00\x804@' p2428 tp2429 Rp2430 g2414 F19.0 tp2431 a(I4230000 g1 (g5 S'=\n\xd7\xa3p}4@' p2432 tp2433 Rp2434 g2414 F20.0 tp2435 a(I4240000 g1 (g5 S'\xc3\xf5(\\\x8f\x824@' p2436 tp2437 Rp2438 g2414 F21.0 tp2439 a(I4250000 g1 (g5 S'\xc3\xf5(\\\x8f\x824@' p2440 tp2441 Rp2442 g2414 F20.0 tp2443 a(I4260000 g1 (g5 S'\x85\xebQ\xb8\x1e\x854@' p2444 tp2445 Rp2446 g2414 F21.0 tp2447 a(I4270000 g1 (g5 S'H\xe1z\x14\xae\x874@' p2448 tp2449 Rp2450 g2414 F21.0 tp2451 a(I4280000 g1 (g5 S'H\xe1z\x14\xae\x874@' p2452 tp2453 Rp2454 g2414 F20.0 tp2455 a(I4290000 g1 (g5 S'\n\xd7\xa3p=\x8a4@' p2456 tp2457 Rp2458 g2414 F19.0 tp2459 a(I4300000 g1 (g5 S'\x85\xebQ\xb8\x1e\x854@' p2460 tp2461 Rp2462 g2414 F20.0 tp2463 a(I4310000 g1 (g5 S'\x85\xebQ\xb8\x1e\x854@' p2464 tp2465 Rp2466 g2414 F21.0 tp2467 a(I4320000 g1 (g5 S'{\x14\xaeG\xe1z4@' p2468 tp2469 Rp2470 g2414 F19.0 tp2471 a(I4330000 g1 (g5 S'\x00\x00\x00\x00\x00\x804@' p2472 tp2473 Rp2474 g2414 F21.0 tp2475 a(I4340000 g1 (g5 S'\xb8\x1e\x85\xebQx4@' p2476 tp2477 Rp2478 g2414 F21.0 tp2479 a(I4350000 g1 (g5 S'\xb8\x1e\x85\xebQx4@' p2480 tp2481 Rp2482 g2414 F21.0 tp2483 a(I4360000 g1 (g5 S'\xf6(\\\x8f\xc2u4@' p2484 tp2485 Rp2486 g2414 F20.0 tp2487 a(I4370000 g1 (g5 S'\xc3\xf5(\\\x8f\x824@' p2488 tp2489 Rp2490 g2414 F21.0 tp2491 a(I4380000 g1 (g5 S'\x85\xebQ\xb8\x1e\x854@' p2492 tp2493 Rp2494 g2414 F21.0 tp2495 a(I4390000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f4@' p2496 tp2497 Rp2498 g2414 F21.0 tp2499 a(I4400000 g1 (g5 S'\n\xd7\xa3p=\x8a4@' p2500 tp2501 Rp2502 g2414 F21.0 tp2503 a(I4410000 g1 (g5 S'\x85\xebQ\xb8\x1e\x854@' p2504 tp2505 Rp2506 g2414 F21.0 tp2507 a(I4420000 g1 (g5 S'H\xe1z\x14\xae\x874@' p2508 tp2509 Rp2510 g2414 F20.0 tp2511 a(I4430000 g1 (g5 S'\x85\xebQ\xb8\x1e\x854@' p2512 tp2513 Rp2514 g2414 F21.0 tp2515 a(I4440000 g1 (g5 S'\xc3\xf5(\\\x8f\x824@' p2516 tp2517 Rp2518 g2414 F20.0 tp2519 a(I4450000 g1 (g5 S'H\xe1z\x14\xae\x874@' p2520 tp2521 Rp2522 g2414 F21.0 tp2523 a(I4460000 g1 (g5 S'\x85\xebQ\xb8\x1e\x854@' p2524 tp2525 Rp2526 g2414 F20.0 tp2527 a(I4470000 g1 (g5 S'\xc3\xf5(\\\x8f\x824@' p2528 tp2529 Rp2530 g2414 F21.0 tp2531 a(I4480000 g1 (g5 S'{\x14\xaeG\xe1z4@' p2532 tp2533 Rp2534 g2414 F20.0 tp2535 a(I4490000 g1 (g5 S'H\xe1z\x14\xae\x874@' p2536 tp2537 Rp2538 g2414 F20.0 tp2539 a(I4500000 g1 (g5 S'H\xe1z\x14\xae\x874@' p2540 tp2541 Rp2542 g2414 F20.0 tp2543 a(I4510000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x8c4@' p2544 tp2545 Rp2546 g2414 F20.0 tp2547 a(I4520000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x8c4@' p2548 tp2549 Rp2550 g2414 F21.0 tp2551 a(I4530000 g1 (g5 S'\x85\xebQ\xb8\x1e\x854@' p2552 tp2553 Rp2554 g2414 F20.0 tp2555 a(I4540000 g1 (g5 S'\x00\x00\x00\x00\x00\x804@' p2556 tp2557 Rp2558 g2414 F20.0 tp2559 a(I4550000 g1 (g5 S'\x00\x00\x00\x00\x00\x804@' p2560 tp2561 Rp2562 g2414 F21.0 tp2563 a(I4560000 g1 (g5 S'{\x14\xaeG\xe1z4@' p2564 tp2565 Rp2566 g2414 F20.0 tp2567 a(I4570000 g1 (g5 S'=\n\xd7\xa3p}4@' p2568 tp2569 Rp2570 g2414 F21.0 tp2571 a(I4580000 g1 (g5 S'33333s4@' p2572 tp2573 Rp2574 g2414 F21.0 tp2575 a(I4590000 g1 (g5 S'\xaeG\xe1z\x14n4@' p2576 tp2577 Rp2578 g2414 F21.0 tp2579 a(I4600000 g1 (g5 S'q=\n\xd7\xa3p4@' p2580 tp2581 Rp2582 g2414 F21.0 tp2583 a(I4610000 g1 (g5 S'\xf6(\\\x8f\xc2u4@' p2584 tp2585 Rp2586 g2414 F21.0 tp2587 a(I4620000 g1 (g5 S'33333s4@' p2588 tp2589 Rp2590 g2414 F21.0 tp2591 a(I4630000 g1 (g5 S'q=\n\xd7\xa3p4@' p2592 tp2593 Rp2594 g2414 F20.0 tp2595 a(I4640000 g1 (g5 S'33333s4@' p2596 tp2597 Rp2598 g2414 F20.0 tp2599 a(I4650000 g1 (g5 S'\xb8\x1e\x85\xebQx4@' p2600 tp2601 Rp2602 g2414 F20.0 tp2603 a(I4660000 g1 (g5 S'\xb8\x1e\x85\xebQx4@' p2604 tp2605 Rp2606 g2414 F21.0 tp2607 a(I4670000 g1 (g5 S'\x00\x00\x00\x00\x00\x804@' p2608 tp2609 Rp2610 g2414 F20.0 tp2611 a(I4680000 g1 (g5 S'=\n\xd7\xa3p}4@' p2612 tp2613 Rp2614 g2414 F21.0 tp2615 a(I4690000 g1 (g5 S'\xb8\x1e\x85\xebQx4@' p2616 tp2617 Rp2618 g2414 F21.0 tp2619 a(I4700000 g1 (g5 S'\xb8\x1e\x85\xebQx4@' p2620 tp2621 Rp2622 g2414 F19.0 tp2623 a(I4710000 g1 (g5 S'33333s4@' p2624 tp2625 Rp2626 g2414 F20.0 tp2627 a(I4720000 g1 (g5 S'33333s4@' p2628 tp2629 Rp2630 g2414 F20.0 tp2631 a(I4730000 g1 (g5 S'\xb8\x1e\x85\xebQx4@' p2632 tp2633 Rp2634 g2414 F20.0 tp2635 a(I4740000 g1 (g5 S'\xb8\x1e\x85\xebQx4@' p2636 tp2637 Rp2638 g2414 F20.0 tp2639 a(I4750000 g1 (g5 S'\xc3\xf5(\\\x8f\x824@' p2640 tp2641 Rp2642 g2414 F20.0 tp2643 a(I4760000 g1 (g5 S'\x00\x00\x00\x00\x00\x804@' p2644 tp2645 Rp2646 g2414 F20.0 tp2647 a(I4770000 g1 (g5 S'=\n\xd7\xa3p}4@' p2648 tp2649 Rp2650 g2414 F21.0 tp2651 a(I4780000 g1 (g5 S'=\n\xd7\xa3p}4@' p2652 tp2653 Rp2654 g2414 F21.0 tp2655 a(I4790000 g1 (g5 S'\xc3\xf5(\\\x8f\x824@' p2656 tp2657 Rp2658 g2414 F21.0 tp2659 a(I4800000 g1 (g5 S'\x85\xebQ\xb8\x1e\x854@' p2660 tp2661 Rp2662 g2414 F21.0 tp2663 a(I4810000 g1 (g5 S'\x85\xebQ\xb8\x1e\x854@' p2664 tp2665 Rp2666 g2414 F19.0 tp2667 a(I4820000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x8c4@' p2668 tp2669 Rp2670 g2414 F21.0 tp2671 a(I4830000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f4@' p2672 tp2673 Rp2674 g2414 F21.0 tp2675 a(I4840000 g1 (g5 S'\n\xd7\xa3p=\x8a4@' p2676 tp2677 Rp2678 g2414 F21.0 tp2679 a(I4850000 g1 (g5 S'\n\xd7\xa3p=\x8a4@' p2680 tp2681 Rp2682 g2414 F21.0 tp2683 a(I4860000 g1 (g5 S'\n\xd7\xa3p=\x8a4@' p2684 tp2685 Rp2686 g2414 F19.0 tp2687 a(I4870000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x8c4@' p2688 tp2689 Rp2690 g2414 F19.0 tp2691 a(I4880000 g1 (g5 S'R\xb8\x1e\x85\xeb\x914@' p2692 tp2693 Rp2694 g1 (g5 S'\x14\xaeG\xe1z\x944@' p2695 tp2696 Rp2697 F20.0 tp2698 a(I4890000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f4@' p2699 tp2700 Rp2701 g2697 F21.0 tp2702 a(I4900000 g1 (g5 S'H\xe1z\x14\xae\x874@' p2703 tp2704 Rp2705 g2697 F20.0 tp2706 a(I4910000 g1 (g5 S'\x00\x00\x00\x00\x00\x804@' p2707 tp2708 Rp2709 g2697 F21.0 tp2710 a(I4920000 g1 (g5 S'\xc3\xf5(\\\x8f\x824@' p2711 tp2712 Rp2713 g2697 F21.0 tp2714 a(I4930000 g1 (g5 S'H\xe1z\x14\xae\x874@' p2715 tp2716 Rp2717 g2697 F21.0 tp2718 a(I4940000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x8c4@' p2719 tp2720 Rp2721 g2697 F21.0 tp2722 a(I4950000 g1 (g5 S'\n\xd7\xa3p=\x8a4@' p2723 tp2724 Rp2725 g2697 F20.0 tp2726 a(I4960000 g1 (g5 S'\x85\xebQ\xb8\x1e\x854@' p2727 tp2728 Rp2729 g2697 F19.0 tp2730 a(I4970000 g1 (g5 S'=\n\xd7\xa3p}4@' p2731 tp2732 Rp2733 g2697 F21.0 tp2734 a(I4980000 g1 (g5 S'=\n\xd7\xa3p}4@' p2735 tp2736 Rp2737 g2697 F20.0 tp2738 a(I4990000 g1 (g5 S'33333s4@' p2739 tp2740 Rp2741 g2697 F20.0 tp2742 a(I5000000 g1 (g5 S'\xecQ\xb8\x1e\x85k4@' p2743 tp2744 Rp2745 g2697 F21.0 tp2746 a(I5010000 g1 (g5 S'q=\n\xd7\xa3p4@' p2747 tp2748 Rp2749 g2697 F21.0 tp2750 a(I5020000 g1 (g5 S'\xf6(\\\x8f\xc2u4@' p2751 tp2752 Rp2753 g2697 F21.0 tp2754 a(I5030000 g1 (g5 S'\xecQ\xb8\x1e\x85k4@' p2755 tp2756 Rp2757 g2697 F19.0 tp2758 a(I5040000 g1 (g5 S'q=\n\xd7\xa3p4@' p2759 tp2760 Rp2761 g2697 F21.0 tp2762 a(I5050000 g1 (g5 S'\xf6(\\\x8f\xc2u4@' p2763 tp2764 Rp2765 g2697 F21.0 tp2766 a(I5060000 g1 (g5 S'\xb8\x1e\x85\xebQx4@' p2767 tp2768 Rp2769 g2697 F21.0 tp2770 a(I5070000 g1 (g5 S'\xc3\xf5(\\\x8f\x824@' p2771 tp2772 Rp2773 g2697 F21.0 tp2774 a(I5080000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x8c4@' p2775 tp2776 Rp2777 g2697 F21.0 tp2778 a(I5090000 g1 (g5 S'\n\xd7\xa3p=\x8a4@' p2779 tp2780 Rp2781 g2697 F21.0 tp2782 a(I5100000 g1 (g5 S'H\xe1z\x14\xae\x874@' p2783 tp2784 Rp2785 g2697 F21.0 tp2786 a(I5110000 g1 (g5 S'H\xe1z\x14\xae\x874@' p2787 tp2788 Rp2789 g2697 F20.0 tp2790 a(I5120000 g1 (g5 S'\x85\xebQ\xb8\x1e\x854@' p2791 tp2792 Rp2793 g2697 F20.0 tp2794 a(I5130000 g1 (g5 S'=\n\xd7\xa3p}4@' p2795 tp2796 Rp2797 g2697 F20.0 tp2798 a(I5140000 g1 (g5 S'\x85\xebQ\xb8\x1e\x854@' p2799 tp2800 Rp2801 g2697 F21.0 tp2802 a(I5150000 g1 (g5 S'\xc3\xf5(\\\x8f\x824@' p2803 tp2804 Rp2805 g2697 F21.0 tp2806 a(I5160000 g1 (g5 S'=\n\xd7\xa3p}4@' p2807 tp2808 Rp2809 g2697 F20.0 tp2810 a(I5170000 g1 (g5 S'H\xe1z\x14\xae\x874@' p2811 tp2812 Rp2813 g2697 F21.0 tp2814 a(I5180000 g1 (g5 S'H\xe1z\x14\xae\x874@' p2815 tp2816 Rp2817 g2697 F21.0 tp2818 a(I5190000 g1 (g5 S'\x00\x00\x00\x00\x00\x804@' p2819 tp2820 Rp2821 g2697 F20.0 tp2822 a(I5200000 g1 (g5 S'\x85\xebQ\xb8\x1e\x854@' p2823 tp2824 Rp2825 g2697 F21.0 tp2826 a(I5210000 g1 (g5 S'\x85\xebQ\xb8\x1e\x854@' p2827 tp2828 Rp2829 g2697 F21.0 tp2830 a(I5220000 g1 (g5 S'\x00\x00\x00\x00\x00\x804@' p2831 tp2832 Rp2833 g2697 F21.0 tp2834 a(I5230000 g1 (g5 S'\xb8\x1e\x85\xebQx4@' p2835 tp2836 Rp2837 g2697 F20.0 tp2838 a(I5240000 g1 (g5 S'\xf6(\\\x8f\xc2u4@' p2839 tp2840 Rp2841 g2697 F21.0 tp2842 a(I5250000 g1 (g5 S'33333s4@' p2843 tp2844 Rp2845 g2697 F21.0 tp2846 a(I5260000 g1 (g5 S'\xaeG\xe1z\x14n4@' p2847 tp2848 Rp2849 g2697 F21.0 tp2850 a(I5270000 g1 (g5 S'\xaeG\xe1z\x14n4@' p2851 tp2852 Rp2853 g2697 F21.0 tp2854 a(I5280000 g1 (g5 S')\\\x8f\xc2\xf5h4@' p2855 tp2856 Rp2857 g2697 F20.0 tp2858 a(I5290000 g1 (g5 S')\\\x8f\xc2\xf5h4@' p2859 tp2860 Rp2861 g2697 F21.0 tp2862 a(I5300000 g1 (g5 S'\xf6(\\\x8f\xc2u4@' p2863 tp2864 Rp2865 g2697 F20.0 tp2866 a(I5310000 g1 (g5 S'{\x14\xaeG\xe1z4@' p2867 tp2868 Rp2869 g2697 F21.0 tp2870 a(I5320000 g1 (g5 S'\x00\x00\x00\x00\x00\x804@' p2871 tp2872 Rp2873 g2697 F21.0 tp2874 a(I5330000 g1 (g5 S'H\xe1z\x14\xae\x874@' p2875 tp2876 Rp2877 g2697 F21.0 tp2878 a(I5340000 g1 (g5 S'\xc3\xf5(\\\x8f\x824@' p2879 tp2880 Rp2881 g2697 F21.0 tp2882 a(I5350000 g1 (g5 S'=\n\xd7\xa3p}4@' p2883 tp2884 Rp2885 g2697 F21.0 tp2886 a(I5360000 g1 (g5 S'\x85\xebQ\xb8\x1e\x854@' p2887 tp2888 Rp2889 g2697 F21.0 tp2890 a(I5370000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x8c4@' p2891 tp2892 Rp2893 g2697 F21.0 tp2894 a(I5380000 g1 (g5 S'\n\xd7\xa3p=\x8a4@' p2895 tp2896 Rp2897 g2697 F21.0 tp2898 a(I5390000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x8c4@' p2899 tp2900 Rp2901 g2697 F21.0 tp2902 a(I5400000 g1 (g5 S'\xd7\xa3p=\n\x974@' p2903 tp2904 Rp2905 g1 (g5 S'\xd7\xa3p=\n\x974@' p2906 tp2907 Rp2908 F20.0 tp2909 a(I5410000 g1 (g5 S'\xd7\xa3p=\n\x974@' p2910 tp2911 Rp2912 g2908 F21.0 tp2913 a(I5420000 g1 (g5 S'\xe1z\x14\xaeG\xa14@' p2914 tp2915 Rp2916 g1 (g5 S'\xe1z\x14\xaeG\xa14@' p2917 tp2918 Rp2919 F21.0 tp2920 a(I5430000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab4@' p2921 tp2922 Rp2923 g1 (g5 S'\xecQ\xb8\x1e\x85\xab4@' p2924 tp2925 Rp2926 F21.0 tp2927 a(I5440000 g1 (g5 S'fffff\xa64@' p2928 tp2929 Rp2930 g2926 F19.0 tp2931 a(I5450000 g1 (g5 S'\xe1z\x14\xaeG\xa14@' p2932 tp2933 Rp2934 g2926 F21.0 tp2935 a(I5460000 g1 (g5 S'\x9a\x99\x99\x99\x99\x994@' p2936 tp2937 Rp2938 g2926 F21.0 tp2939 a(I5470000 g1 (g5 S'\\\x8f\xc2\xf5(\x9c4@' p2940 tp2941 Rp2942 g2926 F21.0 tp2943 a(I5480000 g1 (g5 S'\\\x8f\xc2\xf5(\x9c4@' p2944 tp2945 Rp2946 g2926 F21.0 tp2947 a(I5490000 g1 (g5 S'\x14\xaeG\xe1z\x944@' p2948 tp2949 Rp2950 g2926 F20.0 tp2951 a(I5500000 g1 (g5 S'\xd7\xa3p=\n\x974@' p2952 tp2953 Rp2954 g2926 F21.0 tp2955 a(I5510000 g1 (g5 S'\x14\xaeG\xe1z\x944@' p2956 tp2957 Rp2958 g2926 F21.0 tp2959 a(I5520000 g1 (g5 S'R\xb8\x1e\x85\xeb\x914@' p2960 tp2961 Rp2962 g2926 F21.0 tp2963 a(I5530000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x8c4@' p2964 tp2965 Rp2966 g2926 F21.0 tp2967 a(I5540000 g1 (g5 S'\xc3\xf5(\\\x8f\x824@' p2968 tp2969 Rp2970 g2926 F20.0 tp2971 a(I5550000 g1 (g5 S'H\xe1z\x14\xae\x874@' p2972 tp2973 Rp2974 g2926 F20.0 tp2975 a(I5560000 g1 (g5 S'\n\xd7\xa3p=\x8a4@' p2976 tp2977 Rp2978 g2926 F21.0 tp2979 a(I5570000 g1 (g5 S'\x8f\xc2\xf5(\\\x8f4@' p2980 tp2981 Rp2982 g2926 F20.0 tp2983 a(I5580000 g1 (g5 S'\xc3\xf5(\\\x8f\x824@' p2984 tp2985 Rp2986 g2926 F20.0 tp2987 a(I5590000 g1 (g5 S'=\n\xd7\xa3p}4@' p2988 tp2989 Rp2990 g2926 F20.0 tp2991 a(I5600000 g1 (g5 S'q=\n\xd7\xa3p4@' p2992 tp2993 Rp2994 g2926 F19.0 tp2995 a(I5610000 g1 (g5 S'\xaeG\xe1z\x14n4@' p2996 tp2997 Rp2998 g2926 F20.0 tp2999 a(I5620000 g1 (g5 S'=\n\xd7\xa3p}4@' p3000 tp3001 Rp3002 g2926 F20.0 tp3003 a(I5630000 g1 (g5 S'=\n\xd7\xa3p}4@' p3004 tp3005 Rp3006 g2926 F20.0 tp3007 a(I5640000 g1 (g5 S'\x00\x00\x00\x00\x00\x804@' p3008 tp3009 Rp3010 g2926 F21.0 tp3011 a(I5650000 g1 (g5 S'\x00\x00\x00\x00\x00\x804@' p3012 tp3013 Rp3014 g2926 F21.0 tp3015 a(I5660000 g1 (g5 S'\n\xd7\xa3p=\x8a4@' p3016 tp3017 Rp3018 g2926 F21.0 tp3019 a(I5670000 g1 (g5 S'\x85\xebQ\xb8\x1e\x854@' p3020 tp3021 Rp3022 g2926 F20.0 tp3023 a(I5680000 g1 (g5 S'\x85\xebQ\xb8\x1e\x854@' p3024 tp3025 Rp3026 g2926 F19.0 tp3027 a(I5690000 g1 (g5 S'\n\xd7\xa3p=\x8a4@' p3028 tp3029 Rp3030 g2926 F21.0 tp3031 a(I5700000 g1 (g5 S'\x85\xebQ\xb8\x1e\x854@' p3032 tp3033 Rp3034 g2926 F20.0 tp3035 a(I5710000 g1 (g5 S'\n\xd7\xa3p=\x8a4@' p3036 tp3037 Rp3038 g2926 F20.0 tp3039 a(I5720000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x8c4@' p3040 tp3041 Rp3042 g2926 F20.0 tp3043 a(I5730000 g1 (g5 S'\x85\xebQ\xb8\x1e\x854@' p3044 tp3045 Rp3046 g2926 F21.0 tp3047 a(I5740000 g1 (g5 S'\x85\xebQ\xb8\x1e\x854@' p3048 tp3049 Rp3050 g2926 F21.0 tp3051 a(I5750000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\x8c4@' p3052 tp3053 Rp3054 g2926 F20.0 tp3055 a(I5760000 g1 (g5 S'\x14\xaeG\xe1z\x944@' p3056 tp3057 Rp3058 g2926 F21.0 tp3059 a(I5770000 g1 (g5 S'\\\x8f\xc2\xf5(\x9c4@' p3060 tp3061 Rp3062 g2926 F19.0 tp3063 a(I5780000 g1 (g5 S'\xe1z\x14\xaeG\xa14@' p3064 tp3065 Rp3066 g2926 F21.0 tp3067 a(I5790000 g1 (g5 S')\\\x8f\xc2\xf5\xa84@' p3068 tp3069 Rp3070 g2926 F21.0 tp3071 a(I5800000 g1 (g5 S')\\\x8f\xc2\xf5\xa84@' p3072 tp3073 Rp3074 g2926 F21.0 tp3075 a(I5810000 g1 (g5 S')\\\x8f\xc2\xf5\xa84@' p3076 tp3077 Rp3078 g2926 F20.0 tp3079 a(I5820000 g1 (g5 S'fffff\xa64@' p3080 tp3081 Rp3082 g2926 F21.0 tp3083 a(I5830000 g1 (g5 S'\xa4p=\n\xd7\xa34@' p3084 tp3085 Rp3086 g2926 F21.0 tp3087 a(I5840000 g1 (g5 S')\\\x8f\xc2\xf5\xa84@' p3088 tp3089 Rp3090 g2926 F21.0 tp3091 a(I5850000 g1 (g5 S'\xaeG\xe1z\x14\xae4@' p3092 tp3093 Rp3094 g1 (g5 S'\xaeG\xe1z\x14\xae4@' p3095 tp3096 Rp3097 F20.0 tp3098 a(I5860000 g1 (g5 S'\xaeG\xe1z\x14\xae4@' p3099 tp3100 Rp3101 g1 (g5 S'q=\n\xd7\xa3\xb04@' p3102 tp3103 Rp3104 F21.0 tp3105 a(I5870000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p3106 tp3107 Rp3108 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p3109 tp3110 Rp3111 F21.0 tp3112 a(I5880000 g1 (g5 S'33333\xb34@' p3113 tp3114 Rp3115 g3111 F21.0 tp3116 a(I5890000 g1 (g5 S'33333\xb34@' p3117 tp3118 Rp3119 g3111 F21.0 tp3120 a(I5900000 g1 (g5 S'\xf6(\\\x8f\xc2\xb54@' p3121 tp3122 Rp3123 g3111 F21.0 tp3124 a(I5910000 g1 (g5 S'\xf6(\\\x8f\xc2\xb54@' p3125 tp3126 Rp3127 g3111 F21.0 tp3128 a(I5920000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p3129 tp3130 Rp3131 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p3132 tp3133 Rp3134 F21.0 tp3135 a(I5930000 g1 (g5 S'q=\n\xd7\xa3\xb04@' p3136 tp3137 Rp3138 g3134 F21.0 tp3139 a(I5940000 g1 (g5 S'33333\xb34@' p3140 tp3141 Rp3142 g3134 F20.0 tp3143 a(I5950000 g1 (g5 S'\xaeG\xe1z\x14\xae4@' p3144 tp3145 Rp3146 g3134 F21.0 tp3147 a(I5960000 g1 (g5 S'\xaeG\xe1z\x14\xae4@' p3148 tp3149 Rp3150 g3134 F21.0 tp3151 a(I5970000 g1 (g5 S'33333\xb34@' p3152 tp3153 Rp3154 g3134 F21.0 tp3155 a(I5980000 g1 (g5 S'fffff\xa64@' p3156 tp3157 Rp3158 g3134 F21.0 tp3159 a(I5990000 g1 (g5 S'fffff\xa64@' p3160 tp3161 Rp3162 g3134 F21.0 tp3163 a(I6000000 g1 (g5 S'\xa4p=\n\xd7\xa34@' p3164 tp3165 Rp3166 g3134 F20.0 tp3167 a(I6010000 g1 (g5 S'\xa4p=\n\xd7\xa34@' p3168 tp3169 Rp3170 g3134 F21.0 tp3171 a(I6020000 g1 (g5 S')\\\x8f\xc2\xf5\xa84@' p3172 tp3173 Rp3174 g3134 F21.0 tp3175 a(I6030000 g1 (g5 S')\\\x8f\xc2\xf5\xa84@' p3176 tp3177 Rp3178 g3134 F21.0 tp3179 a(I6040000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab4@' p3180 tp3181 Rp3182 g3134 F21.0 tp3183 a(I6050000 g1 (g5 S'q=\n\xd7\xa3\xb04@' p3184 tp3185 Rp3186 g3134 F21.0 tp3187 a(I6060000 g1 (g5 S'33333\xb34@' p3188 tp3189 Rp3190 g3134 F19.0 tp3191 a(I6070000 g1 (g5 S'\xf6(\\\x8f\xc2\xb54@' p3192 tp3193 Rp3194 g3134 F20.0 tp3195 a(I6080000 g1 (g5 S'\xf6(\\\x8f\xc2\xb54@' p3196 tp3197 Rp3198 g3134 F21.0 tp3199 a(I6090000 g1 (g5 S'33333\xb34@' p3200 tp3201 Rp3202 g3134 F21.0 tp3203 a(I6100000 g1 (g5 S'q=\n\xd7\xa3\xb04@' p3204 tp3205 Rp3206 g3134 F21.0 tp3207 a(I6110000 g1 (g5 S'fffff\xa64@' p3208 tp3209 Rp3210 g3134 F21.0 tp3211 a(I6120000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab4@' p3212 tp3213 Rp3214 g3134 F21.0 tp3215 a(I6130000 g1 (g5 S'\xaeG\xe1z\x14\xae4@' p3216 tp3217 Rp3218 g3134 F21.0 tp3219 a(I6140000 g1 (g5 S')\\\x8f\xc2\xf5\xa84@' p3220 tp3221 Rp3222 g3134 F21.0 tp3223 a(I6150000 g1 (g5 S'33333\xb34@' p3224 tp3225 Rp3226 g3134 F20.0 tp3227 a(I6160000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p3228 tp3229 Rp3230 g3134 F21.0 tp3231 a(I6170000 g1 (g5 S'=\n\xd7\xa3p\xbd4@' p3232 tp3233 Rp3234 g1 (g5 S'=\n\xd7\xa3p\xbd4@' p3235 tp3236 Rp3237 F21.0 tp3238 a(I6180000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p3239 tp3240 Rp3241 g3237 F21.0 tp3242 a(I6190000 g1 (g5 S'q=\n\xd7\xa3\xb04@' p3243 tp3244 Rp3245 g3237 F20.0 tp3246 a(I6200000 g1 (g5 S'\xf6(\\\x8f\xc2\xb54@' p3247 tp3248 Rp3249 g3237 F21.0 tp3250 a(I6210000 g1 (g5 S'\xf6(\\\x8f\xc2\xb54@' p3251 tp3252 Rp3253 g3237 F20.0 tp3254 a(I6220000 g1 (g5 S'q=\n\xd7\xa3\xb04@' p3255 tp3256 Rp3257 g3237 F20.0 tp3258 a(I6230000 g1 (g5 S')\\\x8f\xc2\xf5\xa84@' p3259 tp3260 Rp3261 g3237 F21.0 tp3262 a(I6240000 g1 (g5 S')\\\x8f\xc2\xf5\xa84@' p3263 tp3264 Rp3265 g3237 F21.0 tp3266 a(I6250000 g1 (g5 S'fffff\xa64@' p3267 tp3268 Rp3269 g3237 F21.0 tp3270 a(I6260000 g1 (g5 S')\\\x8f\xc2\xf5\xa84@' p3271 tp3272 Rp3273 g3237 F21.0 tp3274 a(I6270000 g1 (g5 S'q=\n\xd7\xa3\xb04@' p3275 tp3276 Rp3277 g3237 F21.0 tp3278 a(I6280000 g1 (g5 S'\xf6(\\\x8f\xc2\xb54@' p3279 tp3280 Rp3281 g3237 F21.0 tp3282 a(I6290000 g1 (g5 S'33333\xb34@' p3283 tp3284 Rp3285 g3237 F20.0 tp3286 a(I6300000 g1 (g5 S'33333\xb34@' p3287 tp3288 Rp3289 g3237 F21.0 tp3290 a(I6310000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p3291 tp3292 Rp3293 g3237 F21.0 tp3294 a(I6320000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p3295 tp3296 Rp3297 g3237 F21.0 tp3298 a(I6330000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p3299 tp3300 Rp3301 g3237 F21.0 tp3302 a(I6340000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p3303 tp3304 Rp3305 g3237 F21.0 tp3306 a(I6350000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p3307 tp3308 Rp3309 g3237 F20.0 tp3310 a(I6360000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p3311 tp3312 Rp3313 g3237 F21.0 tp3314 a(I6370000 g1 (g5 S'33333\xb34@' p3315 tp3316 Rp3317 g3237 F20.0 tp3318 a(I6380000 g1 (g5 S'\xf6(\\\x8f\xc2\xb54@' p3319 tp3320 Rp3321 g3237 F21.0 tp3322 a(I6390000 g1 (g5 S'=\n\xd7\xa3p\xbd4@' p3323 tp3324 Rp3325 g3237 F21.0 tp3326 a(I6400000 g1 (g5 S'\x00\x00\x00\x00\x00\xc04@' p3327 tp3328 Rp3329 g1 (g5 S'\xc3\xf5(\\\x8f\xc24@' p3330 tp3331 Rp3332 F21.0 tp3333 a(I6410000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p3334 tp3335 Rp3336 g3332 F20.0 tp3337 a(I6420000 g1 (g5 S'\xf6(\\\x8f\xc2\xb54@' p3338 tp3339 Rp3340 g3332 F21.0 tp3341 a(I6430000 g1 (g5 S'\xf6(\\\x8f\xc2\xb54@' p3342 tp3343 Rp3344 g3332 F21.0 tp3345 a(I6440000 g1 (g5 S'q=\n\xd7\xa3\xb04@' p3346 tp3347 Rp3348 g3332 F20.0 tp3349 a(I6450000 g1 (g5 S'q=\n\xd7\xa3\xb04@' p3350 tp3351 Rp3352 g3332 F20.0 tp3353 a(I6460000 g1 (g5 S'q=\n\xd7\xa3\xb04@' p3354 tp3355 Rp3356 g3332 F20.0 tp3357 a(I6470000 g1 (g5 S'\xaeG\xe1z\x14\xae4@' p3358 tp3359 Rp3360 g3332 F20.0 tp3361 a(I6480000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab4@' p3362 tp3363 Rp3364 g3332 F21.0 tp3365 a(I6490000 g1 (g5 S'q=\n\xd7\xa3\xb04@' p3366 tp3367 Rp3368 g3332 F21.0 tp3369 a(I6500000 g1 (g5 S'33333\xb34@' p3370 tp3371 Rp3372 g3332 F21.0 tp3373 a(I6510000 g1 (g5 S')\\\x8f\xc2\xf5\xa84@' p3374 tp3375 Rp3376 g3332 F19.0 tp3377 a(I6520000 g1 (g5 S')\\\x8f\xc2\xf5\xa84@' p3378 tp3379 Rp3380 g3332 F21.0 tp3381 a(I6530000 g1 (g5 S'\xaeG\xe1z\x14\xae4@' p3382 tp3383 Rp3384 g3332 F21.0 tp3385 a(I6540000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab4@' p3386 tp3387 Rp3388 g3332 F20.0 tp3389 a(I6550000 g1 (g5 S')\\\x8f\xc2\xf5\xa84@' p3390 tp3391 Rp3392 g3332 F20.0 tp3393 a(I6560000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab4@' p3394 tp3395 Rp3396 g3332 F21.0 tp3397 a(I6570000 g1 (g5 S'33333\xb34@' p3398 tp3399 Rp3400 g3332 F21.0 tp3401 a(I6580000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p3402 tp3403 Rp3404 g3332 F20.0 tp3405 a(I6590000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p3406 tp3407 Rp3408 g3332 F21.0 tp3409 a(I6600000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p3410 tp3411 Rp3412 g3332 F21.0 tp3413 a(I6610000 g1 (g5 S'\x00\x00\x00\x00\x00\xc04@' p3414 tp3415 Rp3416 g3332 F21.0 tp3417 a(I6620000 g1 (g5 S'\xc3\xf5(\\\x8f\xc24@' p3418 tp3419 Rp3420 g3332 F21.0 tp3421 a(I6630000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p3422 tp3423 Rp3424 g3332 F21.0 tp3425 a(I6640000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p3426 tp3427 Rp3428 g3332 F21.0 tp3429 a(I6650000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p3430 tp3431 Rp3432 g3332 F21.0 tp3433 a(I6660000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p3434 tp3435 Rp3436 g3332 F21.0 tp3437 a(I6670000 g1 (g5 S'33333\xb34@' p3438 tp3439 Rp3440 g3332 F20.0 tp3441 a(I6680000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p3442 tp3443 Rp3444 g3332 F21.0 tp3445 a(I6690000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p3446 tp3447 Rp3448 g3332 F20.0 tp3449 a(I6700000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p3450 tp3451 Rp3452 g3332 F21.0 tp3453 a(I6710000 g1 (g5 S'=\n\xd7\xa3p\xbd4@' p3454 tp3455 Rp3456 g3332 F21.0 tp3457 a(I6720000 g1 (g5 S'\x00\x00\x00\x00\x00\xc04@' p3458 tp3459 Rp3460 g3332 F21.0 tp3461 a(I6730000 g1 (g5 S'=\n\xd7\xa3p\xbd4@' p3462 tp3463 Rp3464 g3332 F21.0 tp3465 a(I6740000 g1 (g5 S'\x00\x00\x00\x00\x00\xc04@' p3466 tp3467 Rp3468 g3332 F21.0 tp3469 a(I6750000 g1 (g5 S'\x00\x00\x00\x00\x00\xc04@' p3470 tp3471 Rp3472 g3332 F21.0 tp3473 a(I6760000 g1 (g5 S'=\n\xd7\xa3p\xbd4@' p3474 tp3475 Rp3476 g3332 F20.0 tp3477 a(I6770000 g1 (g5 S'\x00\x00\x00\x00\x00\xc04@' p3478 tp3479 Rp3480 g3332 F21.0 tp3481 a(I6780000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc54@' p3482 tp3483 Rp3484 g1 (g5 S'\x85\xebQ\xb8\x1e\xc54@' p3485 tp3486 Rp3487 F21.0 tp3488 a(I6790000 g1 (g5 S'\n\xd7\xa3p=\xca4@' p3489 tp3490 Rp3491 g1 (g5 S'\n\xd7\xa3p=\xca4@' p3492 tp3493 Rp3494 F21.0 tp3495 a(I6800000 g1 (g5 S'R\xb8\x1e\x85\xeb\xd14@' p3496 tp3497 Rp3498 g1 (g5 S'R\xb8\x1e\x85\xeb\xd14@' p3499 tp3500 Rp3501 F21.0 tp3502 a(I6810000 g1 (g5 S'\x14\xaeG\xe1z\xd44@' p3503 tp3504 Rp3505 g1 (g5 S'\x14\xaeG\xe1z\xd44@' p3506 tp3507 Rp3508 F21.0 tp3509 a(I6820000 g1 (g5 S'R\xb8\x1e\x85\xeb\xd14@' p3510 tp3511 Rp3512 g3508 F21.0 tp3513 a(I6830000 g1 (g5 S'\x8f\xc2\xf5(\\\xcf4@' p3514 tp3515 Rp3516 g3508 F20.0 tp3517 a(I6840000 g1 (g5 S'\x8f\xc2\xf5(\\\xcf4@' p3518 tp3519 Rp3520 g3508 F21.0 tp3521 a(I6850000 g1 (g5 S'\x14\xaeG\xe1z\xd44@' p3522 tp3523 Rp3524 g1 (g5 S'\xd7\xa3p=\n\xd74@' p3525 tp3526 Rp3527 F21.0 tp3528 a(I6860000 g1 (g5 S'\xd7\xa3p=\n\xd74@' p3529 tp3530 Rp3531 g3527 F21.0 tp3532 a(I6870000 g1 (g5 S'\x14\xaeG\xe1z\xd44@' p3533 tp3534 Rp3535 g1 (g5 S'\x9a\x99\x99\x99\x99\xd94@' p3536 tp3537 Rp3538 F21.0 tp3539 a(I6880000 g1 (g5 S'\x14\xaeG\xe1z\xd44@' p3540 tp3541 Rp3542 g3538 F20.0 tp3543 a(I6890000 g1 (g5 S'\x14\xaeG\xe1z\xd44@' p3544 tp3545 Rp3546 g3538 F21.0 tp3547 a(I6900000 g1 (g5 S'\x14\xaeG\xe1z\xd44@' p3548 tp3549 Rp3550 g3538 F21.0 tp3551 a(I6910000 g1 (g5 S'R\xb8\x1e\x85\xeb\xd14@' p3552 tp3553 Rp3554 g3538 F21.0 tp3555 a(I6920000 g1 (g5 S'\x8f\xc2\xf5(\\\xcf4@' p3556 tp3557 Rp3558 g3538 F20.0 tp3559 a(I6930000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc4@' p3560 tp3561 Rp3562 g3538 F21.0 tp3563 a(I6940000 g1 (g5 S'H\xe1z\x14\xae\xc74@' p3564 tp3565 Rp3566 g3538 F21.0 tp3567 a(I6950000 g1 (g5 S'\xc3\xf5(\\\x8f\xc24@' p3568 tp3569 Rp3570 g3538 F21.0 tp3571 a(I6960000 g1 (g5 S'\xc3\xf5(\\\x8f\xc24@' p3572 tp3573 Rp3574 g3538 F20.0 tp3575 a(I6970000 g1 (g5 S'=\n\xd7\xa3p\xbd4@' p3576 tp3577 Rp3578 g3538 F20.0 tp3579 a(I6980000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p3580 tp3581 Rp3582 g3538 F20.0 tp3583 a(I6990000 g1 (g5 S'q=\n\xd7\xa3\xb04@' p3584 tp3585 Rp3586 g3538 F21.0 tp3587 a(I7000000 g1 (g5 S'33333\xb34@' p3588 tp3589 Rp3590 g3538 F21.0 tp3591 a(I7010000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p3592 tp3593 Rp3594 g3538 F21.0 tp3595 a(I7020000 g1 (g5 S'\xf6(\\\x8f\xc2\xb54@' p3596 tp3597 Rp3598 g3538 F21.0 tp3599 a(I7030000 g1 (g5 S'q=\n\xd7\xa3\xb04@' p3600 tp3601 Rp3602 g3538 F20.0 tp3603 a(I7040000 g1 (g5 S'q=\n\xd7\xa3\xb04@' p3604 tp3605 Rp3606 g3538 F20.0 tp3607 a(I7050000 g1 (g5 S'q=\n\xd7\xa3\xb04@' p3608 tp3609 Rp3610 g3538 F21.0 tp3611 a(I7060000 g1 (g5 S'\xf6(\\\x8f\xc2\xb54@' p3612 tp3613 Rp3614 g3538 F21.0 tp3615 a(I7070000 g1 (g5 S'q=\n\xd7\xa3\xb04@' p3616 tp3617 Rp3618 g3538 F21.0 tp3619 a(I7080000 g1 (g5 S'\xaeG\xe1z\x14\xae4@' p3620 tp3621 Rp3622 g3538 F21.0 tp3623 a(I7090000 g1 (g5 S')\\\x8f\xc2\xf5\xa84@' p3624 tp3625 Rp3626 g3538 F18.0 tp3627 a(I7100000 g1 (g5 S')\\\x8f\xc2\xf5\xa84@' p3628 tp3629 Rp3630 g3538 F21.0 tp3631 a(I7110000 g1 (g5 S'\xaeG\xe1z\x14\xae4@' p3632 tp3633 Rp3634 g3538 F20.0 tp3635 a(I7120000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab4@' p3636 tp3637 Rp3638 g3538 F21.0 tp3639 a(I7130000 g1 (g5 S'\xaeG\xe1z\x14\xae4@' p3640 tp3641 Rp3642 g3538 F21.0 tp3643 a(I7140000 g1 (g5 S'q=\n\xd7\xa3\xb04@' p3644 tp3645 Rp3646 g3538 F20.0 tp3647 a(I7150000 g1 (g5 S'\xf6(\\\x8f\xc2\xb54@' p3648 tp3649 Rp3650 g3538 F21.0 tp3651 a(I7160000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p3652 tp3653 Rp3654 g3538 F21.0 tp3655 a(I7170000 g1 (g5 S'33333\xb34@' p3656 tp3657 Rp3658 g3538 F19.0 tp3659 a(I7180000 g1 (g5 S'\xecQ\xb8\x1e\x85\xab4@' p3660 tp3661 Rp3662 g3538 F20.0 tp3663 a(I7190000 g1 (g5 S'q=\n\xd7\xa3\xb04@' p3664 tp3665 Rp3666 g3538 F21.0 tp3667 a(I7200000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p3668 tp3669 Rp3670 g3538 F21.0 tp3671 a(I7210000 g1 (g5 S'\xf6(\\\x8f\xc2\xb54@' p3672 tp3673 Rp3674 g3538 F21.0 tp3675 a(I7220000 g1 (g5 S'\xb8\x1e\x85\xebQ\xb84@' p3676 tp3677 Rp3678 g3538 F21.0 tp3679 a(I7230000 g1 (g5 S'\xaeG\xe1z\x14\xae4@' p3680 tp3681 Rp3682 g3538 F19.0 tp3683 a(I7240000 g1 (g5 S'33333\xb34@' p3684 tp3685 Rp3686 g3538 F21.0 tp3687 a(I7250000 g1 (g5 S'33333\xb34@' p3688 tp3689 Rp3690 g3538 F21.0 tp3691 a(I7260000 g1 (g5 S'=\n\xd7\xa3p\xbd4@' p3692 tp3693 Rp3694 g3538 F21.0 tp3695 a(I7270000 g1 (g5 S'{\x14\xaeG\xe1\xba4@' p3696 tp3697 Rp3698 g3538 F19.0 tp3699 a(I7280000 g1 (g5 S'\xc3\xf5(\\\x8f\xc24@' p3700 tp3701 Rp3702 g3538 F21.0 tp3703 a(I7290000 g1 (g5 S'\xc3\xf5(\\\x8f\xc24@' p3704 tp3705 Rp3706 g3538 F21.0 tp3707 a(I7300000 g1 (g5 S'\xc3\xf5(\\\x8f\xc24@' p3708 tp3709 Rp3710 g3538 F21.0 tp3711 a(I7310000 g1 (g5 S'=\n\xd7\xa3p\xbd4@' p3712 tp3713 Rp3714 g3538 F21.0 tp3715 a(I7320000 g1 (g5 S'\x00\x00\x00\x00\x00\xc04@' p3716 tp3717 Rp3718 g3538 F21.0 tp3719 a(I7330000 g1 (g5 S'\x00\x00\x00\x00\x00\xc04@' p3720 tp3721 Rp3722 g3538 F20.0 tp3723 a(I7340000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc54@' p3724 tp3725 Rp3726 g3538 F20.0 tp3727 a(I7350000 g1 (g5 S'\n\xd7\xa3p=\xca4@' p3728 tp3729 Rp3730 g3538 F21.0 tp3731 a(I7360000 g1 (g5 S'\n\xd7\xa3p=\xca4@' p3732 tp3733 Rp3734 g3538 F21.0 tp3735 a(I7370000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc4@' p3736 tp3737 Rp3738 g3538 F21.0 tp3739 a(I7380000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc4@' p3740 tp3741 Rp3742 g3538 F21.0 tp3743 a(I7390000 g1 (g5 S'\x8f\xc2\xf5(\\\xcf4@' p3744 tp3745 Rp3746 g3538 F21.0 tp3747 a(I7400000 g1 (g5 S'R\xb8\x1e\x85\xeb\xd14@' p3748 tp3749 Rp3750 g3538 F21.0 tp3751 a(I7410000 g1 (g5 S'\x8f\xc2\xf5(\\\xcf4@' p3752 tp3753 Rp3754 g3538 F21.0 tp3755 a(I7420000 g1 (g5 S'\x8f\xc2\xf5(\\\xcf4@' p3756 tp3757 Rp3758 g3538 F21.0 tp3759 a(I7430000 g1 (g5 S'\n\xd7\xa3p=\xca4@' p3760 tp3761 Rp3762 g3538 F21.0 tp3763 a(I7440000 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc4@' p3764 tp3765 Rp3766 g3538 F21.0 tp3767 a(I7450000 g1 (g5 S'\x85\xebQ\xb8\x1e\xc54@' p3768 tp3769 Rp3770 g3538 F21.0 tp3771 a(I7460000 g1 (g5 S'H\xe1z\x14\xae\xc74@' p3772 tp3773 Rp3774 g3538 F21.0 tp3775 a(I7470000 g1 (g5 S'\n\xd7\xa3p=\xca4@' p3776 tp3777 Rp3778 g3538 F21.0 tp3779 a(I7476547 g1 (g5 S'\xcd\xcc\xcc\xcc\xcc\xcc4@' p3780 tp3781 Rp3782 g3538 F21.0 tp3783 a.(lp0 F-21.0 aF-21.0 aF-21.0 aF-21.0 aF-20.0 aF-21.0 aF-19.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-19.0 aF-20.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-18.0 aF-20.0 aF-19.0 aF-20.0 aF-18.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-19.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-20.0 aF-21.0 aF-20.0 aF-20.0 aF-20.0 aF-20.0 aF-19.0 aF-21.0 aF-19.0 aF-20.0 aF-20.0 aF-20.0 aF-21.0 aF-21.0 aF-20.0 aF-21.0 aF-21.0 aF-21.0 aF-20.0 aF-20.0 aF-20.0 aF-19.0 aF-18.0 aF-20.0 aF-18.0 aF-21.0 aF-21.0 aF-20.0 aF-21.0 aF-19.0 aF-19.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-19.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-20.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-19.0 aF-21.0 aF-20.0 aF-19.0 aF-21.0 aF-20.0 aF-18.0 aF-19.0 aF-20.0 aF-20.0 aF-21.0 aF-20.0 aF-21.0 aF-21.0 aF-20.0 aF-19.0 aF-20.0 aF-21.0 aF-20.0 aF-21.0 aF-19.0 aF-20.0 aF-20.0 aF-20.0 aF-20.0 aF-21.0 aF-21.0 aF-20.0 aF-21.0 aF-20.0 aF-21.0 aF-20.0 aF-21.0 aF-21.0 aF-19.0 aF-20.0 aF-20.0 aF-21.0 aF-20.0 aF-19.0 aF-21.0 aF-21.0 aF-20.0 aF-21.0 aF-20.0 aF-21.0 aF-21.0 aF-19.0 aF-19.0 aF-21.0 aF-20.0 aF-19.0 aF-21.0 aF-21.0 aF-20.0 aF-20.0 aF-21.0 aF-20.0 aF-19.0 aF-19.0 aF-20.0 aF-21.0 aF-21.0 aF-21.0 aF-20.0 aF-20.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-20.0 aF-21.0 aF-19.0 aF-20.0 aF-20.0 aF-20.0 aF-20.0 aF-19.0 aF-20.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-20.0 aF-21.0 aF-21.0 aF-21.0 aF-20.0 aF-20.0 aF-20.0 aF-17.0 aF-21.0 aF-20.0 aF-21.0 aF-20.0 aF-20.0 aF-21.0 aF-20.0 aF-21.0 aF-20.0 aF-21.0 aF-20.0 aF-20.0 aF-20.0 aF-21.0 aF-20.0 aF-21.0 aF-21.0 aF-19.0 aF-19.0 aF-21.0 aF-19.0 aF-21.0 aF-20.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-20.0 aF-21.0 aF-21.0 aF-21.0 aF-20.0 aF-20.0 aF-20.0 aF-21.0 aF-20.0 aF-20.0 aF-20.0 aF-21.0 aF-20.0 aF-21.0 aF-20.0 aF-20.0 aF-20.0 aF-21.0 aF-21.0 aF-21.0 aF-20.0 aF-21.0 aF-20.0 aF-20.0 aF-19.0 aF-21.0 aF-19.0 aF-21.0 aF-21.0 aF-21.0 aF-17.0 aF-21.0 aF-21.0 aF-21.0 aF-20.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-18.0 aF-20.0 aF-21.0 aF-18.0 aF-20.0 aF-20.0 aF-20.0 aF-17.0 aF-20.0 aF-20.0 aF-21.0 aF-21.0 aF-20.0 aF-20.0 aF-21.0 aF-21.0 aF-20.0 aF-21.0 aF-21.0 aF-20.0 aF-21.0 aF-21.0 aF-21.0 aF-19.0 aF-19.0 aF-21.0 aF-20.0 aF-18.0 aF-20.0 aF-21.0 aF-21.0 aF-18.0 aF-20.0 aF-21.0 aF-19.0 aF-20.0 aF-20.0 aF-21.0 aF-20.0 aF-18.0 aF-19.0 aF-21.0 aF-20.0 aF-20.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-20.0 aF-20.0 aF-20.0 aF-21.0 aF-21.0 aF-21.0 aF-20.0 aF-21.0 aF-21.0 aF-20.0 aF-20.0 aF-20.0 aF-19.0 aF-21.0 aF-19.0 aF-20.0 aF-21.0 aF-21.0 aF-18.0 aF-20.0 aF-21.0 aF-20.0 aF-20.0 aF-20.0 aF-20.0 aF-21.0 aF-21.0 aF-19.0 aF-20.0 aF-21.0 aF-20.0 aF-21.0 aF-17.0 aF-21.0 aF-21.0 aF-21.0 aF-20.0 aF-21.0 aF-21.0 aF-21.0 aF-19.0 aF-21.0 aF-20.0 aF-21.0 aF-21.0 aF-20.0 aF-21.0 aF-20.0 aF-21.0 aF-19.0 aF-21.0 aF-19.0 aF-21.0 aF-20.0 aF-20.0 aF-19.0 aF-21.0 aF-21.0 aF-20.0 aF-20.0 aF-20.0 aF-20.0 aF-21.0 aF-21.0 aF-21.0 aF-20.0 aF-20.0 aF-19.0 aF-21.0 aF-20.0 aF-20.0 aF-20.0 aF-20.0 aF-20.0 aF-20.0 aF-21.0 aF-20.0 aF-21.0 aF-20.0 aF-21.0 aF-20.0 aF-20.0 aF-19.0 aF-21.0 aF-21.0 aF-18.0 aF-21.0 aF-19.0 aF-18.0 aF-18.0 aF-19.0 aF-19.0 aF-20.0 aF-19.0 aF-19.0 aF-18.0 aF-18.0 aF-20.0 aF-21.0 aF-21.0 aF-21.0 aF-18.0 aF-19.0 aF-19.0 aF-18.0 aF-21.0 aF-21.0 aF-21.0 aF-21.0 aF-19.0 aF-20.0 aF-21.0 aF-21.0 aF-19.0 aF-21.0 aF-20.0 aF-19.0 aF-21.0 aF-19.0 aF-20.0 aF-18.0 aF-19.0 aF-19.0 aF-20.0 aF-20.0 aF-20.0 aF-19.0 aF-21.0 aF-16.0 aF-18.0 aF-18.0 aF-20.0 aF-20.0 aF-18.0 aF-21.0 aF-21.0 aF-17.0 aF-18.0 aF-19.0 aF-15.0 aF-18.0 aF-18.0 aF-18.0 aF-21.0 aF-18.0 aF-20.0 aF-20.0 aF-19.0 aF-19.0 aF-21.0 aF-20.0 aF-20.0 aF-17.0 aF-15.0 aF-16.0 aF-16.0 aF-16.0 aF-21.0 aF-20.0 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elapsed time: 91.9 seconds (0.03 hours) Timestep: 70000 mean reward (100 episodes): 339.6981 best mean reward: -inf current episode reward: 396.0000 episodes: 53 exploration: 0.93700 learning_rate: 0.00010 elapsed time: 124.4 seconds (0.03 hours) Timestep: 80000 mean reward (100 episodes): 338.9333 best mean reward: -inf current episode reward: 308.0000 episodes: 60 exploration: 0.92800 learning_rate: 0.00010 elapsed time: 156.8 seconds (0.04 hours) Timestep: 90000 mean reward (100 episodes): 346.2941 best mean reward: -inf current episode reward: 352.0000 episodes: 68 exploration: 0.91900 learning_rate: 0.00010 elapsed time: 190.4 seconds (0.05 hours) Timestep: 100000 mean reward (100 episodes): 351.7867 best mean reward: -inf current episode reward: 900.0000 episodes: 75 exploration: 0.91000 learning_rate: 0.00010 elapsed time: 223.4 seconds (0.06 hours) Timestep: 110000 mean reward (100 episodes): 354.5366 best mean reward: -inf current episode reward: 308.0000 episodes: 82 exploration: 0.90100 learning_rate: 0.00010 elapsed time: 256.6 seconds (0.07 hours) Timestep: 120000 mean reward (100 episodes): 351.8667 best mean reward: -inf current episode reward: 264.0000 episodes: 90 exploration: 0.89200 learning_rate: 0.00010 elapsed time: 289.6 seconds (0.08 hours) Timestep: 130000 mean reward (100 episodes): 349.1546 best mean reward: -inf current episode reward: 308.0000 episodes: 97 exploration: 0.88300 learning_rate: 0.00010 elapsed time: 322.9 seconds (0.09 hours) Timestep: 140000 mean reward (100 episodes): 347.0400 best mean reward: 348.8000 current episode reward: 264.0000 episodes: 105 exploration: 0.87400 learning_rate: 0.00010 elapsed time: 356.1 seconds (0.10 hours) Timestep: 150000 mean reward (100 episodes): 343.9600 best mean reward: 348.8000 current episode reward: 352.0000 episodes: 114 exploration: 0.86500 learning_rate: 0.00010 elapsed time: 389.6 seconds (0.11 hours) Timestep: 160000 mean reward (100 episodes): 348.8400 best mean reward: 351.0400 current episode reward: 352.0000 episodes: 122 exploration: 0.85600 learning_rate: 0.00010 elapsed time: 422.9 seconds (0.12 hours) Timestep: 170000 mean reward (100 episodes): 358.5200 best mean reward: 358.5200 current episode reward: 352.0000 episodes: 129 exploration: 0.84700 learning_rate: 0.00010 elapsed time: 458.3 seconds (0.13 hours) Timestep: 180000 mean reward (100 episodes): 362.4800 best mean reward: 363.8000 current episode reward: 440.0000 episodes: 136 exploration: 0.83800 learning_rate: 0.00010 elapsed time: 492.5 seconds (0.14 hours) Timestep: 190000 mean reward (100 episodes): 358.5200 best mean reward: 363.8000 current episode reward: 396.0000 episodes: 144 exploration: 0.82900 learning_rate: 0.00010 elapsed time: 527.1 seconds (0.15 hours) Timestep: 200000 mean reward (100 episodes): 360.6400 best mean reward: 365.5600 current episode reward: 264.0000 episodes: 151 exploration: 0.82000 learning_rate: 0.00010 elapsed time: 561.6 seconds (0.16 hours) Timestep: 210000 mean reward (100 episodes): 356.6800 best mean reward: 365.5600 current episode reward: 176.0000 episodes: 160 exploration: 0.81100 learning_rate: 0.00010 elapsed time: 596.2 seconds (0.17 hours) Timestep: 220000 mean reward (100 episodes): 354.0400 best mean reward: 365.5600 current episode reward: 176.0000 episodes: 167 exploration: 0.80200 learning_rate: 0.00010 elapsed time: 630.8 seconds (0.18 hours) Timestep: 230000 mean reward (100 episodes): 358.0800 best mean reward: 365.5600 current episode reward: 264.0000 episodes: 174 exploration: 0.79300 learning_rate: 0.00010 elapsed time: 665.5 seconds (0.18 hours) Timestep: 240000 mean reward (100 episodes): 354.4400 best mean reward: 365.5600 current episode reward: 440.0000 episodes: 181 exploration: 0.78400 learning_rate: 0.00010 elapsed time: 700.5 seconds (0.19 hours) Timestep: 250000 mean reward (100 episodes): 356.2000 best mean reward: 365.5600 current episode reward: 308.0000 episodes: 189 exploration: 0.77500 learning_rate: 0.00010 elapsed time: 735.6 seconds (0.20 hours) Timestep: 260000 mean reward (100 episodes): 363.2400 best mean reward: 365.5600 current episode reward: 440.0000 episodes: 196 exploration: 0.76600 learning_rate: 0.00010 elapsed time: 770.5 seconds (0.21 hours) Timestep: 270000 mean reward (100 episodes): 363.2400 best mean reward: 366.7600 current episode reward: 352.0000 episodes: 202 exploration: 0.75700 learning_rate: 0.00010 elapsed time: 805.9 seconds (0.22 hours) Timestep: 280000 mean reward (100 episodes): 369.8400 best mean reward: 369.8400 current episode reward: 176.0000 episodes: 210 exploration: 0.74800 learning_rate: 0.00010 elapsed time: 841.1 seconds (0.23 hours) Timestep: 290000 mean reward (100 episodes): 361.0000 best mean reward: 369.8400 current episode reward: 440.0000 episodes: 218 exploration: 0.73900 learning_rate: 0.00010 elapsed time: 877.7 seconds (0.24 hours) Timestep: 300000 mean reward (100 episodes): 349.5600 best mean reward: 369.8400 current episode reward: 132.0000 episodes: 226 exploration: 0.73000 learning_rate: 0.00010 elapsed time: 913.5 seconds (0.25 hours) Timestep: 310000 mean reward (100 episodes): 350.8800 best mean reward: 369.8400 current episode reward: 484.0000 episodes: 233 exploration: 0.72100 learning_rate: 0.00010 elapsed time: 949.2 seconds (0.26 hours) Timestep: 320000 mean reward (100 episodes): 357.9200 best mean reward: 369.8400 current episode reward: 176.0000 episodes: 241 exploration: 0.71200 learning_rate: 0.00010 elapsed time: 985.5 seconds (0.27 hours) Timestep: 330000 mean reward (100 episodes): 351.3200 best mean reward: 369.8400 current episode reward: 176.0000 episodes: 248 exploration: 0.70300 learning_rate: 0.00010 elapsed time: 1021.9 seconds (0.28 hours) Timestep: 340000 mean reward (100 episodes): 355.2800 best mean reward: 369.8400 current episode reward: 484.0000 episodes: 255 exploration: 0.69400 learning_rate: 0.00010 elapsed time: 1058.2 seconds (0.29 hours) Timestep: 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learning_rate: 0.00010 elapsed time: 1242.6 seconds (0.35 hours) Timestep: 400000 mean reward (100 episodes): 359.0400 best mean reward: 369.8400 current episode reward: 440.0000 episodes: 296 exploration: 0.64000 learning_rate: 0.00010 elapsed time: 1279.1 seconds (0.36 hours) Timestep: 410000 mean reward (100 episodes): 358.1600 best mean reward: 369.8400 current episode reward: 572.0000 episodes: 302 exploration: 0.63100 learning_rate: 0.00010 elapsed time: 1316.2 seconds (0.37 hours) Timestep: 420000 mean reward (100 episodes): 366.5200 best mean reward: 369.8400 current episode reward: 352.0000 episodes: 308 exploration: 0.62200 learning_rate: 0.00010 elapsed time: 1352.9 seconds (0.38 hours) Timestep: 430000 mean reward (100 episodes): 375.3200 best mean reward: 375.3200 current episode reward: 440.0000 episodes: 316 exploration: 0.61300 learning_rate: 0.00010 elapsed time: 1390.3 seconds (0.39 hours) Timestep: 440000 mean reward (100 episodes): 380.2400 best mean reward: 380.2400 current episode reward: 756.0000 episodes: 323 exploration: 0.60400 learning_rate: 0.00010 elapsed time: 1427.4 seconds (0.40 hours) Timestep: 450000 mean reward (100 episodes): 388.6000 best mean reward: 390.3600 current episode reward: 264.0000 episodes: 329 exploration: 0.59500 learning_rate: 0.00010 elapsed time: 1464.8 seconds (0.41 hours) Timestep: 460000 mean reward (100 episodes): 372.8800 best mean reward: 390.3600 current episode reward: 88.0000 episodes: 337 exploration: 0.58600 learning_rate: 0.00010 elapsed time: 1502.6 seconds (0.42 hours) Timestep: 470000 mean reward (100 episodes): 377.7200 best mean reward: 390.3600 current episode reward: 660.0000 episodes: 344 exploration: 0.57700 learning_rate: 0.00010 elapsed time: 1542.2 seconds (0.43 hours) Timestep: 480000 mean reward (100 episodes): 380.8000 best mean reward: 390.3600 current episode reward: 352.0000 episodes: 351 exploration: 0.56800 learning_rate: 0.00010 elapsed time: 1581.0 seconds (0.44 hours) Timestep: 490000 mean reward (100 episodes): 375.5200 best mean reward: 390.3600 current episode reward: 528.0000 episodes: 359 exploration: 0.55900 learning_rate: 0.00010 elapsed time: 1619.7 seconds (0.45 hours) Timestep: 500000 mean reward (100 episodes): 374.2000 best mean reward: 390.3600 current episode reward: 308.0000 episodes: 366 exploration: 0.55000 learning_rate: 0.00010 elapsed time: 1659.5 seconds (0.46 hours) Timestep: 510000 mean reward (100 episodes): 375.5200 best mean reward: 390.3600 current episode reward: 220.0000 episodes: 373 exploration: 0.54100 learning_rate: 0.00010 elapsed time: 1698.6 seconds (0.47 hours) Timestep: 520000 mean reward (100 episodes): 375.5200 best mean reward: 390.3600 current episode reward: 352.0000 episodes: 379 exploration: 0.53200 learning_rate: 0.00010 elapsed time: 1737.7 seconds (0.48 hours) Timestep: 530000 mean reward (100 episodes): 378.6000 best mean reward: 390.3600 current episode reward: 396.0000 episodes: 385 exploration: 0.52300 learning_rate: 0.00010 elapsed time: 1777.3 seconds (0.49 hours) Timestep: 540000 mean reward (100 episodes): 385.7200 best mean reward: 390.3600 current episode reward: 308.0000 episodes: 392 exploration: 0.51400 learning_rate: 0.00010 elapsed time: 1816.2 seconds (0.50 hours) Timestep: 550000 mean reward (100 episodes): 379.1200 best mean reward: 390.3600 current episode reward: 176.0000 episodes: 400 exploration: 0.50500 learning_rate: 0.00010 elapsed time: 1855.5 seconds (0.52 hours) Timestep: 560000 mean reward (100 episodes): 377.3600 best mean reward: 390.3600 current episode reward: 484.0000 episodes: 407 exploration: 0.49600 learning_rate: 0.00010 elapsed time: 1895.0 seconds (0.53 hours) Timestep: 570000 mean reward (100 episodes): 370.3200 best mean reward: 390.3600 current episode reward: 264.0000 episodes: 416 exploration: 0.48700 learning_rate: 0.00010 elapsed time: 1934.9 seconds (0.54 hours) Timestep: 580000 mean reward (100 episodes): 355.2800 best mean reward: 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Timestep: 630000 mean reward (100 episodes): 358.3600 best mean reward: 390.3600 current episode reward: 396.0000 episodes: 459 exploration: 0.43300 learning_rate: 0.00010 elapsed time: 2172.2 seconds (0.60 hours) Timestep: 640000 mean reward (100 episodes): 350.4400 best mean reward: 390.3600 current episode reward: 308.0000 episodes: 467 exploration: 0.42400 learning_rate: 0.00010 elapsed time: 2212.1 seconds (0.61 hours) Timestep: 650000 mean reward (100 episodes): 344.2800 best mean reward: 390.3600 current episode reward: 264.0000 episodes: 475 exploration: 0.41500 learning_rate: 0.00010 elapsed time: 2252.1 seconds (0.63 hours) Timestep: 660000 mean reward (100 episodes): 342.6000 best mean reward: 390.3600 current episode reward: 440.0000 episodes: 482 exploration: 0.40600 learning_rate: 0.00010 elapsed time: 2291.5 seconds (0.64 hours) Timestep: 670000 mean reward (100 episodes): 332.8400 best mean reward: 390.3600 current episode reward: 132.0000 episodes: 489 exploration: 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Timestep: 770000 mean reward (100 episodes): 346.0000 best mean reward: 390.3600 current episode reward: 440.0000 episodes: 560 exploration: 0.30700 learning_rate: 0.00010 elapsed time: 2742.2 seconds (0.76 hours) Timestep: 780000 mean reward (100 episodes): 355.2400 best mean reward: 390.3600 current episode reward: 264.0000 episodes: 567 exploration: 0.29800 learning_rate: 0.00010 elapsed time: 2784.1 seconds (0.77 hours) Timestep: 790000 mean reward (100 episodes): 358.3200 best mean reward: 390.3600 current episode reward: 660.0000 episodes: 573 exploration: 0.28900 learning_rate: 0.00010 elapsed time: 2825.5 seconds (0.78 hours) Timestep: 800000 mean reward (100 episodes): 357.3600 best mean reward: 390.3600 current episode reward: 264.0000 episodes: 580 exploration: 0.28000 learning_rate: 0.00010 elapsed time: 2866.7 seconds (0.80 hours) Timestep: 810000 mean reward (100 episodes): 356.9200 best mean reward: 390.3600 current episode reward: 220.0000 episodes: 587 exploration: 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Timestep: 910000 mean reward (100 episodes): 349.5200 best mean reward: 390.3600 current episode reward: 660.0000 episodes: 654 exploration: 0.18100 learning_rate: 0.00010 elapsed time: 3338.6 seconds (0.93 hours) Timestep: 920000 mean reward (100 episodes): 349.0800 best mean reward: 390.3600 current episode reward: 88.0000 episodes: 659 exploration: 0.17200 learning_rate: 0.00010 elapsed time: 3382.3 seconds (0.94 hours) Timestep: 930000 mean reward (100 episodes): 341.1600 best mean reward: 390.3600 current episode reward: 176.0000 episodes: 666 exploration: 0.16300 learning_rate: 0.00010 elapsed time: 3425.8 seconds (0.95 hours) Timestep: 940000 mean reward (100 episodes): 349.0800 best mean reward: 390.3600 current episode reward: 616.0000 episodes: 671 exploration: 0.15400 learning_rate: 0.00010 elapsed time: 3471.0 seconds (0.96 hours) Timestep: 950000 mean reward (100 episodes): 341.1600 best mean reward: 390.3600 current episode reward: 484.0000 episodes: 678 exploration: 0.14500 learning_rate: 0.00010 elapsed time: 3515.3 seconds (0.98 hours) Timestep: 960000 mean reward (100 episodes): 337.6400 best mean reward: 390.3600 current episode reward: 484.0000 episodes: 685 exploration: 0.13600 learning_rate: 0.00010 elapsed time: 3558.9 seconds (0.99 hours) Timestep: 970000 mean reward (100 episodes): 347.7600 best mean reward: 390.3600 current episode reward: 660.0000 episodes: 689 exploration: 0.12700 learning_rate: 0.00010 elapsed time: 3603.2 seconds (1.00 hours) Timestep: 980000 mean reward (100 episodes): 362.7200 best mean reward: 390.3600 current episode reward: 572.0000 episodes: 694 exploration: 0.11800 learning_rate: 0.00010 elapsed time: 3647.3 seconds (1.01 hours) Timestep: 990000 mean reward (100 episodes): 356.9600 best mean reward: 390.3600 current episode reward: 220.0000 episodes: 701 exploration: 0.10900 learning_rate: 0.00010 elapsed time: 3691.4 seconds (1.03 hours) Timestep: 1000000 mean reward (100 episodes): 350.3600 best mean reward: 390.3600 current episode reward: 440.0000 episodes: 707 exploration: 0.10000 learning_rate: 0.00010 elapsed time: 3736.3 seconds (1.04 hours) Timestep: 1010000 mean reward (100 episodes): 341.1200 best mean reward: 390.3600 current episode reward: 264.0000 episodes: 715 exploration: 0.09978 learning_rate: 0.00010 elapsed time: 3781.3 seconds (1.05 hours) Timestep: 1020000 mean reward (100 episodes): 341.1200 best mean reward: 390.3600 current episode reward: 440.0000 episodes: 722 exploration: 0.09955 learning_rate: 0.00010 elapsed time: 3826.6 seconds (1.06 hours) Timestep: 1030000 mean reward (100 episodes): 340.2400 best mean reward: 390.3600 current episode reward: 616.0000 episodes: 729 exploration: 0.09933 learning_rate: 0.00010 elapsed time: 3871.6 seconds (1.08 hours) Timestep: 1040000 mean reward (100 episodes): 352.2000 best mean reward: 390.3600 current episode reward: 220.0000 episodes: 735 exploration: 0.09910 learning_rate: 0.00010 elapsed time: 3917.2 seconds (1.09 hours) Timestep: 1050000 mean reward (100 episodes): 355.7200 best mean reward: 390.3600 current episode reward: 528.0000 episodes: 741 exploration: 0.09888 learning_rate: 0.00010 elapsed time: 3961.9 seconds (1.10 hours) Timestep: 1060000 mean reward (100 episodes): 357.9200 best mean reward: 390.3600 current episode reward: 44.0000 episodes: 748 exploration: 0.09865 learning_rate: 0.00010 elapsed time: 4007.0 seconds (1.11 hours) Timestep: 1070000 mean reward (100 episodes): 338.8800 best mean reward: 390.3600 current episode reward: 176.0000 episodes: 755 exploration: 0.09842 learning_rate: 0.00010 elapsed time: 4052.4 seconds (1.13 hours) Timestep: 1080000 mean reward (100 episodes): 335.3600 best mean reward: 390.3600 current episode reward: 572.0000 episodes: 763 exploration: 0.09820 learning_rate: 0.00010 elapsed time: 4097.0 seconds (1.14 hours) Timestep: 1090000 mean reward (100 episodes): 334.9200 best mean reward: 390.3600 current episode reward: 440.0000 episodes: 770 exploration: 0.09798 learning_rate: 0.00010 elapsed time: 4141.1 seconds (1.15 hours) Timestep: 1100000 mean reward (100 episodes): 330.9600 best mean reward: 390.3600 current episode reward: 352.0000 episodes: 775 exploration: 0.09775 learning_rate: 0.00010 elapsed time: 4185.2 seconds (1.16 hours) Timestep: 1110000 mean reward (100 episodes): 338.4400 best mean reward: 390.3600 current episode reward: 88.0000 episodes: 782 exploration: 0.09753 learning_rate: 0.00010 elapsed time: 4229.8 seconds (1.17 hours) Timestep: 1120000 mean reward (100 episodes): 327.4400 best mean reward: 390.3600 current episode reward: 352.0000 episodes: 789 exploration: 0.09730 learning_rate: 0.00010 elapsed time: 4274.7 seconds (1.19 hours) Timestep: 1130000 mean reward (100 episodes): 324.8000 best mean reward: 390.3600 current episode reward: 440.0000 episodes: 794 exploration: 0.09708 learning_rate: 0.00010 elapsed time: 4320.4 seconds (1.20 hours) Timestep: 1140000 mean reward (100 episodes): 330.9600 best mean reward: 390.3600 current episode reward: 308.0000 episodes: 800 exploration: 0.09685 learning_rate: 0.00010 elapsed time: 4364.9 seconds (1.21 hours) Timestep: 1150000 mean reward (100 episodes): 341.5200 best mean reward: 390.3600 current episode reward: 528.0000 episodes: 806 exploration: 0.09663 learning_rate: 0.00010 elapsed time: 4409.6 seconds (1.22 hours) Timestep: 1160000 mean reward (100 episodes): 342.8400 best mean reward: 390.3600 current episode reward: 352.0000 episodes: 813 exploration: 0.09640 learning_rate: 0.00010 elapsed time: 4454.8 seconds (1.24 hours) Timestep: 1170000 mean reward (100 episodes): 345.4800 best mean reward: 390.3600 current episode reward: 220.0000 episodes: 821 exploration: 0.09618 learning_rate: 0.00010 elapsed time: 4499.8 seconds (1.25 hours) Timestep: 1180000 mean reward (100 episodes): 350.7600 best mean reward: 390.3600 current episode reward: 176.0000 episodes: 828 exploration: 0.09595 learning_rate: 0.00010 elapsed time: 4544.8 seconds (1.26 hours) Timestep: 1190000 mean reward (100 episodes): 337.9200 best mean reward: 390.3600 current episode reward: 220.0000 episodes: 835 exploration: 0.09573 learning_rate: 0.00010 elapsed time: 4589.2 seconds (1.27 hours) Timestep: 1200000 mean reward (100 episodes): 335.2800 best mean reward: 390.3600 current episode reward: 484.0000 episodes: 843 exploration: 0.09550 learning_rate: 0.00010 elapsed time: 4634.0 seconds (1.29 hours) Timestep: 1210000 mean reward (100 episodes): 330.0000 best mean reward: 390.3600 current episode reward: 220.0000 episodes: 851 exploration: 0.09527 learning_rate: 0.00010 elapsed time: 4677.9 seconds (1.30 hours) Timestep: 1220000 mean reward (100 episodes): 331.3200 best mean reward: 390.3600 current episode reward: 440.0000 episodes: 857 exploration: 0.09505 learning_rate: 0.00010 elapsed time: 4723.0 seconds (1.31 hours) Timestep: 1230000 mean reward (100 episodes): 341.0000 best mean reward: 390.3600 current episode reward: 264.0000 episodes: 864 exploration: 0.09483 learning_rate: 0.00010 elapsed time: 4767.4 seconds (1.32 hours) Timestep: 1240000 mean reward (100 episodes): 341.8800 best mean reward: 390.3600 current episode reward: 660.0000 episodes: 871 exploration: 0.09460 learning_rate: 0.00010 elapsed time: 4812.3 seconds (1.34 hours) Timestep: 1250000 mean reward (100 episodes): 337.9200 best mean reward: 390.3600 current episode reward: 660.0000 episodes: 878 exploration: 0.09438 learning_rate: 0.00010 elapsed time: 4856.7 seconds (1.35 hours) Timestep: 1260000 mean reward (100 episodes): 340.5600 best mean reward: 390.3600 current episode reward: 308.0000 episodes: 885 exploration: 0.09415 learning_rate: 0.00010 elapsed time: 4901.8 seconds (1.36 hours) Timestep: 1270000 mean reward (100 episodes): 342.7600 best mean reward: 390.3600 current episode reward: 396.0000 episodes: 891 exploration: 0.09393 learning_rate: 0.00010 elapsed time: 4946.3 seconds (1.37 hours) Timestep: 1280000 mean reward (100 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elapsed time: 5170.0 seconds (1.44 hours) Timestep: 1330000 mean reward (100 episodes): 324.3200 best mean reward: 390.3600 current episode reward: 396.0000 episodes: 931 exploration: 0.09258 learning_rate: 0.00010 elapsed time: 5214.9 seconds (1.45 hours) Timestep: 1340000 mean reward (100 episodes): 332.6800 best mean reward: 390.3600 current episode reward: 484.0000 episodes: 937 exploration: 0.09235 learning_rate: 0.00010 elapsed time: 5260.1 seconds (1.46 hours) Timestep: 1350000 mean reward (100 episodes): 335.7600 best mean reward: 390.3600 current episode reward: 352.0000 episodes: 944 exploration: 0.09213 learning_rate: 0.00010 elapsed time: 5305.2 seconds (1.47 hours) Timestep: 1360000 mean reward (100 episodes): 341.9200 best mean reward: 390.3600 current episode reward: 440.0000 episodes: 951 exploration: 0.09190 learning_rate: 0.00010 elapsed time: 5350.0 seconds (1.49 hours) Timestep: 1370000 mean reward (100 episodes): 338.4000 best mean reward: 390.3600 current episode reward: 440.0000 episodes: 958 exploration: 0.09168 learning_rate: 0.00010 elapsed time: 5394.5 seconds (1.50 hours) Timestep: 1380000 mean reward (100 episodes): 332.6800 best mean reward: 390.3600 current episode reward: 132.0000 episodes: 965 exploration: 0.09145 learning_rate: 0.00010 elapsed time: 5439.1 seconds (1.51 hours) Timestep: 1390000 mean reward (100 episodes): 325.6400 best mean reward: 390.3600 current episode reward: 308.0000 episodes: 973 exploration: 0.09123 learning_rate: 0.00010 elapsed time: 5483.8 seconds (1.52 hours) Timestep: 1400000 mean reward (100 episodes): 326.0800 best mean reward: 390.3600 current episode reward: 396.0000 episodes: 980 exploration: 0.09100 learning_rate: 0.00010 elapsed time: 5528.3 seconds (1.54 hours) Timestep: 1410000 mean reward (100 episodes): 328.2800 best mean reward: 390.3600 current episode reward: 132.0000 episodes: 987 exploration: 0.09078 learning_rate: 0.00009 elapsed time: 5573.1 seconds (1.55 hours) Timestep: 1420000 mean 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learning_rate: 0.00009 elapsed time: 5795.9 seconds (1.61 hours) Timestep: 1470000 mean reward (100 episodes): 339.6800 best mean reward: 390.3600 current episode reward: 132.0000 episodes: 1025 exploration: 0.08943 learning_rate: 0.00009 elapsed time: 5840.4 seconds (1.62 hours) Timestep: 1480000 mean reward (100 episodes): 343.2000 best mean reward: 390.3600 current episode reward: 528.0000 episodes: 1031 exploration: 0.08920 learning_rate: 0.00009 elapsed time: 5885.3 seconds (1.63 hours) Timestep: 1490000 mean reward (100 episodes): 338.3600 best mean reward: 390.3600 current episode reward: 352.0000 episodes: 1038 exploration: 0.08897 learning_rate: 0.00009 elapsed time: 5929.6 seconds (1.65 hours) Timestep: 1500000 mean reward (100 episodes): 342.7600 best mean reward: 390.3600 current episode reward: 220.0000 episodes: 1043 exploration: 0.08875 learning_rate: 0.00009 elapsed time: 5974.0 seconds (1.66 hours) Timestep: 1510000 mean reward (100 episodes): 344.5200 best mean reward: 390.3600 current episode reward: 176.0000 episodes: 1049 exploration: 0.08853 learning_rate: 0.00009 elapsed time: 6017.9 seconds (1.67 hours) Timestep: 1520000 mean reward (100 episodes): 348.4800 best mean reward: 390.3600 current episode reward: 352.0000 episodes: 1056 exploration: 0.08830 learning_rate: 0.00009 elapsed time: 6062.2 seconds (1.68 hours) Timestep: 1530000 mean reward (100 episodes): 339.6800 best mean reward: 390.3600 current episode reward: 220.0000 episodes: 1063 exploration: 0.08808 learning_rate: 0.00009 elapsed time: 6106.7 seconds (1.70 hours) Timestep: 1540000 mean reward (100 episodes): 348.9200 best mean reward: 390.3600 current episode reward: 176.0000 episodes: 1070 exploration: 0.08785 learning_rate: 0.00009 elapsed time: 6150.6 seconds (1.71 hours) Timestep: 1550000 mean reward (100 episodes): 349.8000 best mean reward: 390.3600 current episode reward: 264.0000 episodes: 1077 exploration: 0.08763 learning_rate: 0.00009 elapsed time: 6195.3 seconds (1.72 hours) Timestep: 1560000 mean reward (100 episodes): 341.4400 best mean reward: 390.3600 current episode reward: 308.0000 episodes: 1083 exploration: 0.08740 learning_rate: 0.00009 elapsed time: 6240.3 seconds (1.73 hours) Timestep: 1570000 mean reward (100 episodes): 345.4000 best mean reward: 390.3600 current episode reward: 308.0000 episodes: 1090 exploration: 0.08718 learning_rate: 0.00009 elapsed time: 6284.5 seconds (1.75 hours) Timestep: 1580000 mean reward (100 episodes): 343.2000 best mean reward: 390.3600 current episode reward: 528.0000 episodes: 1097 exploration: 0.08695 learning_rate: 0.00009 elapsed time: 6329.4 seconds (1.76 hours) Timestep: 1590000 mean reward (100 episodes): 335.2800 best mean reward: 390.3600 current episode reward: 264.0000 episodes: 1103 exploration: 0.08673 learning_rate: 0.00009 elapsed time: 6374.3 seconds (1.77 hours) Timestep: 1600000 mean reward (100 episodes): 315.9200 best mean reward: 390.3600 current episode reward: 264.0000 episodes: 1111 exploration: 0.08650 learning_rate: 0.00009 elapsed time: 6419.0 seconds (1.78 hours) Timestep: 1610000 mean reward (100 episodes): 319.0000 best mean reward: 390.3600 current episode reward: 440.0000 episodes: 1118 exploration: 0.08628 learning_rate: 0.00009 elapsed time: 6463.6 seconds (1.80 hours) Timestep: 1620000 mean reward (100 episodes): 322.5200 best mean reward: 390.3600 current episode reward: 396.0000 episodes: 1124 exploration: 0.08605 learning_rate: 0.00009 elapsed time: 6508.5 seconds (1.81 hours) Timestep: 1630000 mean reward (100 episodes): 325.6000 best mean reward: 390.3600 current episode reward: 396.0000 episodes: 1130 exploration: 0.08582 learning_rate: 0.00009 elapsed time: 6553.6 seconds (1.82 hours) Timestep: 1640000 mean reward (100 episodes): 321.6400 best mean reward: 390.3600 current episode reward: 220.0000 episodes: 1136 exploration: 0.08560 learning_rate: 0.00009 elapsed time: 6598.8 seconds (1.83 hours) Timestep: 1650000 mean reward (100 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0.00009 elapsed time: 6823.1 seconds (1.90 hours) Timestep: 1700000 mean reward (100 episodes): 312.8400 best mean reward: 390.3600 current episode reward: 352.0000 episodes: 1177 exploration: 0.08425 learning_rate: 0.00009 elapsed time: 6868.1 seconds (1.91 hours) Timestep: 1710000 mean reward (100 episodes): 311.5200 best mean reward: 390.3600 current episode reward: 220.0000 episodes: 1184 exploration: 0.08403 learning_rate: 0.00009 elapsed time: 6912.6 seconds (1.92 hours) Timestep: 1720000 mean reward (100 episodes): 315.4800 best mean reward: 390.3600 current episode reward: 176.0000 episodes: 1191 exploration: 0.08380 learning_rate: 0.00009 elapsed time: 6958.0 seconds (1.93 hours) Timestep: 1730000 mean reward (100 episodes): 311.0800 best mean reward: 390.3600 current episode reward: 220.0000 episodes: 1199 exploration: 0.08358 learning_rate: 0.00009 elapsed time: 7002.1 seconds (1.95 hours) Timestep: 1740000 mean reward (100 episodes): 314.6000 best mean reward: 390.3600 current episode reward: 616.0000 episodes: 1206 exploration: 0.08335 learning_rate: 0.00009 elapsed time: 7047.0 seconds (1.96 hours) Timestep: 1750000 mean reward (100 episodes): 306.2400 best mean reward: 390.3600 current episode reward: 440.0000 episodes: 1214 exploration: 0.08313 learning_rate: 0.00009 elapsed time: 7091.4 seconds (1.97 hours) Timestep: 1760000 mean reward (100 episodes): 304.0400 best mean reward: 390.3600 current episode reward: 484.0000 episodes: 1221 exploration: 0.08290 learning_rate: 0.00009 elapsed time: 7135.9 seconds (1.98 hours) Timestep: 1770000 mean reward (100 episodes): 300.0800 best mean reward: 390.3600 current episode reward: 220.0000 episodes: 1227 exploration: 0.08267 learning_rate: 0.00009 elapsed time: 7180.4 seconds (1.99 hours) Timestep: 1780000 mean reward (100 episodes): 303.6000 best mean reward: 390.3600 current episode reward: 88.0000 episodes: 1235 exploration: 0.08245 learning_rate: 0.00009 elapsed time: 7225.4 seconds (2.01 hours) Timestep: 1790000 mean reward (100 episodes): 301.8400 best mean reward: 390.3600 current episode reward: 132.0000 episodes: 1242 exploration: 0.08223 learning_rate: 0.00009 elapsed time: 7269.3 seconds (2.02 hours) Timestep: 1800000 mean reward (100 episodes): 308.8800 best mean reward: 390.3600 current episode reward: 572.0000 episodes: 1248 exploration: 0.08200 learning_rate: 0.00009 elapsed time: 7314.0 seconds (2.03 hours) Timestep: 1810000 mean reward (100 episodes): 299.6400 best mean reward: 390.3600 current episode reward: 220.0000 episodes: 1256 exploration: 0.08178 learning_rate: 0.00009 elapsed time: 7358.4 seconds (2.04 hours) Timestep: 1820000 mean reward (100 episodes): 293.9200 best mean reward: 390.3600 current episode reward: 176.0000 episodes: 1264 exploration: 0.08155 learning_rate: 0.00009 elapsed time: 7403.6 seconds (2.06 hours) Timestep: 1830000 mean reward (100 episodes): 292.6000 best mean reward: 390.3600 current episode reward: 572.0000 episodes: 1270 exploration: 0.08133 learning_rate: 0.00009 elapsed time: 7448.9 seconds (2.07 hours) Timestep: 1840000 mean reward (100 episodes): 287.7600 best mean reward: 390.3600 current episode reward: 484.0000 episodes: 1277 exploration: 0.08110 learning_rate: 0.00009 elapsed time: 7493.4 seconds (2.08 hours) Timestep: 1850000 mean reward (100 episodes): 288.6400 best mean reward: 390.3600 current episode reward: 396.0000 episodes: 1284 exploration: 0.08088 learning_rate: 0.00009 elapsed time: 7538.1 seconds (2.09 hours) Timestep: 1860000 mean reward (100 episodes): 284.2400 best mean reward: 390.3600 current episode reward: 88.0000 episodes: 1291 exploration: 0.08065 learning_rate: 0.00009 elapsed time: 7581.6 seconds (2.11 hours) Timestep: 1870000 mean reward (100 episodes): 289.5200 best mean reward: 390.3600 current episode reward: 308.0000 episodes: 1298 exploration: 0.08042 learning_rate: 0.00009 elapsed time: 7626.3 seconds (2.12 hours) Timestep: 1880000 mean reward (100 episodes): 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reward: 528.0000 episodes: 1365 exploration: 0.07818 learning_rate: 0.00009 elapsed time: 8073.9 seconds (2.24 hours) Timestep: 1980000 mean reward (100 episodes): 310.6400 best mean reward: 390.3600 current episode reward: 396.0000 episodes: 1373 exploration: 0.07795 learning_rate: 0.00009 elapsed time: 8118.4 seconds (2.26 hours) Timestep: 1990000 mean reward (100 episodes): 305.8000 best mean reward: 390.3600 current episode reward: 176.0000 episodes: 1380 exploration: 0.07773 learning_rate: 0.00009 elapsed time: 8163.6 seconds (2.27 hours) Timestep: 2000000 mean reward (100 episodes): 298.3200 best mean reward: 390.3600 current episode reward: 308.0000 episodes: 1388 exploration: 0.07750 learning_rate: 0.00009 elapsed time: 8207.6 seconds (2.28 hours) Timestep: 2010000 mean reward (100 episodes): 295.2400 best mean reward: 390.3600 current episode reward: 308.0000 episodes: 1395 exploration: 0.07728 learning_rate: 0.00009 elapsed time: 8252.0 seconds (2.29 hours) Timestep: 2020000 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learning_rate: 0.00009 elapsed time: 8474.2 seconds (2.35 hours) Timestep: 2070000 mean reward (100 episodes): 290.8400 best mean reward: 390.3600 current episode reward: 220.0000 episodes: 1438 exploration: 0.07593 learning_rate: 0.00009 elapsed time: 8518.8 seconds (2.37 hours) Timestep: 2080000 mean reward (100 episodes): 290.4000 best mean reward: 390.3600 current episode reward: 308.0000 episodes: 1444 exploration: 0.07570 learning_rate: 0.00009 elapsed time: 8563.0 seconds (2.38 hours) Timestep: 2090000 mean reward (100 episodes): 291.2800 best mean reward: 390.3600 current episode reward: 220.0000 episodes: 1451 exploration: 0.07548 learning_rate: 0.00009 elapsed time: 8607.4 seconds (2.39 hours) Timestep: 2100000 mean reward (100 episodes): 281.6000 best mean reward: 390.3600 current episode reward: 132.0000 episodes: 1458 exploration: 0.07525 learning_rate: 0.00009 elapsed time: 8651.2 seconds (2.40 hours) Timestep: 2110000 mean reward (100 episodes): 271.9200 best mean reward: 390.3600 current episode reward: 352.0000 episodes: 1466 exploration: 0.07503 learning_rate: 0.00009 elapsed time: 8696.0 seconds (2.42 hours) Timestep: 2120000 mean reward (100 episodes): 276.3200 best mean reward: 390.3600 current episode reward: 528.0000 episodes: 1473 exploration: 0.07480 learning_rate: 0.00009 elapsed time: 8740.3 seconds (2.43 hours) Timestep: 2130000 mean reward (100 episodes): 289.5200 best mean reward: 390.3600 current episode reward: 484.0000 episodes: 1479 exploration: 0.07458 learning_rate: 0.00009 elapsed time: 8784.9 seconds (2.44 hours) Timestep: 2140000 mean reward (100 episodes): 286.0000 best mean reward: 390.3600 current episode reward: 88.0000 episodes: 1487 exploration: 0.07435 learning_rate: 0.00009 elapsed time: 8829.0 seconds (2.45 hours) Timestep: 2150000 mean reward (100 episodes): 288.6400 best mean reward: 390.3600 current episode reward: 176.0000 episodes: 1494 exploration: 0.07412 learning_rate: 0.00009 elapsed time: 8873.0 seconds (2.46 hours) Timestep: 2160000 mean reward (100 episodes): 289.9600 best mean reward: 390.3600 current episode reward: 176.0000 episodes: 1501 exploration: 0.07390 learning_rate: 0.00009 elapsed time: 8917.8 seconds (2.48 hours) Timestep: 2170000 mean reward (100 episodes): 290.4000 best mean reward: 390.3600 current episode reward: 308.0000 episodes: 1508 exploration: 0.07368 learning_rate: 0.00009 elapsed time: 8963.2 seconds (2.49 hours) Timestep: 2180000 mean reward (100 episodes): 285.5600 best mean reward: 390.3600 current episode reward: 88.0000 episodes: 1515 exploration: 0.07345 learning_rate: 0.00009 elapsed time: 9006.8 seconds (2.50 hours) Timestep: 2190000 mean reward (100 episodes): 285.5600 best mean reward: 390.3600 current episode reward: 264.0000 episodes: 1522 exploration: 0.07322 learning_rate: 0.00009 elapsed time: 9052.4 seconds (2.51 hours) Timestep: 2200000 mean reward (100 episodes): 272.8000 best mean reward: 390.3600 current episode reward: 88.0000 episodes: 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reward (100 episodes): 270.6000 best mean reward: 390.3600 current episode reward: 264.0000 episodes: 1667 exploration: 0.06873 learning_rate: 0.00008 elapsed time: 9943.8 seconds (2.76 hours) Timestep: 2400000 mean reward (100 episodes): 272.8000 best mean reward: 390.3600 current episode reward: 484.0000 episodes: 1674 exploration: 0.06850 learning_rate: 0.00008 elapsed time: 9987.8 seconds (2.77 hours) Timestep: 2410000 mean reward (100 episodes): 274.1200 best mean reward: 390.3600 current episode reward: 264.0000 episodes: 1681 exploration: 0.06828 learning_rate: 0.00008 elapsed time: 10032.5 seconds (2.79 hours) Timestep: 2420000 mean reward (100 episodes): 270.6000 best mean reward: 390.3600 current episode reward: 88.0000 episodes: 1688 exploration: 0.06805 learning_rate: 0.00008 elapsed time: 10077.1 seconds (2.80 hours) Timestep: 2430000 mean reward (100 episodes): 269.2800 best mean reward: 390.3600 current episode reward: 132.0000 episodes: 1695 exploration: 0.06782 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seconds (2.92 hours) Timestep: 2530000 mean reward (100 episodes): 309.7600 best mean reward: 390.3600 current episode reward: 616.0000 episodes: 1764 exploration: 0.06557 learning_rate: 0.00008 elapsed time: 10566.9 seconds (2.94 hours) Timestep: 2540000 mean reward (100 episodes): 314.1600 best mean reward: 390.3600 current episode reward: 396.0000 episodes: 1770 exploration: 0.06535 learning_rate: 0.00008 elapsed time: 10611.2 seconds (2.95 hours) Timestep: 2550000 mean reward (100 episodes): 323.4000 best mean reward: 390.3600 current episode reward: 528.0000 episodes: 1777 exploration: 0.06513 learning_rate: 0.00008 elapsed time: 10655.8 seconds (2.96 hours) Timestep: 2560000 mean reward (100 episodes): 334.1200 best mean reward: 390.3600 current episode reward: 352.0000 episodes: 1782 exploration: 0.06490 learning_rate: 0.00008 elapsed time: 10700.2 seconds (2.97 hours) Timestep: 2570000 mean reward (100 episodes): 341.6000 best mean reward: 390.3600 current episode reward: 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mean reward (100 episodes): 355.6800 best mean reward: 390.3600 current episode reward: 264.0000 episodes: 1821 exploration: 0.06355 learning_rate: 0.00008 elapsed time: 10969.1 seconds (3.05 hours) Timestep: 2630000 mean reward (100 episodes): 369.3200 best mean reward: 390.3600 current episode reward: 616.0000 episodes: 1828 exploration: 0.06333 learning_rate: 0.00008 elapsed time: 11014.1 seconds (3.06 hours) Timestep: 2640000 mean reward (100 episodes): 371.5600 best mean reward: 390.3600 current episode reward: 440.0000 episodes: 1834 exploration: 0.06310 learning_rate: 0.00008 elapsed time: 11058.6 seconds (3.07 hours) Timestep: 2650000 mean reward (100 episodes): 387.6800 best mean reward: 390.3600 current episode reward: 572.0000 episodes: 1839 exploration: 0.06288 learning_rate: 0.00008 elapsed time: 11103.3 seconds (3.08 hours) Timestep: 2660000 mean reward (100 episodes): 388.6000 best mean reward: 390.3600 current episode reward: 132.0000 episodes: 1845 exploration: 0.06265 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seconds (3.21 hours) Timestep: 2760000 mean reward (100 episodes): 413.4400 best mean reward: 414.3200 current episode reward: 308.0000 episodes: 1907 exploration: 0.06040 learning_rate: 0.00008 elapsed time: 11595.8 seconds (3.22 hours) Timestep: 2770000 mean reward (100 episodes): 414.3200 best mean reward: 416.9600 current episode reward: 220.0000 episodes: 1913 exploration: 0.06017 learning_rate: 0.00008 elapsed time: 11640.3 seconds (3.23 hours) Timestep: 2780000 mean reward (100 episodes): 420.9200 best mean reward: 421.8000 current episode reward: 264.0000 episodes: 1920 exploration: 0.05995 learning_rate: 0.00008 elapsed time: 11685.0 seconds (3.25 hours) Timestep: 2790000 mean reward (100 episodes): 427.0800 best mean reward: 429.7200 current episode reward: 396.0000 episodes: 1926 exploration: 0.05973 learning_rate: 0.00008 elapsed time: 11729.2 seconds (3.26 hours) Timestep: 2800000 mean reward (100 episodes): 423.5200 best mean reward: 429.7200 current episode reward: 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seconds (3.50 hours) Timestep: 2990000 mean reward (100 episodes): 364.9200 best mean reward: 429.7200 current episode reward: 572.0000 episodes: 2049 exploration: 0.05523 learning_rate: 0.00008 elapsed time: 12627.6 seconds (3.51 hours) Timestep: 3000000 mean reward (100 episodes): 357.3600 best mean reward: 429.7200 current episode reward: 264.0000 episodes: 2054 exploration: 0.05500 learning_rate: 0.00008 elapsed time: 12672.7 seconds (3.52 hours) Timestep: 3010000 mean reward (100 episodes): 375.4000 best mean reward: 429.7200 current episode reward: 440.0000 episodes: 2061 exploration: 0.05478 learning_rate: 0.00007 elapsed time: 12717.5 seconds (3.53 hours) Timestep: 3020000 mean reward (100 episodes): 385.9600 best mean reward: 429.7200 current episode reward: 264.0000 episodes: 2068 exploration: 0.05455 learning_rate: 0.00007 elapsed time: 12762.7 seconds (3.55 hours) Timestep: 3030000 mean reward (100 episodes): 400.0400 best mean reward: 429.7200 current episode reward: 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reward: 495.8400 current episode reward: 708.0000 episodes: 2151 exploration: 0.05117 learning_rate: 0.00007 elapsed time: 13438.9 seconds (3.73 hours) Timestep: 3180000 mean reward (100 episodes): 494.9200 best mean reward: 499.3200 current episode reward: 308.0000 episodes: 2158 exploration: 0.05095 learning_rate: 0.00007 elapsed time: 13483.0 seconds (3.75 hours) Timestep: 3190000 mean reward (100 episodes): 498.8800 best mean reward: 501.0800 current episode reward: 308.0000 episodes: 2164 exploration: 0.05072 learning_rate: 0.00007 elapsed time: 13527.5 seconds (3.76 hours) Timestep: 3200000 mean reward (100 episodes): 498.0000 best mean reward: 501.0800 current episode reward: 484.0000 episodes: 2170 exploration: 0.05050 learning_rate: 0.00007 elapsed time: 13572.2 seconds (3.77 hours) Timestep: 3210000 mean reward (100 episodes): 498.8800 best mean reward: 501.0800 current episode reward: 616.0000 episodes: 2175 exploration: 0.05028 learning_rate: 0.00007 elapsed time: 13617.1 seconds (3.78 hours) Timestep: 3220000 mean reward (100 episodes): 506.8000 best mean reward: 508.5600 current episode reward: 440.0000 episodes: 2181 exploration: 0.05005 learning_rate: 0.00007 elapsed time: 13662.6 seconds (3.80 hours) Timestep: 3230000 mean reward (100 episodes): 485.6800 best mean reward: 509.4400 current episode reward: 176.0000 episodes: 2188 exploration: 0.04983 learning_rate: 0.00007 elapsed time: 13707.2 seconds (3.81 hours) Timestep: 3240000 mean reward (100 episodes): 479.0800 best mean reward: 509.4400 current episode reward: 352.0000 episodes: 2195 exploration: 0.04960 learning_rate: 0.00007 elapsed time: 13752.2 seconds (3.82 hours) Timestep: 3250000 mean reward (100 episodes): 476.4400 best mean reward: 509.4400 current episode reward: 264.0000 episodes: 2202 exploration: 0.04938 learning_rate: 0.00007 elapsed time: 13798.1 seconds (3.83 hours) Timestep: 3260000 mean reward (100 episodes): 471.6000 best mean reward: 509.4400 current episode reward: 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mean reward (100 episodes): 426.0800 best mean reward: 509.4400 current episode reward: 352.0000 episodes: 2239 exploration: 0.04802 learning_rate: 0.00007 elapsed time: 14068.3 seconds (3.91 hours) Timestep: 3320000 mean reward (100 episodes): 421.1600 best mean reward: 509.4400 current episode reward: 264.0000 episodes: 2245 exploration: 0.04780 learning_rate: 0.00007 elapsed time: 14113.3 seconds (3.92 hours) Timestep: 3330000 mean reward (100 episodes): 420.7600 best mean reward: 509.4400 current episode reward: 440.0000 episodes: 2250 exploration: 0.04757 learning_rate: 0.00007 elapsed time: 14158.2 seconds (3.93 hours) Timestep: 3340000 mean reward (100 episodes): 425.1200 best mean reward: 509.4400 current episode reward: 572.0000 episodes: 2257 exploration: 0.04735 learning_rate: 0.00007 elapsed time: 14202.2 seconds (3.95 hours) Timestep: 3350000 mean reward (100 episodes): 433.9600 best mean reward: 509.4400 current episode reward: 528.0000 episodes: 2262 exploration: 0.04713 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reward: 509.4400 current episode reward: 176.0000 episodes: 2289 exploration: 0.04600 learning_rate: 0.00007 elapsed time: 14470.2 seconds (4.02 hours) Timestep: 3410000 mean reward (100 episodes): 462.3600 best mean reward: 509.4400 current episode reward: 528.0000 episodes: 2295 exploration: 0.04577 learning_rate: 0.00007 elapsed time: 14514.9 seconds (4.03 hours) Timestep: 3420000 mean reward (100 episodes): 473.3600 best mean reward: 509.4400 current episode reward: 308.0000 episodes: 2300 exploration: 0.04555 learning_rate: 0.00007 elapsed time: 14559.9 seconds (4.04 hours) Timestep: 3430000 mean reward (100 episodes): 478.3200 best mean reward: 509.4400 current episode reward: 264.0000 episodes: 2306 exploration: 0.04532 learning_rate: 0.00007 elapsed time: 14604.8 seconds (4.06 hours) Timestep: 3440000 mean reward (100 episodes): 484.0400 best mean reward: 509.4400 current episode reward: 264.0000 episodes: 2312 exploration: 0.04510 learning_rate: 0.00007 elapsed time: 14649.6 seconds (4.07 hours) Timestep: 3450000 mean reward (100 episodes): 493.4000 best mean reward: 509.4400 current episode reward: 660.0000 episodes: 2319 exploration: 0.04487 learning_rate: 0.00007 elapsed time: 14694.5 seconds (4.08 hours) Timestep: 3460000 mean reward (100 episodes): 507.4800 best mean reward: 509.4400 current episode reward: 660.0000 episodes: 2324 exploration: 0.04465 learning_rate: 0.00007 elapsed time: 14739.4 seconds (4.09 hours) Timestep: 3470000 mean reward (100 episodes): 508.8800 best mean reward: 511.9600 current episode reward: 264.0000 episodes: 2330 exploration: 0.04442 learning_rate: 0.00007 elapsed time: 14785.0 seconds (4.11 hours) Timestep: 3480000 mean reward (100 episodes): 506.8400 best mean reward: 511.9600 current episode reward: 852.0000 episodes: 2335 exploration: 0.04420 learning_rate: 0.00007 elapsed time: 14830.7 seconds (4.12 hours) Timestep: 3490000 mean reward (100 episodes): 527.6000 best mean reward: 527.6000 current episode reward: 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mean reward (100 episodes): 558.5600 best mean reward: 558.5600 current episode reward: 616.0000 episodes: 2365 exploration: 0.04285 learning_rate: 0.00007 elapsed time: 15102.6 seconds (4.20 hours) Timestep: 3550000 mean reward (100 episodes): 564.1200 best mean reward: 566.0400 current episode reward: 804.0000 episodes: 2371 exploration: 0.04263 learning_rate: 0.00007 elapsed time: 15148.3 seconds (4.21 hours) Timestep: 3560000 mean reward (100 episodes): 566.7600 best mean reward: 567.2000 current episode reward: 756.0000 episodes: 2377 exploration: 0.04240 learning_rate: 0.00007 elapsed time: 15193.6 seconds (4.22 hours) Timestep: 3570000 mean reward (100 episodes): 564.4400 best mean reward: 567.2000 current episode reward: 616.0000 episodes: 2382 exploration: 0.04218 learning_rate: 0.00007 elapsed time: 15238.5 seconds (4.23 hours) Timestep: 3580000 mean reward (100 episodes): 568.3200 best mean reward: 568.3200 current episode reward: 756.0000 episodes: 2386 exploration: 0.04195 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reward: 598.5200 current episode reward: 660.0000 episodes: 2416 exploration: 0.04083 learning_rate: 0.00007 elapsed time: 15508.3 seconds (4.31 hours) Timestep: 3640000 mean reward (100 episodes): 600.4000 best mean reward: 600.8400 current episode reward: 616.0000 episodes: 2421 exploration: 0.04060 learning_rate: 0.00007 elapsed time: 15553.3 seconds (4.32 hours) Timestep: 3650000 mean reward (100 episodes): 608.9600 best mean reward: 608.9600 current episode reward: 616.0000 episodes: 2427 exploration: 0.04038 learning_rate: 0.00007 elapsed time: 15598.1 seconds (4.33 hours) Timestep: 3660000 mean reward (100 episodes): 614.6800 best mean reward: 614.6800 current episode reward: 572.0000 episodes: 2432 exploration: 0.04015 learning_rate: 0.00007 elapsed time: 15644.9 seconds (4.35 hours) Timestep: 3670000 mean reward (100 episodes): 618.4800 best mean reward: 621.7200 current episode reward: 1284.0000 episodes: 2437 exploration: 0.03993 learning_rate: 0.00007 elapsed time: 15690.0 seconds (4.36 hours) Timestep: 3680000 mean reward (100 episodes): 615.8400 best mean reward: 621.7200 current episode reward: 396.0000 episodes: 2442 exploration: 0.03970 learning_rate: 0.00007 elapsed time: 15734.8 seconds (4.37 hours) Timestep: 3690000 mean reward (100 episodes): 618.3200 best mean reward: 621.7200 current episode reward: 528.0000 episodes: 2448 exploration: 0.03947 learning_rate: 0.00007 elapsed time: 15780.6 seconds (4.38 hours) Timestep: 3700000 mean reward (100 episodes): 620.4000 best mean reward: 621.7200 current episode reward: 1140.0000 episodes: 2454 exploration: 0.03925 learning_rate: 0.00007 elapsed time: 15825.6 seconds (4.40 hours) Timestep: 3710000 mean reward (100 episodes): 622.8400 best mean reward: 623.9200 current episode reward: 948.0000 episodes: 2461 exploration: 0.03902 learning_rate: 0.00007 elapsed time: 15871.0 seconds (4.41 hours) Timestep: 3720000 mean reward (100 episodes): 619.0400 best mean reward: 625.6800 current episode reward: 528.0000 episodes: 2467 exploration: 0.03880 learning_rate: 0.00007 elapsed time: 15916.2 seconds (4.42 hours) Timestep: 3730000 mean reward (100 episodes): 615.4400 best mean reward: 625.6800 current episode reward: 484.0000 episodes: 2474 exploration: 0.03857 learning_rate: 0.00007 elapsed time: 15962.2 seconds (4.43 hours) Timestep: 3740000 mean reward (100 episodes): 622.8800 best mean reward: 625.6800 current episode reward: 708.0000 episodes: 2480 exploration: 0.03835 learning_rate: 0.00007 elapsed time: 16007.9 seconds (4.45 hours) Timestep: 3750000 mean reward (100 episodes): 617.2800 best mean reward: 625.6800 current episode reward: 660.0000 episodes: 2487 exploration: 0.03812 learning_rate: 0.00007 elapsed time: 16052.5 seconds (4.46 hours) Timestep: 3760000 mean reward (100 episodes): 623.9600 best mean reward: 625.6800 current episode reward: 528.0000 episodes: 2491 exploration: 0.03790 learning_rate: 0.00007 elapsed time: 16097.5 seconds (4.47 hours) Timestep: 3770000 mean reward (100 episodes): 630.5200 best mean reward: 632.2000 current episode reward: 756.0000 episodes: 2498 exploration: 0.03768 learning_rate: 0.00007 elapsed time: 16142.1 seconds (4.48 hours) Timestep: 3780000 mean reward (100 episodes): 632.3600 best mean reward: 634.7200 current episode reward: 616.0000 episodes: 2504 exploration: 0.03745 learning_rate: 0.00007 elapsed time: 16186.7 seconds (4.50 hours) Timestep: 3790000 mean reward (100 episodes): 649.2400 best mean reward: 649.2400 current episode reward: 660.0000 episodes: 2509 exploration: 0.03722 learning_rate: 0.00007 elapsed time: 16232.2 seconds (4.51 hours) Timestep: 3800000 mean reward (100 episodes): 653.7200 best mean reward: 653.7200 current episode reward: 572.0000 episodes: 2515 exploration: 0.03700 learning_rate: 0.00007 elapsed time: 16277.4 seconds (4.52 hours) Timestep: 3810000 mean reward (100 episodes): 666.2400 best mean reward: 666.2400 current episode reward: 616.0000 episodes: 2521 exploration: 0.03678 learning_rate: 0.00006 elapsed time: 16323.0 seconds (4.53 hours) Timestep: 3820000 mean reward (100 episodes): 660.0400 best mean reward: 666.2400 current episode reward: 708.0000 episodes: 2527 exploration: 0.03655 learning_rate: 0.00006 elapsed time: 16368.0 seconds (4.55 hours) Timestep: 3830000 mean reward (100 episodes): 668.1200 best mean reward: 668.1200 current episode reward: 1092.0000 episodes: 2532 exploration: 0.03632 learning_rate: 0.00006 elapsed time: 16413.0 seconds (4.56 hours) Timestep: 3840000 mean reward (100 episodes): 666.0800 best mean reward: 671.8400 current episode reward: 708.0000 episodes: 2537 exploration: 0.03610 learning_rate: 0.00006 elapsed time: 16458.6 seconds (4.57 hours) Timestep: 3850000 mean reward (100 episodes): 673.8400 best mean reward: 673.8400 current episode reward: 756.0000 episodes: 2543 exploration: 0.03587 learning_rate: 0.00006 elapsed time: 16503.6 seconds (4.58 hours) Timestep: 3860000 mean reward (100 episodes): 681.8800 best mean reward: 682.0000 current episode reward: 948.0000 episodes: 2548 exploration: 0.03565 learning_rate: 0.00006 elapsed time: 16548.8 seconds (4.60 hours) Timestep: 3870000 mean reward (100 episodes): 689.0000 best mean reward: 690.9200 current episode reward: 948.0000 episodes: 2554 exploration: 0.03542 learning_rate: 0.00006 elapsed time: 16593.6 seconds (4.61 hours) Timestep: 3880000 mean reward (100 episodes): 699.0000 best mean reward: 699.0000 current episode reward: 852.0000 episodes: 2560 exploration: 0.03520 learning_rate: 0.00006 elapsed time: 16638.1 seconds (4.62 hours) Timestep: 3890000 mean reward (100 episodes): 699.3200 best mean reward: 699.3200 current episode reward: 1432.0000 episodes: 2566 exploration: 0.03497 learning_rate: 0.00006 elapsed time: 16683.2 seconds (4.63 hours) Timestep: 3900000 mean reward (100 episodes): 703.6400 best mean reward: 706.3200 current episode reward: 660.0000 episodes: 2572 exploration: 0.03475 learning_rate: 0.00006 elapsed time: 16727.5 seconds (4.65 hours) Timestep: 3910000 mean reward (100 episodes): 712.1600 best mean reward: 712.1600 current episode reward: 804.0000 episodes: 2578 exploration: 0.03453 learning_rate: 0.00006 elapsed time: 16772.6 seconds (4.66 hours) Timestep: 3920000 mean reward (100 episodes): 714.6000 best mean reward: 715.5200 current episode reward: 708.0000 episodes: 2584 exploration: 0.03430 learning_rate: 0.00006 elapsed time: 16817.3 seconds (4.67 hours) Timestep: 3930000 mean reward (100 episodes): 705.4000 best mean reward: 717.2400 current episode reward: 616.0000 episodes: 2590 exploration: 0.03407 learning_rate: 0.00006 elapsed time: 16862.2 seconds (4.68 hours) Timestep: 3940000 mean reward (100 episodes): 717.1200 best mean reward: 717.7600 current episode reward: 660.0000 episodes: 2596 exploration: 0.03385 learning_rate: 0.00006 elapsed time: 16906.8 seconds (4.70 hours) Timestep: 3950000 mean reward (100 episodes): 725.8000 best mean reward: 726.2400 current episode reward: 616.0000 episodes: 2602 exploration: 0.03362 learning_rate: 0.00006 elapsed time: 16951.5 seconds (4.71 hours) Timestep: 3960000 mean reward (100 episodes): 720.1600 best mean reward: 726.2400 current episode reward: 660.0000 episodes: 2607 exploration: 0.03340 learning_rate: 0.00006 elapsed time: 16997.1 seconds (4.72 hours) Timestep: 3970000 mean reward (100 episodes): 724.1200 best mean reward: 726.2400 current episode reward: 1044.0000 episodes: 2614 exploration: 0.03317 learning_rate: 0.00006 elapsed time: 17042.5 seconds (4.73 hours) Timestep: 3980000 mean reward (100 episodes): 734.5600 best mean reward: 735.5200 current episode reward: 660.0000 episodes: 2619 exploration: 0.03295 learning_rate: 0.00006 elapsed time: 17087.7 seconds (4.75 hours) Timestep: 3990000 mean reward (100 episodes): 733.1200 best mean reward: 735.5200 current episode reward: 660.0000 episodes: 2625 exploration: 0.03272 learning_rate: 0.00006 elapsed time: 17132.1 seconds (4.76 hours) Timestep: 4000000 mean reward (100 episodes): 738.1600 best mean reward: 739.4800 current episode reward: 484.0000 episodes: 2630 exploration: 0.03250 learning_rate: 0.00006 elapsed time: 17177.4 seconds (4.77 hours) Timestep: 4010000 mean reward (100 episodes): 734.8000 best mean reward: 739.4800 current episode reward: 852.0000 episodes: 2635 exploration: 0.03227 learning_rate: 0.00006 elapsed time: 17222.4 seconds (4.78 hours) Timestep: 4020000 mean reward (100 episodes): 734.8000 best mean reward: 739.4800 current episode reward: 396.0000 episodes: 2642 exploration: 0.03205 learning_rate: 0.00006 elapsed time: 17267.8 seconds (4.80 hours) Timestep: 4030000 mean reward (100 episodes): 727.2800 best mean reward: 739.4800 current episode reward: 660.0000 episodes: 2647 exploration: 0.03183 learning_rate: 0.00006 elapsed time: 17312.5 seconds (4.81 hours) Timestep: 4040000 mean reward (100 episodes): 737.6400 best mean reward: 739.4800 current episode reward: 1140.0000 episodes: 2653 exploration: 0.03160 learning_rate: 0.00006 elapsed time: 17357.6 seconds (4.82 hours) Timestep: 4050000 mean reward (100 episodes): 736.8400 best mean reward: 739.4800 current episode reward: 948.0000 episodes: 2658 exploration: 0.03138 learning_rate: 0.00006 elapsed time: 17402.7 seconds (4.83 hours) Timestep: 4060000 mean reward (100 episodes): 750.2400 best mean reward: 750.2400 current episode reward: 900.0000 episodes: 2663 exploration: 0.03115 learning_rate: 0.00006 elapsed time: 17447.8 seconds (4.85 hours) Timestep: 4070000 mean reward (100 episodes): 744.9200 best mean reward: 754.5600 current episode reward: 660.0000 episodes: 2669 exploration: 0.03092 learning_rate: 0.00006 elapsed time: 17493.8 seconds (4.86 hours) Timestep: 4080000 mean reward (100 episodes): 751.3600 best mean reward: 754.5600 current episode reward: 804.0000 episodes: 2674 exploration: 0.03070 learning_rate: 0.00006 elapsed time: 17538.6 seconds (4.87 hours) Timestep: 4090000 mean reward (100 episodes): 783.6000 best mean reward: 783.6000 current episode reward: 1284.0000 episodes: 2678 exploration: 0.03048 learning_rate: 0.00006 elapsed time: 17584.2 seconds (4.88 hours) Timestep: 4100000 mean reward (100 episodes): 802.8000 best mean reward: 802.8000 current episode reward: 1692.0000 episodes: 2683 exploration: 0.03025 learning_rate: 0.00006 elapsed time: 17629.8 seconds (4.90 hours) Timestep: 4110000 mean reward (100 episodes): 822.3200 best mean reward: 822.3200 current episode reward: 948.0000 episodes: 2688 exploration: 0.03002 learning_rate: 0.00006 elapsed time: 17674.7 seconds (4.91 hours) Timestep: 4120000 mean reward (100 episodes): 839.4800 best mean reward: 839.4800 current episode reward: 2160.0000 episodes: 2692 exploration: 0.02980 learning_rate: 0.00006 elapsed time: 17719.4 seconds (4.92 hours) Timestep: 4130000 mean reward (100 episodes): 857.0000 best mean reward: 857.0000 current episode reward: 660.0000 episodes: 2696 exploration: 0.02958 learning_rate: 0.00006 elapsed time: 17764.7 seconds (4.93 hours) Timestep: 4140000 mean reward (100 episodes): 878.1200 best mean reward: 878.1200 current episode reward: 1140.0000 episodes: 2701 exploration: 0.02935 learning_rate: 0.00006 elapsed time: 17809.1 seconds (4.95 hours) Timestep: 4150000 mean reward (100 episodes): 903.0800 best mean reward: 903.0800 current episode reward: 1380.0000 episodes: 2705 exploration: 0.02912 learning_rate: 0.00006 elapsed time: 17854.8 seconds (4.96 hours) Timestep: 4160000 mean reward (100 episodes): 930.3200 best mean reward: 930.3200 current episode reward: 2004.0000 episodes: 2709 exploration: 0.02890 learning_rate: 0.00006 elapsed time: 17899.4 seconds (4.97 hours) Timestep: 4170000 mean reward (100 episodes): 970.5200 best mean reward: 970.5200 current episode reward: 1284.0000 episodes: 2713 exploration: 0.02867 learning_rate: 0.00006 elapsed time: 17944.4 seconds (4.98 hours) Timestep: 4180000 mean reward (100 episodes): 977.7200 best mean reward: 977.7200 current episode reward: 2004.0000 episodes: 2716 exploration: 0.02845 learning_rate: 0.00006 elapsed time: 17989.5 seconds (5.00 hours) Timestep: 4190000 mean reward (100 episodes): 992.1200 best mean reward: 992.1200 current episode reward: 708.0000 episodes: 2721 exploration: 0.02823 learning_rate: 0.00006 elapsed time: 18034.6 seconds (5.01 hours) Timestep: 4200000 mean reward (100 episodes): 1005.5600 best mean reward: 1005.5600 current episode reward: 1484.0000 episodes: 2725 exploration: 0.02800 learning_rate: 0.00006 elapsed time: 18079.8 seconds (5.02 hours) Timestep: 4210000 mean reward (100 episodes): 1035.3600 best mean reward: 1035.3600 current episode reward: 1332.0000 episodes: 2729 exploration: 0.02777 learning_rate: 0.00006 elapsed time: 18125.5 seconds (5.03 hours) Timestep: 4220000 mean reward (100 episodes): 1078.2000 best mean reward: 1079.1600 current episode reward: 708.0000 episodes: 2733 exploration: 0.02755 learning_rate: 0.00006 elapsed time: 18171.0 seconds (5.05 hours) Timestep: 4230000 mean reward (100 episodes): 1106.4000 best mean reward: 1106.4000 current episode reward: 1236.0000 episodes: 2736 exploration: 0.02733 learning_rate: 0.00006 elapsed time: 18216.2 seconds (5.06 hours) Timestep: 4240000 mean reward (100 episodes): 1136.8400 best mean reward: 1136.8400 current episode reward: 2004.0000 episodes: 2740 exploration: 0.02710 learning_rate: 0.00006 elapsed time: 18261.3 seconds (5.07 hours) Timestep: 4250000 mean reward (100 episodes): 1154.2400 best mean reward: 1154.2400 current episode reward: 660.0000 episodes: 2744 exploration: 0.02687 learning_rate: 0.00006 elapsed time: 18306.2 seconds (5.09 hours) Timestep: 4260000 mean reward (100 episodes): 1180.1200 best mean reward: 1185.4000 current episode reward: 660.0000 episodes: 2748 exploration: 0.02665 learning_rate: 0.00006 elapsed time: 18351.8 seconds (5.10 hours) Timestep: 4270000 mean reward (100 episodes): 1195.8400 best mean reward: 1195.8400 current episode reward: 1380.0000 episodes: 2752 exploration: 0.02642 learning_rate: 0.00006 elapsed time: 18397.9 seconds (5.11 hours) Timestep: 4280000 mean reward (100 episodes): 1219.4400 best mean reward: 1219.4400 current episode reward: 1432.0000 episodes: 2756 exploration: 0.02620 learning_rate: 0.00006 elapsed time: 18443.3 seconds (5.12 hours) Timestep: 4290000 mean reward (100 episodes): 1232.2000 best mean reward: 1234.1200 current episode reward: 804.0000 episodes: 2760 exploration: 0.02597 learning_rate: 0.00006 elapsed time: 18488.2 seconds (5.14 hours) Timestep: 4300000 mean reward (100 episodes): 1264.1200 best mean reward: 1264.1200 current episode reward: 1332.0000 episodes: 2763 exploration: 0.02575 learning_rate: 0.00006 elapsed time: 18533.7 seconds (5.15 hours) Timestep: 4310000 mean reward (100 episodes): 1295.1200 best mean reward: 1295.1200 current episode reward: 2636.0000 episodes: 2766 exploration: 0.02552 learning_rate: 0.00006 elapsed time: 18578.8 seconds (5.16 hours) Timestep: 4320000 mean reward (100 episodes): 1334.9200 best mean reward: 1334.9200 current episode reward: 1900.0000 episodes: 2770 exploration: 0.02530 learning_rate: 0.00006 elapsed time: 18623.8 seconds (5.17 hours) Timestep: 4330000 mean reward (100 episodes): 1359.3600 best mean reward: 1359.3600 current episode reward: 2384.0000 episodes: 2772 exploration: 0.02508 learning_rate: 0.00006 elapsed time: 18669.2 seconds (5.19 hours) Timestep: 4340000 mean reward (100 episodes): 1375.9600 best mean reward: 1375.9600 current episode reward: 2328.0000 episodes: 2776 exploration: 0.02485 learning_rate: 0.00006 elapsed time: 18714.9 seconds (5.20 hours) Timestep: 4350000 mean reward (100 episodes): 1398.8800 best mean reward: 1398.8800 current episode reward: 2004.0000 episodes: 2778 exploration: 0.02462 learning_rate: 0.00006 elapsed time: 18759.3 seconds (5.21 hours) Timestep: 4360000 mean reward (100 episodes): 1421.3600 best mean reward: 1421.3600 current episode reward: 1380.0000 episodes: 2782 exploration: 0.02440 learning_rate: 0.00006 elapsed time: 18805.2 seconds (5.22 hours) Timestep: 4370000 mean reward (100 episodes): 1449.2000 best mean reward: 1449.2000 current episode reward: 1140.0000 episodes: 2785 exploration: 0.02417 learning_rate: 0.00006 elapsed time: 18849.4 seconds (5.24 hours) Timestep: 4380000 mean reward (100 episodes): 1455.4400 best mean reward: 1455.4400 current episode reward: 948.0000 episodes: 2788 exploration: 0.02395 learning_rate: 0.00006 elapsed time: 18894.8 seconds (5.25 hours) Timestep: 4390000 mean reward (100 episodes): 1473.5600 best mean reward: 1473.5600 current episode reward: 3000.0000 episodes: 2792 exploration: 0.02372 learning_rate: 0.00006 elapsed time: 18939.9 seconds (5.26 hours) Timestep: 4400000 mean reward (100 episodes): 1505.1200 best mean reward: 1505.1200 current episode reward: 3084.0000 episodes: 2795 exploration: 0.02350 learning_rate: 0.00006 elapsed time: 18985.3 seconds (5.27 hours) Timestep: 4410000 mean reward (100 episodes): 1537.1200 best mean reward: 1537.1200 current episode reward: 1380.0000 episodes: 2798 exploration: 0.02327 learning_rate: 0.00006 elapsed time: 19030.3 seconds (5.29 hours) Timestep: 4420000 mean reward (100 episodes): 1556.0400 best mean reward: 1556.0400 current episode reward: 1332.0000 episodes: 2801 exploration: 0.02305 learning_rate: 0.00006 elapsed time: 19075.1 seconds (5.30 hours) Timestep: 4430000 mean reward (100 episodes): 1586.0000 best mean reward: 1586.0000 current episode reward: 4156.0000 episodes: 2803 exploration: 0.02282 learning_rate: 0.00006 elapsed time: 19121.2 seconds (5.31 hours) Timestep: 4440000 mean reward (100 episodes): 1612.6800 best mean reward: 1612.6800 current episode reward: 1432.0000 episodes: 2806 exploration: 0.02260 learning_rate: 0.00006 elapsed time: 19166.9 seconds (5.32 hours) Timestep: 4450000 mean reward (100 episodes): 1638.8000 best mean reward: 1638.8000 current episode reward: 2832.0000 episodes: 2808 exploration: 0.02237 learning_rate: 0.00006 elapsed time: 19213.0 seconds (5.34 hours) Timestep: 4460000 mean reward (100 episodes): 1655.9600 best mean reward: 1655.9600 current episode reward: 4028.0000 episodes: 2810 exploration: 0.02215 learning_rate: 0.00006 elapsed time: 19259.2 seconds (5.35 hours) Timestep: 4470000 mean reward (100 episodes): 1662.3200 best mean reward: 1666.6400 current episode reward: 852.0000 episodes: 2813 exploration: 0.02192 learning_rate: 0.00006 elapsed time: 19304.7 seconds (5.36 hours) Timestep: 4480000 mean reward (100 episodes): 1697.1200 best mean reward: 1697.1200 current episode reward: 3840.0000 episodes: 2816 exploration: 0.02170 learning_rate: 0.00006 elapsed time: 19349.8 seconds (5.37 hours) Timestep: 4490000 mean reward (100 episodes): 1718.8400 best mean reward: 1718.8400 current episode reward: 1332.0000 episodes: 2819 exploration: 0.02147 learning_rate: 0.00006 elapsed time: 19395.3 seconds (5.39 hours) Timestep: 4500000 mean reward (100 episodes): 1758.4000 best mean reward: 1758.4000 current episode reward: 3360.0000 episodes: 2821 exploration: 0.02125 learning_rate: 0.00006 elapsed time: 19440.0 seconds (5.40 hours) Timestep: 4510000 mean reward (100 episodes): 1802.2000 best mean reward: 1802.2000 current episode reward: 1744.0000 episodes: 2824 exploration: 0.02103 learning_rate: 0.00006 elapsed time: 19485.5 seconds (5.41 hours) Timestep: 4520000 mean reward (100 episodes): 1834.8000 best mean reward: 1834.8000 current episode reward: 4992.0000 episodes: 2826 exploration: 0.02080 learning_rate: 0.00006 elapsed time: 19530.5 seconds (5.43 hours) Timestep: 4530000 mean reward (100 episodes): 1874.0800 best mean reward: 1874.0800 current episode reward: 2832.0000 episodes: 2829 exploration: 0.02057 learning_rate: 0.00006 elapsed time: 19575.6 seconds (5.44 hours) Timestep: 4540000 mean reward (100 episodes): 1903.8400 best mean reward: 1903.8400 current episode reward: 3780.0000 episodes: 2830 exploration: 0.02035 learning_rate: 0.00006 elapsed time: 19620.9 seconds (5.45 hours) Timestep: 4550000 mean reward (100 episodes): 1917.2000 best mean reward: 1922.6000 current episode reward: 1900.0000 episodes: 2832 exploration: 0.02013 learning_rate: 0.00006 elapsed time: 19666.7 seconds (5.46 hours) Timestep: 4560000 mean reward (100 episodes): 1970.3200 best mean reward: 1970.3200 current episode reward: 3600.0000 episodes: 2834 exploration: 0.01990 learning_rate: 0.00006 elapsed time: 19712.7 seconds (5.48 hours) Timestep: 4570000 mean reward (100 episodes): 2007.1600 best mean reward: 2007.1600 current episode reward: 1588.0000 episodes: 2837 exploration: 0.01967 learning_rate: 0.00006 elapsed time: 19758.2 seconds (5.49 hours) Timestep: 4580000 mean reward (100 episodes): 2028.5200 best mean reward: 2028.5200 current episode reward: 2440.0000 episodes: 2840 exploration: 0.01945 learning_rate: 0.00006 elapsed time: 19803.5 seconds (5.50 hours) Timestep: 4590000 mean reward (100 episodes): 2054.2400 best mean reward: 2054.2400 current episode reward: 3084.0000 episodes: 2842 exploration: 0.01923 learning_rate: 0.00006 elapsed time: 19848.6 seconds (5.51 hours) Timestep: 4600000 mean reward (100 episodes): 2095.4600 best mean reward: 2095.4600 current episode reward: 4470.0000 episodes: 2844 exploration: 0.01900 learning_rate: 0.00006 elapsed time: 19893.3 seconds (5.53 hours) Timestep: 4610000 mean reward (100 episodes): 2117.6800 best mean reward: 2117.6800 current episode reward: 3810.0000 episodes: 2846 exploration: 0.01878 learning_rate: 0.00005 elapsed time: 19938.6 seconds (5.54 hours) Timestep: 4620000 mean reward (100 episodes): 2196.6000 best mean reward: 2196.6000 current episode reward: 3840.0000 episodes: 2848 exploration: 0.01855 learning_rate: 0.00005 elapsed time: 19983.9 seconds (5.55 hours) Timestep: 4630000 mean reward (100 episodes): 2244.4800 best mean reward: 2244.4800 current episode reward: 3600.0000 episodes: 2850 exploration: 0.01832 learning_rate: 0.00005 elapsed time: 20029.8 seconds (5.56 hours) Timestep: 4640000 mean reward (100 episodes): 2286.6800 best mean reward: 2286.6800 current episode reward: 3784.0000 episodes: 2852 exploration: 0.01810 learning_rate: 0.00005 elapsed time: 20075.2 seconds (5.58 hours) Timestep: 4650000 mean reward (100 episodes): 2311.0400 best mean reward: 2311.0400 current episode reward: 3480.0000 episodes: 2853 exploration: 0.01788 learning_rate: 0.00005 elapsed time: 20120.7 seconds (5.59 hours) Timestep: 4660000 mean reward (100 episodes): 2417.8200 best mean reward: 2417.8200 current episode reward: 5246.0000 episodes: 2855 exploration: 0.01765 learning_rate: 0.00005 elapsed time: 20166.4 seconds (5.60 hours) Timestep: 4670000 mean reward (100 episodes): 2452.4200 best mean reward: 2452.4200 current episode reward: 4892.0000 episodes: 2856 exploration: 0.01742 learning_rate: 0.00005 elapsed time: 20211.6 seconds (5.61 hours) Timestep: 4680000 mean reward (100 episodes): 2514.7400 best mean reward: 2514.7400 current episode reward: 5070.0000 episodes: 2858 exploration: 0.01720 learning_rate: 0.00005 elapsed time: 20257.7 seconds (5.63 hours) Timestep: 4690000 mean reward (100 episodes): 2576.7000 best mean reward: 2576.7000 current episode reward: 4560.0000 episodes: 2860 exploration: 0.01697 learning_rate: 0.00005 elapsed time: 20303.6 seconds (5.64 hours) Timestep: 4700000 mean reward (100 episodes): 2607.9800 best mean reward: 2607.9800 current episode reward: 4412.0000 episodes: 2862 exploration: 0.01675 learning_rate: 0.00005 elapsed time: 20349.2 seconds (5.65 hours) Timestep: 4710000 mean reward (100 episodes): 2658.4000 best mean reward: 2658.4000 current episode reward: 3600.0000 episodes: 2864 exploration: 0.01652 learning_rate: 0.00005 elapsed time: 20394.0 seconds (5.67 hours) Timestep: 4720000 mean reward (100 episodes): 2678.0800 best mean reward: 2678.0800 current episode reward: 2944.0000 episodes: 2866 exploration: 0.01630 learning_rate: 0.00005 elapsed time: 20439.2 seconds (5.68 hours) Timestep: 4730000 mean reward (100 episodes): 2746.4800 best mean reward: 2746.4800 current episode reward: 2944.0000 episodes: 2868 exploration: 0.01607 learning_rate: 0.00005 elapsed time: 20484.4 seconds (5.69 hours) Timestep: 4740000 mean reward (100 episodes): 2778.4800 best mean reward: 2778.4800 current episode reward: 4892.0000 episodes: 2869 exploration: 0.01585 learning_rate: 0.00005 elapsed time: 20528.5 seconds (5.70 hours) Timestep: 4750000 mean reward (100 episodes): 2809.4000 best mean reward: 2809.4000 current episode reward: 1640.0000 episodes: 2871 exploration: 0.01562 learning_rate: 0.00005 elapsed time: 20574.1 seconds (5.72 hours) Timestep: 4760000 mean reward (100 episodes): 2849.4000 best mean reward: 2849.4000 current episode reward: 3840.0000 episodes: 2873 exploration: 0.01540 learning_rate: 0.00005 elapsed time: 20619.4 seconds (5.73 hours) Timestep: 4770000 mean reward (100 episodes): 2931.9800 best mean reward: 2931.9800 current episode reward: 5340.0000 episodes: 2875 exploration: 0.01517 learning_rate: 0.00005 elapsed time: 20665.3 seconds (5.74 hours) Timestep: 4780000 mean reward (100 episodes): 2948.2200 best mean reward: 2948.2200 current episode reward: 5212.0000 episodes: 2877 exploration: 0.01495 learning_rate: 0.00005 elapsed time: 20710.4 seconds (5.75 hours) Timestep: 4790000 mean reward (100 episodes): 2975.5200 best mean reward: 2975.5200 current episode reward: 4734.0000 episodes: 2878 exploration: 0.01472 learning_rate: 0.00005 elapsed time: 20755.9 seconds (5.77 hours) Timestep: 4800000 mean reward (100 episodes): 3012.0400 best mean reward: 3012.0400 current episode reward: 2160.0000 episodes: 2881 exploration: 0.01450 learning_rate: 0.00005 elapsed time: 20800.6 seconds (5.78 hours) Timestep: 4810000 mean reward (100 episodes): 3066.2000 best mean reward: 3066.2000 current episode reward: 3252.0000 episodes: 2883 exploration: 0.01427 learning_rate: 0.00005 elapsed time: 20845.8 seconds (5.79 hours) Timestep: 4820000 mean reward (100 episodes): 3058.0800 best mean reward: 3066.2000 current episode reward: 1744.0000 episodes: 2885 exploration: 0.01405 learning_rate: 0.00005 elapsed time: 20891.1 seconds (5.80 hours) Timestep: 4830000 mean reward (100 episodes): 3133.3200 best mean reward: 3133.3200 current episode reward: 1848.0000 episodes: 2888 exploration: 0.01382 learning_rate: 0.00005 elapsed time: 20936.8 seconds (5.82 hours) Timestep: 4840000 mean reward (100 episodes): 3157.4400 best mean reward: 3157.4400 current episode reward: 1952.0000 episodes: 2890 exploration: 0.01360 learning_rate: 0.00005 elapsed time: 20982.4 seconds (5.83 hours) Timestep: 4850000 mean reward (100 episodes): 3225.5200 best mean reward: 3225.5200 current episode reward: 4606.0000 episodes: 2892 exploration: 0.01337 learning_rate: 0.00005 elapsed time: 21027.6 seconds (5.84 hours) Timestep: 4860000 mean reward (100 episodes): 3224.9600 best mean reward: 3225.5200 current episode reward: 3028.0000 episodes: 2895 exploration: 0.01315 learning_rate: 0.00005 elapsed time: 21073.0 seconds (5.85 hours) Timestep: 4870000 mean reward (100 episodes): 3241.0400 best mean reward: 3241.0400 current episode reward: 4384.0000 episodes: 2896 exploration: 0.01292 learning_rate: 0.00005 elapsed time: 21118.1 seconds (5.87 hours) Timestep: 4880000 mean reward (100 episodes): 3312.0800 best mean reward: 3312.0800 current episode reward: 4412.0000 episodes: 2898 exploration: 0.01270 learning_rate: 0.00005 elapsed time: 21162.3 seconds (5.88 hours) Timestep: 4890000 mean reward (100 episodes): 3350.5800 best mean reward: 3350.5800 current episode reward: 4734.0000 episodes: 2900 exploration: 0.01247 learning_rate: 0.00005 elapsed time: 21207.1 seconds (5.89 hours) Timestep: 4900000 mean reward (100 episodes): 3399.1200 best mean reward: 3399.1200 current episode reward: 4290.0000 episodes: 2902 exploration: 0.01225 learning_rate: 0.00005 elapsed time: 21251.9 seconds (5.90 hours) Timestep: 4910000 mean reward (100 episodes): 3398.9600 best mean reward: 3399.1200 current episode reward: 2160.0000 episodes: 2904 exploration: 0.01202 learning_rate: 0.00005 elapsed time: 21297.2 seconds (5.92 hours) Timestep: 4920000 mean reward (100 episodes): 3439.4400 best mean reward: 3439.4400 current episode reward: 4892.0000 episodes: 2906 exploration: 0.01180 learning_rate: 0.00005 elapsed time: 21342.7 seconds (5.93 hours) Timestep: 4930000 mean reward (100 episodes): 3480.9600 best mean reward: 3480.9600 current episode reward: 6312.0000 episodes: 2907 exploration: 0.01157 learning_rate: 0.00005 elapsed time: 21389.1 seconds (5.94 hours) Timestep: 4940000 mean reward (100 episodes): 3497.1200 best mean reward: 3498.8400 current episode reward: 4512.0000 episodes: 2910 exploration: 0.01135 learning_rate: 0.00005 elapsed time: 21433.6 seconds (5.95 hours) Timestep: 4950000 mean reward (100 episodes): 3534.1200 best mean reward: 3534.1200 current episode reward: 5340.0000 episodes: 2911 exploration: 0.01112 learning_rate: 0.00005 elapsed time: 21478.9 seconds (5.97 hours) Timestep: 4960000 mean reward (100 episodes): 3618.9800 best mean reward: 3618.9800 current episode reward: 5632.0000 episodes: 2913 exploration: 0.01090 learning_rate: 0.00005 elapsed time: 21524.0 seconds (5.98 hours) Timestep: 4970000 mean reward (100 episodes): 3647.5000 best mean reward: 3647.5000 current episode reward: 5340.0000 episodes: 2916 exploration: 0.01067 learning_rate: 0.00005 elapsed time: 21569.9 seconds (5.99 hours) Timestep: 4980000 mean reward (100 episodes): 3696.7200 best mean reward: 3696.7200 current episode reward: 4500.0000 episodes: 2918 exploration: 0.01045 learning_rate: 0.00005 elapsed time: 21615.4 seconds (6.00 hours) Timestep: 4990000 mean reward (100 episodes): 3732.0000 best mean reward: 3732.0000 current episode reward: 5276.0000 episodes: 2920 exploration: 0.01022 learning_rate: 0.00005 elapsed time: 21661.1 seconds (6.02 hours) Timestep: 5000000 mean reward (100 episodes): 3772.5600 best mean reward: 3772.5600 current episode reward: 7416.0000 episodes: 2921 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 21706.7 seconds (6.03 hours) Timestep: 5010000 mean reward (100 episodes): 3806.2400 best mean reward: 3806.2400 current episode reward: 2160.0000 episodes: 2923 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 21751.4 seconds (6.04 hours) Timestep: 5020000 mean reward (100 episodes): 3856.3600 best mean reward: 3856.3600 current episode reward: 3964.0000 episodes: 2925 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 21796.6 seconds (6.05 hours) Timestep: 5030000 mean reward (100 episodes): 3855.5600 best mean reward: 3863.9200 current episode reward: 2108.0000 episodes: 2927 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 21841.4 seconds (6.07 hours) Timestep: 5040000 mean reward (100 episodes): 3889.9600 best mean reward: 3889.9600 current episode reward: 3840.0000 episodes: 2929 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 21887.0 seconds (6.08 hours) Timestep: 5050000 mean reward (100 episodes): 3940.1600 best mean reward: 3940.1600 current episode reward: 5504.0000 episodes: 2931 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 21931.7 seconds (6.09 hours) Timestep: 5060000 mean reward (100 episodes): 3954.2400 best mean reward: 3971.3600 current episode reward: 2608.0000 episodes: 2933 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 21976.0 seconds (6.10 hours) Timestep: 5070000 mean reward (100 episodes): 3953.6400 best mean reward: 3971.3600 current episode reward: 3000.0000 episodes: 2935 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22021.1 seconds (6.12 hours) Timestep: 5080000 mean reward (100 episodes): 3986.4200 best mean reward: 3986.4200 current episode reward: 2160.0000 episodes: 2937 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22066.6 seconds (6.13 hours) Timestep: 5090000 mean reward (100 episodes): 4015.2200 best mean reward: 4015.2200 current episode reward: 3300.0000 episodes: 2939 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22111.1 seconds (6.14 hours) Timestep: 5100000 mean reward (100 episodes): 4089.2400 best mean reward: 4089.2400 current episode reward: 6262.0000 episodes: 2941 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22156.4 seconds (6.15 hours) Timestep: 5110000 mean reward (100 episodes): 4116.3800 best mean reward: 4116.3800 current episode reward: 5798.0000 episodes: 2942 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22200.9 seconds (6.17 hours) Timestep: 5120000 mean reward (100 episodes): 4133.2000 best mean reward: 4138.9000 current episode reward: 3900.0000 episodes: 2944 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22247.0 seconds (6.18 hours) Timestep: 5130000 mean reward (100 episodes): 4195.0000 best mean reward: 4195.0000 current episode reward: 8132.0000 episodes: 2945 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22291.5 seconds (6.19 hours) Timestep: 5140000 mean reward (100 episodes): 4174.1800 best mean reward: 4217.8600 current episode reward: 2384.0000 episodes: 2948 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22336.2 seconds (6.20 hours) Timestep: 5150000 mean reward (100 episodes): 4185.5800 best mean reward: 4217.8600 current episode reward: 3420.0000 episodes: 2950 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22381.8 seconds (6.22 hours) Timestep: 5160000 mean reward (100 episodes): 4195.7400 best mean reward: 4217.8600 current episode reward: 3420.0000 episodes: 2952 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22427.5 seconds (6.23 hours) Timestep: 5170000 mean reward (100 episodes): 4146.1800 best mean reward: 4217.8600 current episode reward: 2608.0000 episodes: 2954 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22472.8 seconds (6.24 hours) Timestep: 5180000 mean reward (100 episodes): 4136.4400 best mean reward: 4217.8600 current episode reward: 5504.0000 episodes: 2956 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22517.7 seconds (6.25 hours) Timestep: 5190000 mean reward (100 episodes): 4164.8400 best mean reward: 4217.8600 current episode reward: 4860.0000 episodes: 2958 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22562.7 seconds (6.27 hours) Timestep: 5200000 mean reward (100 episodes): 4169.2000 best mean reward: 4217.8600 current episode reward: 2944.0000 episodes: 2960 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22607.7 seconds (6.28 hours) Timestep: 5210000 mean reward (100 episodes): 4196.3000 best mean reward: 4217.8600 current episode reward: 3660.0000 episodes: 2962 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22653.6 seconds (6.29 hours) Timestep: 5220000 mean reward (100 episodes): 4204.6200 best mean reward: 4217.8600 current episode reward: 5246.0000 episodes: 2963 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22699.1 seconds (6.31 hours) Timestep: 5230000 mean reward (100 episodes): 4248.1000 best mean reward: 4248.1000 current episode reward: 5964.0000 episodes: 2965 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22744.8 seconds (6.32 hours) Timestep: 5240000 mean reward (100 episodes): 4236.7200 best mean reward: 4264.0600 current episode reward: 3990.0000 episodes: 2967 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22789.6 seconds (6.33 hours) Timestep: 5250000 mean reward (100 episodes): 4232.4400 best mean reward: 4267.5600 current episode reward: 1380.0000 episodes: 2969 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22835.7 seconds (6.34 hours) Timestep: 5260000 mean reward (100 episodes): 4218.0000 best mean reward: 4267.5600 current episode reward: 1744.0000 episodes: 2972 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22881.5 seconds (6.36 hours) Timestep: 5270000 mean reward (100 episodes): 4229.8400 best mean reward: 4267.5600 current episode reward: 5024.0000 episodes: 2973 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22926.9 seconds (6.37 hours) Timestep: 5280000 mean reward (100 episodes): 4208.2400 best mean reward: 4267.5600 current episode reward: 1952.0000 episodes: 2976 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22972.6 seconds (6.38 hours) Timestep: 5290000 mean reward (100 episodes): 4203.9600 best mean reward: 4267.5600 current episode reward: 3720.0000 episodes: 2978 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23018.1 seconds (6.39 hours) Timestep: 5300000 mean reward (100 episodes): 4226.0200 best mean reward: 4267.5600 current episode reward: 3196.0000 episodes: 2981 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23064.4 seconds (6.41 hours) Timestep: 5310000 mean reward (100 episodes): 4227.7400 best mean reward: 4267.5600 current episode reward: 3000.0000 episodes: 2983 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23109.5 seconds (6.42 hours) Timestep: 5320000 mean reward (100 episodes): 4263.1600 best mean reward: 4267.5600 current episode reward: 4050.0000 episodes: 2985 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23154.5 seconds (6.43 hours) Timestep: 5330000 mean reward (100 episodes): 4254.3600 best mean reward: 4267.5600 current episode reward: 3964.0000 episodes: 2987 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23199.5 seconds (6.44 hours) Timestep: 5340000 mean reward (100 episodes): 4258.8800 best mean reward: 4281.9200 current episode reward: 1536.0000 episodes: 2989 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23244.7 seconds (6.46 hours) Timestep: 5350000 mean reward (100 episodes): 4289.6200 best mean reward: 4289.6200 current episode reward: 6508.0000 episodes: 2991 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23290.4 seconds (6.47 hours) Timestep: 5360000 mean reward (100 episodes): 4283.3200 best mean reward: 4289.6200 current episode reward: 2748.0000 episodes: 2993 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23335.7 seconds (6.48 hours) Timestep: 5370000 mean reward (100 episodes): 4340.4000 best mean reward: 4340.4000 current episode reward: 6096.0000 episodes: 2995 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23381.3 seconds (6.49 hours) Timestep: 5380000 mean reward (100 episodes): 4333.9200 best mean reward: 4343.2400 current episode reward: 5412.0000 episodes: 2997 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23427.7 seconds (6.51 hours) Timestep: 5390000 mean reward (100 episodes): 4318.1400 best mean reward: 4343.2400 current episode reward: 3900.0000 episodes: 3000 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23473.2 seconds (6.52 hours) Timestep: 5400000 mean reward (100 episodes): 4358.3800 best mean reward: 4358.3800 current episode reward: 6146.0000 episodes: 3002 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23519.8 seconds (6.53 hours) Timestep: 5410000 mean reward (100 episodes): 4390.9000 best mean reward: 4390.9000 current episode reward: 5952.0000 episodes: 3004 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23566.0 seconds (6.55 hours) Timestep: 5420000 mean reward (100 episodes): 4417.8200 best mean reward: 4417.8200 current episode reward: 6804.0000 episodes: 3006 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23612.0 seconds (6.56 hours) Timestep: 5430000 mean reward (100 episodes): 4384.3800 best mean reward: 4417.8200 current episode reward: 3240.0000 episodes: 3008 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23657.3 seconds (6.57 hours) Timestep: 5440000 mean reward (100 episodes): 4426.3000 best mean reward: 4426.3000 current episode reward: 5602.0000 episodes: 3010 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23704.1 seconds (6.58 hours) Timestep: 5450000 mean reward (100 episodes): 4417.3200 best mean reward: 4428.8800 current episode reward: 4606.0000 episodes: 3012 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23750.2 seconds (6.60 hours) Timestep: 5460000 mean reward (100 episodes): 4438.8600 best mean reward: 4438.8600 current episode reward: 5812.0000 episodes: 3014 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23796.8 seconds (6.61 hours) Timestep: 5470000 mean reward (100 episodes): 4459.8000 best mean reward: 4461.6600 current episode reward: 5154.0000 episodes: 3016 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23841.8 seconds (6.62 hours) Timestep: 5480000 mean reward (100 episodes): 4423.8200 best mean reward: 4461.6600 current episode reward: 4156.0000 episodes: 3018 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23887.7 seconds (6.64 hours) Timestep: 5490000 mean reward (100 episodes): 4467.9400 best mean reward: 4467.9400 current episode reward: 5748.0000 episodes: 3020 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23933.5 seconds (6.65 hours) Timestep: 5500000 mean reward (100 episodes): 4442.1600 best mean reward: 4467.9400 current episode reward: 7242.0000 episodes: 3022 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23979.0 seconds (6.66 hours) Timestep: 5510000 mean reward (100 episodes): 4482.7200 best mean reward: 4482.7200 current episode reward: 4704.0000 episodes: 3024 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24024.3 seconds (6.67 hours) Timestep: 5520000 mean reward (100 episodes): 4505.1400 best mean reward: 4509.1400 current episode reward: 5348.0000 episodes: 3026 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24070.3 seconds (6.69 hours) Timestep: 5530000 mean reward (100 episodes): 4583.4200 best mean reward: 4583.4200 current episode reward: 9680.0000 episodes: 3028 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24116.1 seconds (6.70 hours) Timestep: 5540000 mean reward (100 episodes): 4627.5800 best mean reward: 4627.5800 current episode reward: 8256.0000 episodes: 3029 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24161.6 seconds (6.71 hours) Timestep: 5550000 mean reward (100 episodes): 4554.0800 best mean reward: 4627.5800 current episode reward: 3990.0000 episodes: 3032 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24207.6 seconds (6.72 hours) Timestep: 5560000 mean reward (100 episodes): 4557.4400 best mean reward: 4627.5800 current episode reward: 2944.0000 episodes: 3033 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24253.7 seconds (6.74 hours) Timestep: 5570000 mean reward (100 episodes): 4639.6400 best mean reward: 4639.6400 current episode reward: 7182.0000 episodes: 3035 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24299.0 seconds (6.75 hours) Timestep: 5580000 mean reward (100 episodes): 4653.2600 best mean reward: 4653.2600 current episode reward: 6876.0000 episodes: 3038 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24344.5 seconds (6.76 hours) Timestep: 5590000 mean reward (100 episodes): 4652.9200 best mean reward: 4679.8400 current episode reward: 2944.0000 episodes: 3040 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24390.2 seconds (6.78 hours) Timestep: 5600000 mean reward (100 episodes): 4610.9200 best mean reward: 4679.8400 current episode reward: 3000.0000 episodes: 3042 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24436.2 seconds (6.79 hours) Timestep: 5610000 mean reward (100 episodes): 4605.1000 best mean reward: 4679.8400 current episode reward: 2832.0000 episodes: 3044 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24481.7 seconds (6.80 hours) Timestep: 5620000 mean reward (100 episodes): 4592.3000 best mean reward: 4679.8400 current episode reward: 3672.0000 episodes: 3046 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24527.5 seconds (6.81 hours) Timestep: 5630000 mean reward (100 episodes): 4632.0400 best mean reward: 4679.8400 current episode reward: 3000.0000 episodes: 3048 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24572.3 seconds (6.83 hours) Timestep: 5640000 mean reward (100 episodes): 4652.3600 best mean reward: 4679.8400 current episode reward: 4028.0000 episodes: 3050 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24616.9 seconds (6.84 hours) Timestep: 5650000 mean reward (100 episodes): 4665.7000 best mean reward: 4679.8400 current episode reward: 3540.0000 episodes: 3052 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24663.3 seconds (6.85 hours) Timestep: 5660000 mean reward (100 episodes): 4699.6600 best mean reward: 4699.6600 current episode reward: 4284.0000 episodes: 3055 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24708.9 seconds (6.86 hours) Timestep: 5670000 mean reward (100 episodes): 4634.3000 best mean reward: 4699.6600 current episode reward: 3000.0000 episodes: 3057 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24754.0 seconds (6.88 hours) Timestep: 5680000 mean reward (100 episodes): 4631.0200 best mean reward: 4699.6600 current episode reward: 5880.0000 episodes: 3059 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24799.8 seconds (6.89 hours) Timestep: 5690000 mean reward (100 episodes): 4638.3600 best mean reward: 4699.6600 current episode reward: 6240.0000 episodes: 3061 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24845.0 seconds (6.90 hours) Timestep: 5700000 mean reward (100 episodes): 4651.7400 best mean reward: 4699.6600 current episode reward: 4348.0000 episodes: 3063 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24891.1 seconds (6.91 hours) Timestep: 5710000 mean reward (100 episodes): 4660.4200 best mean reward: 4699.6600 current episode reward: 7920.0000 episodes: 3065 exploration: 0.01000 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4807.6200 best mean reward: 4822.1600 current episode reward: 3840.0000 episodes: 3076 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25162.5 seconds (6.99 hours) Timestep: 5770000 mean reward (100 episodes): 4767.3200 best mean reward: 4822.1600 current episode reward: 1588.0000 episodes: 3078 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25208.4 seconds (7.00 hours) Timestep: 5780000 mean reward (100 episodes): 4821.8000 best mean reward: 4822.1600 current episode reward: 4414.0000 episodes: 3080 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25254.5 seconds (7.02 hours) Timestep: 5790000 mean reward (100 episodes): 4859.7200 best mean reward: 4859.7200 current episode reward: 7260.0000 episodes: 3082 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25300.5 seconds (7.03 hours) Timestep: 5800000 mean reward (100 episodes): 4881.4000 best mean reward: 4887.3200 current episode reward: 3308.0000 episodes: 3084 exploration: 0.01000 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5171.2800 best mean reward: 5171.2800 current episode reward: 6462.0000 episodes: 3109 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25981.2 seconds (7.22 hours) Timestep: 5950000 mean reward (100 episodes): 5172.9200 best mean reward: 5180.9400 current episode reward: 4796.0000 episodes: 3111 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26026.7 seconds (7.23 hours) Timestep: 5960000 mean reward (100 episodes): 5170.0000 best mean reward: 5198.9800 current episode reward: 4412.0000 episodes: 3114 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26072.4 seconds (7.24 hours) Timestep: 5970000 mean reward (100 episodes): 5134.7800 best mean reward: 5198.9800 current episode reward: 2496.0000 episodes: 3116 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26117.7 seconds (7.25 hours) Timestep: 5980000 mean reward (100 episodes): 5192.6200 best mean reward: 5198.9800 current episode reward: 7684.0000 episodes: 3117 exploration: 0.01000 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5141.5800 best mean reward: 5233.6600 current episode reward: 3900.0000 episodes: 3127 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26391.3 seconds (7.33 hours) Timestep: 6040000 mean reward (100 episodes): 5033.9200 best mean reward: 5233.6600 current episode reward: 1380.0000 episodes: 3130 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26436.1 seconds (7.34 hours) Timestep: 6050000 mean reward (100 episodes): 5047.5400 best mean reward: 5233.6600 current episode reward: 6600.0000 episodes: 3132 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26481.8 seconds (7.36 hours) Timestep: 6060000 mean reward (100 episodes): 5046.9600 best mean reward: 5233.6600 current episode reward: 5130.0000 episodes: 3134 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26526.8 seconds (7.37 hours) Timestep: 6070000 mean reward (100 episodes): 5053.6600 best mean reward: 5233.6600 current episode reward: 5310.0000 episodes: 3137 exploration: 0.01000 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5184.0600 best mean reward: 5233.6600 current episode reward: 6682.0000 episodes: 3145 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26798.6 seconds (7.44 hours) Timestep: 6130000 mean reward (100 episodes): 5194.1000 best mean reward: 5233.6600 current episode reward: 4350.0000 episodes: 3147 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26844.4 seconds (7.46 hours) Timestep: 6140000 mean reward (100 episodes): 5215.7400 best mean reward: 5233.6600 current episode reward: 8552.0000 episodes: 3149 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26890.3 seconds (7.47 hours) Timestep: 6150000 mean reward (100 episodes): 5254.0200 best mean reward: 5254.0200 current episode reward: 8064.0000 episodes: 3151 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26936.1 seconds (7.48 hours) Timestep: 6160000 mean reward (100 episodes): 5261.3400 best mean reward: 5265.6600 current episode reward: 5268.0000 episodes: 3153 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26982.4 seconds (7.50 hours) Timestep: 6170000 mean reward (100 episodes): 5259.4600 best mean reward: 5265.6600 current episode reward: 4260.0000 episodes: 3155 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27028.4 seconds (7.51 hours) Timestep: 6180000 mean reward (100 episodes): 5307.8200 best mean reward: 5307.8200 current episode reward: 6504.0000 episodes: 3157 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27074.3 seconds (7.52 hours) Timestep: 6190000 mean reward (100 episodes): 5311.6600 best mean reward: 5311.6600 current episode reward: 4284.0000 episodes: 3158 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27119.3 seconds (7.53 hours) Timestep: 6200000 mean reward (100 episodes): 5332.8400 best mean reward: 5332.8400 current episode reward: 4512.0000 episodes: 3160 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27164.8 seconds (7.55 hours) Timestep: 6210000 mean reward (100 episodes): 5309.8800 best mean reward: 5332.8400 current episode reward: 4220.0000 episodes: 3162 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27210.0 seconds (7.56 hours) Timestep: 6220000 mean reward (100 episodes): 5362.9400 best mean reward: 5362.9400 current episode reward: 7020.0000 episodes: 3164 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27256.1 seconds (7.57 hours) Timestep: 6230000 mean reward (100 episodes): 5335.7000 best mean reward: 5362.9400 current episode reward: 6198.0000 episodes: 3166 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27301.5 seconds (7.58 hours) Timestep: 6240000 mean reward (100 episodes): 5294.2800 best mean reward: 5362.9400 current episode reward: 5632.0000 episodes: 3168 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27346.4 seconds (7.60 hours) Timestep: 6250000 mean reward (100 episodes): 5322.7400 best mean reward: 5362.9400 current episode reward: 5536.0000 episodes: 3170 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27391.9 seconds (7.61 hours) Timestep: 6260000 mean reward (100 episodes): 5299.4400 best mean reward: 5362.9400 current episode reward: 6146.0000 episodes: 3172 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27436.8 seconds (7.62 hours) Timestep: 6270000 mean reward (100 episodes): 5391.0400 best mean reward: 5391.0400 current episode reward: 5926.0000 episodes: 3174 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27482.2 seconds (7.63 hours) Timestep: 6280000 mean reward (100 episodes): 5417.3200 best mean reward: 5417.3200 current episode reward: 5376.0000 episodes: 3176 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27527.9 seconds (7.65 hours) Timestep: 6290000 mean reward (100 episodes): 5452.2400 best mean reward: 5452.2400 current episode reward: 7392.0000 episodes: 3177 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27573.4 seconds (7.66 hours) Timestep: 6300000 mean reward (100 episodes): 5467.0200 best mean reward: 5501.7400 current episode reward: 3900.0000 episodes: 3180 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27619.0 seconds (7.67 hours) Timestep: 6310000 mean reward (100 episodes): 5510.4000 best mean reward: 5510.4000 current episode reward: 9674.0000 episodes: 3181 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27664.6 seconds (7.68 hours) Timestep: 6320000 mean reward (100 episodes): 5513.2000 best mean reward: 5513.2000 current episode reward: 7760.0000 episodes: 3183 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27710.2 seconds (7.70 hours) Timestep: 6330000 mean reward (100 episodes): 5539.3200 best mean reward: 5539.3200 current episode reward: 6562.0000 episodes: 3185 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27756.2 seconds (7.71 hours) Timestep: 6340000 mean reward (100 episodes): 5555.3800 best mean reward: 5555.3800 current episode reward: 6024.0000 episodes: 3187 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27801.2 seconds (7.72 hours) Timestep: 6350000 mean reward (100 episodes): 5577.8800 best mean reward: 5577.8800 current episode reward: 7046.0000 episodes: 3189 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27847.0 seconds (7.74 hours) Timestep: 6360000 mean reward (100 episodes): 5607.4000 best mean reward: 5607.4000 current episode reward: 5132.0000 episodes: 3191 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27892.4 seconds (7.75 hours) Timestep: 6370000 mean reward (100 episodes): 5642.5600 best mean reward: 5642.5600 current episode reward: 8604.0000 episodes: 3192 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27937.1 seconds (7.76 hours) Timestep: 6380000 mean reward (100 episodes): 5635.9800 best mean reward: 5644.6000 current episode reward: 4320.0000 episodes: 3194 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27982.0 seconds (7.77 hours) Timestep: 6390000 mean reward (100 episodes): 5616.7400 best mean reward: 5644.6000 current episode reward: 4734.0000 episodes: 3197 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28026.6 seconds (7.79 hours) Timestep: 6400000 mean reward (100 episodes): 5593.6400 best mean reward: 5644.6000 current episode reward: 5700.0000 episodes: 3198 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28072.2 seconds (7.80 hours) Timestep: 6410000 mean reward (100 episodes): 5612.7600 best mean reward: 5644.6000 current episode reward: 4832.0000 episodes: 3200 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28118.5 seconds (7.81 hours) Timestep: 6420000 mean reward (100 episodes): 5590.9400 best mean reward: 5644.6000 current episode reward: 6182.0000 episodes: 3202 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28163.5 seconds (7.82 hours) Timestep: 6430000 mean reward (100 episodes): 5573.0400 best mean reward: 5644.6000 current episode reward: 5608.0000 episodes: 3204 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28209.3 seconds (7.84 hours) Timestep: 6440000 mean reward (100 episodes): 5562.8200 best mean reward: 5644.6000 current episode reward: 4770.0000 episodes: 3206 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28255.1 seconds (7.85 hours) Timestep: 6450000 mean reward (100 episodes): 5609.1400 best mean reward: 5644.6000 current episode reward: 6802.0000 episodes: 3208 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28301.3 seconds (7.86 hours) Timestep: 6460000 mean reward (100 episodes): 5642.8800 best mean reward: 5644.6000 current episode reward: 9760.0000 episodes: 3210 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28346.0 seconds (7.87 hours) Timestep: 6470000 mean reward (100 episodes): 5656.3000 best mean reward: 5656.3000 current episode reward: 6138.0000 episodes: 3211 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28392.2 seconds (7.89 hours) Timestep: 6480000 mean reward (100 episodes): 5716.5600 best mean reward: 5716.5600 current episode reward: 10290.0000 episodes: 3213 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28438.7 seconds (7.90 hours) Timestep: 6490000 mean reward (100 episodes): 5736.1600 best mean reward: 5736.1600 current episode reward: 4932.0000 episodes: 3215 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28484.3 seconds (7.91 hours) Timestep: 6500000 mean reward (100 episodes): 5747.7800 best mean reward: 5772.1600 current episode reward: 5246.0000 episodes: 3217 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28528.9 seconds (7.92 hours) Timestep: 6510000 mean reward (100 episodes): 5723.9800 best mean reward: 5772.1600 current episode reward: 5880.0000 episodes: 3218 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28574.7 seconds (7.94 hours) Timestep: 6520000 mean reward (100 episodes): 5771.4400 best mean reward: 5772.1600 current episode reward: 4542.0000 episodes: 3220 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28620.0 seconds (7.95 hours) Timestep: 6530000 mean reward (100 episodes): 5766.6800 best mean reward: 5772.1600 current episode reward: 5070.0000 episodes: 3222 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28665.6 seconds (7.96 hours) Timestep: 6540000 mean reward (100 episodes): 5796.0400 best mean reward: 5796.0400 current episode reward: 5892.0000 episodes: 3224 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28710.8 seconds (7.98 hours) Timestep: 6550000 mean reward (100 episodes): 5795.9800 best mean reward: 5815.3200 current episode reward: 2608.0000 episodes: 3226 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28755.9 seconds (7.99 hours) Timestep: 6560000 mean reward (100 episodes): 5839.4400 best mean reward: 5839.4400 current episode reward: 9080.0000 episodes: 3228 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28800.8 seconds (8.00 hours) Timestep: 6570000 mean reward (100 episodes): 5844.1600 best mean reward: 5844.1600 current episode reward: 3196.0000 episodes: 3230 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28846.3 seconds (8.01 hours) Timestep: 6580000 mean reward (100 episodes): 5850.0400 best mean reward: 5878.8400 current episode reward: 3720.0000 episodes: 3232 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28892.0 seconds (8.03 hours) Timestep: 6590000 mean reward (100 episodes): 5866.6800 best mean reward: 5879.5800 current episode reward: 3840.0000 episodes: 3234 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28937.3 seconds (8.04 hours) Timestep: 6600000 mean reward (100 episodes): 5923.2200 best mean reward: 5923.2200 current episode reward: 7314.0000 episodes: 3236 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28983.0 seconds (8.05 hours) Timestep: 6610000 mean reward (100 episodes): 5898.8600 best mean reward: 5925.5200 current episode reward: 3000.0000 episodes: 3238 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29028.9 seconds (8.06 hours) Timestep: 6620000 mean reward (100 episodes): 5933.4200 best mean reward: 5933.4200 current episode reward: 8766.0000 episodes: 3239 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29073.7 seconds (8.08 hours) Timestep: 6630000 mean reward (100 episodes): 5897.5800 best mean reward: 5933.4200 current episode reward: 5676.0000 episodes: 3241 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29119.7 seconds (8.09 hours) Timestep: 6640000 mean reward (100 episodes): 5839.4200 best mean reward: 5933.4200 current episode reward: 5926.0000 episodes: 3243 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29165.3 seconds (8.10 hours) Timestep: 6650000 mean reward (100 episodes): 5871.1600 best mean reward: 5933.4200 current episode reward: 6130.0000 episodes: 3245 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29210.9 seconds (8.11 hours) Timestep: 6660000 mean reward (100 episodes): 5907.6800 best mean reward: 5933.4200 current episode reward: 6232.0000 episodes: 3247 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29256.1 seconds (8.13 hours) Timestep: 6670000 mean reward (100 episodes): 5893.5600 best mean reward: 5933.4200 current episode reward: 5088.0000 episodes: 3249 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29301.8 seconds (8.14 hours) Timestep: 6680000 mean reward (100 episodes): 5868.1800 best mean reward: 5933.4200 current episode reward: 4670.0000 episodes: 3251 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29346.9 seconds (8.15 hours) Timestep: 6690000 mean reward (100 episodes): 5908.2200 best mean reward: 5933.4200 current episode reward: 6742.0000 episodes: 3253 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29392.4 seconds (8.16 hours) Timestep: 6700000 mean reward (100 episodes): 5926.1800 best mean reward: 5933.4200 current episode reward: 5952.0000 episodes: 3254 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29438.9 seconds (8.18 hours) Timestep: 6710000 mean reward (100 episodes): 5997.0200 best mean reward: 5997.0200 current episode reward: 5934.0000 episodes: 3256 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29484.9 seconds (8.19 hours) Timestep: 6720000 mean reward (100 episodes): 5995.3200 best mean reward: 5997.0200 current episode reward: 6334.0000 episodes: 3257 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29530.1 seconds (8.20 hours) Timestep: 6730000 mean reward (100 episodes): 6049.5000 best mean reward: 6051.8800 current episode reward: 7568.0000 episodes: 3259 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29574.7 seconds (8.22 hours) Timestep: 6740000 mean reward (100 episodes): 6073.1200 best mean reward: 6073.1200 current episode reward: 7566.0000 episodes: 3261 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29619.6 seconds (8.23 hours) Timestep: 6750000 mean reward (100 episodes): 6090.5600 best mean reward: 6090.5600 current episode reward: 5964.0000 episodes: 3262 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29666.1 seconds (8.24 hours) Timestep: 6760000 mean reward (100 episodes): 6078.3200 best mean reward: 6090.5600 current episode reward: 6742.0000 episodes: 3265 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29711.8 seconds (8.25 hours) Timestep: 6770000 mean reward (100 episodes): 6097.3400 best mean reward: 6097.3400 current episode reward: 8100.0000 episodes: 3266 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29757.6 seconds (8.27 hours) Timestep: 6780000 mean reward (100 episodes): 6106.6000 best mean reward: 6106.6000 current episode reward: 5820.0000 episodes: 3268 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29803.3 seconds (8.28 hours) Timestep: 6790000 mean reward (100 episodes): 6125.3800 best mean reward: 6125.3800 current episode reward: 7086.0000 episodes: 3270 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29848.6 seconds (8.29 hours) Timestep: 6800000 mean reward (100 episodes): 6214.5800 best mean reward: 6214.5800 current episode reward: 10300.0000 episodes: 3271 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29894.6 seconds (8.30 hours) Timestep: 6810000 mean reward (100 episodes): 6199.7800 best mean reward: 6222.9600 current episode reward: 6372.0000 episodes: 3273 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29940.6 seconds (8.32 hours) Timestep: 6820000 mean reward (100 episodes): 6196.6000 best mean reward: 6222.9600 current episode reward: 5608.0000 episodes: 3274 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29986.1 seconds (8.33 hours) Timestep: 6830000 mean reward (100 episodes): 6244.8400 best mean reward: 6244.8400 current episode reward: 7486.0000 episodes: 3276 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30032.0 seconds (8.34 hours) Timestep: 6840000 mean reward (100 episodes): 6217.6200 best mean reward: 6244.8400 current episode reward: 4670.0000 episodes: 3277 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30077.7 seconds (8.35 hours) Timestep: 6850000 mean reward (100 episodes): 6259.8800 best mean reward: 6259.8800 current episode reward: 6312.0000 episodes: 3279 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30122.4 seconds (8.37 hours) Timestep: 6860000 mean reward (100 episodes): 6266.1800 best mean reward: 6281.8400 current episode reward: 8108.0000 episodes: 3281 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30167.8 seconds (8.38 hours) Timestep: 6870000 mean reward (100 episodes): 6274.8800 best mean reward: 6281.8400 current episode reward: 7446.0000 episodes: 3283 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30213.4 seconds (8.39 hours) Timestep: 6880000 mean reward (100 episodes): 6269.0400 best mean reward: 6281.8400 current episode reward: 6876.0000 episodes: 3285 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30259.4 seconds (8.41 hours) Timestep: 6890000 mean reward (100 episodes): 6260.4000 best mean reward: 6281.8400 current episode reward: 4092.0000 episodes: 3286 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30305.0 seconds (8.42 hours) Timestep: 6900000 mean reward (100 episodes): 6295.0400 best mean reward: 6313.8600 current episode reward: 4860.0000 episodes: 3288 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30350.1 seconds (8.43 hours) Timestep: 6910000 mean reward (100 episodes): 6300.7200 best mean reward: 6313.8600 current episode reward: 6462.0000 episodes: 3290 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30395.6 seconds (8.44 hours) Timestep: 6920000 mean reward (100 episodes): 6293.3800 best mean reward: 6313.8600 current episode reward: 7488.0000 episodes: 3292 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30441.5 seconds (8.46 hours) Timestep: 6930000 mean reward (100 episodes): 6285.0800 best mean reward: 6313.8600 current episode reward: 4170.0000 episodes: 3294 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30486.4 seconds (8.47 hours) Timestep: 6940000 mean reward (100 episodes): 6309.6200 best mean reward: 6313.8600 current episode reward: 5308.0000 episodes: 3296 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30531.7 seconds (8.48 hours) Timestep: 6950000 mean reward (100 episodes): 6343.0800 best mean reward: 6343.0800 current episode reward: 6176.0000 episodes: 3298 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30577.3 seconds (8.49 hours) Timestep: 6960000 mean reward (100 episodes): 6327.5200 best mean reward: 6343.0800 current episode reward: 4092.0000 episodes: 3300 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30623.4 seconds (8.51 hours) Timestep: 6970000 mean reward (100 episodes): 6335.1200 best mean reward: 6343.0800 current episode reward: 7492.0000 episodes: 3301 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30668.7 seconds (8.52 hours) Timestep: 6980000 mean reward (100 episodes): 6304.6600 best mean reward: 6343.0800 current episode reward: 6262.0000 episodes: 3303 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30714.7 seconds (8.53 hours) Timestep: 6990000 mean reward (100 episodes): 6306.3000 best mean reward: 6343.0800 current episode reward: 5248.0000 episodes: 3306 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30760.8 seconds (8.54 hours) Timestep: 7000000 mean reward (100 episodes): 6266.4200 best mean reward: 6343.0800 current episode reward: 4220.0000 episodes: 3308 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30806.1 seconds (8.56 hours) Timestep: 7010000 mean reward (100 episodes): 6220.6400 best mean reward: 6343.0800 current episode reward: 5608.0000 episodes: 3310 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30851.2 seconds (8.57 hours) Timestep: 7020000 mean reward (100 episodes): 6216.2200 best mean reward: 6343.0800 current episode reward: 5696.0000 episodes: 3311 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30896.7 seconds (8.58 hours) Timestep: 7030000 mean reward (100 episodes): 6166.2000 best mean reward: 6343.0800 current episode reward: 3000.0000 episodes: 3314 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30943.5 seconds (8.60 hours) Timestep: 7040000 mean reward (100 episodes): 6112.6200 best mean reward: 6343.0800 current episode reward: 1380.0000 episodes: 3316 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30989.4 seconds (8.61 hours) Timestep: 7050000 mean reward (100 episodes): 6150.1000 best mean reward: 6343.0800 current episode reward: 7314.0000 episodes: 3318 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31035.1 seconds (8.62 hours) Timestep: 7060000 mean reward (100 episodes): 6121.9800 best mean reward: 6343.0800 current episode reward: 4092.0000 episodes: 3320 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31081.0 seconds (8.63 hours) Timestep: 7070000 mean reward (100 episodes): 6129.6000 best mean reward: 6343.0800 current episode reward: 8340.0000 episodes: 3322 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31127.2 seconds (8.65 hours) Timestep: 7080000 mean reward (100 episodes): 6107.1600 best mean reward: 6343.0800 current episode reward: 6894.0000 episodes: 3324 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31173.3 seconds (8.66 hours) Timestep: 7090000 mean reward (100 episodes): 6112.9600 best mean reward: 6343.0800 current episode reward: 7050.0000 episodes: 3325 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31218.0 seconds (8.67 hours) Timestep: 7100000 mean reward (100 episodes): 6162.5800 best mean reward: 6343.0800 current episode reward: 5410.0000 episodes: 3327 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31263.3 seconds (8.68 hours) Timestep: 7110000 mean reward (100 episodes): 6156.2800 best mean reward: 6343.0800 current episode reward: 4290.0000 episodes: 3330 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31308.5 seconds (8.70 hours) Timestep: 7120000 mean reward (100 episodes): 6162.7600 best mean reward: 6343.0800 current episode reward: 6948.0000 episodes: 3331 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31353.7 seconds (8.71 hours) Timestep: 7130000 mean reward (100 episodes): 6145.8800 best mean reward: 6343.0800 current episode reward: 6240.0000 episodes: 3334 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31398.7 seconds (8.72 hours) Timestep: 7140000 mean reward (100 episodes): 6026.4200 best mean reward: 6343.0800 current episode reward: 4110.0000 episodes: 3337 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31444.1 seconds (8.73 hours) Timestep: 7150000 mean reward (100 episodes): 6058.6600 best mean reward: 6343.0800 current episode reward: 6224.0000 episodes: 3338 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31489.4 seconds (8.75 hours) Timestep: 7160000 mean reward (100 episodes): 6042.1800 best mean reward: 6343.0800 current episode reward: 7776.0000 episodes: 3340 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31535.1 seconds (8.76 hours) Timestep: 7170000 mean reward (100 episodes): 6050.5400 best mean reward: 6343.0800 current episode reward: 5280.0000 episodes: 3342 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31580.5 seconds (8.77 hours) Timestep: 7180000 mean reward (100 episodes): 6049.8000 best mean reward: 6343.0800 current episode reward: 6892.0000 episodes: 3344 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31625.1 seconds (8.78 hours) Timestep: 7190000 mean reward (100 episodes): 6080.7000 best mean reward: 6343.0800 current episode reward: 9220.0000 episodes: 3345 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31671.2 seconds (8.80 hours) Timestep: 7200000 mean reward (100 episodes): 6048.0800 best mean reward: 6343.0800 current episode reward: 4860.0000 episodes: 3347 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31717.1 seconds (8.81 hours) Timestep: 7210000 mean reward (100 episodes): 6019.4200 best mean reward: 6343.0800 current episode reward: 3570.0000 episodes: 3349 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31762.5 seconds (8.82 hours) Timestep: 7220000 mean reward (100 episodes): 6068.6200 best mean reward: 6343.0800 current episode reward: 11358.0000 episodes: 3350 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31808.1 seconds (8.84 hours) Timestep: 7230000 mean reward (100 episodes): 6101.6600 best mean reward: 6343.0800 current episode reward: 7068.0000 episodes: 3352 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31854.1 seconds (8.85 hours) Timestep: 7240000 mean reward (100 episodes): 6099.0200 best mean reward: 6343.0800 current episode reward: 6478.0000 episodes: 3353 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31900.3 seconds (8.86 hours) Timestep: 7250000 mean reward (100 episodes): 6088.3200 best mean reward: 6343.0800 current episode reward: 7476.0000 episodes: 3355 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31945.9 seconds (8.87 hours) Timestep: 7260000 mean reward (100 episodes): 6115.4200 best mean reward: 6343.0800 current episode reward: 6168.0000 episodes: 3357 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31991.5 seconds (8.89 hours) Timestep: 7270000 mean reward (100 episodes): 6095.8800 best mean reward: 6343.0800 current episode reward: 7986.0000 episodes: 3358 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32036.8 seconds (8.90 hours) Timestep: 7280000 mean reward (100 episodes): 6129.3200 best mean reward: 6343.0800 current episode reward: 7818.0000 episodes: 3360 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32082.2 seconds (8.91 hours) Timestep: 7290000 mean reward (100 episodes): 6131.5400 best mean reward: 6343.0800 current episode reward: 7224.0000 episodes: 3362 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32127.6 seconds (8.92 hours) Timestep: 7300000 mean reward (100 episodes): 6132.0400 best mean reward: 6343.0800 current episode reward: 4898.0000 episodes: 3364 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32172.7 seconds (8.94 hours) Timestep: 7310000 mean reward (100 episodes): 6135.1200 best mean reward: 6343.0800 current episode reward: 7050.0000 episodes: 3365 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32218.9 seconds (8.95 hours) Timestep: 7320000 mean reward (100 episodes): 6124.6800 best mean reward: 6343.0800 current episode reward: 6894.0000 episodes: 3367 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32264.6 seconds (8.96 hours) Timestep: 7330000 mean reward (100 episodes): 6156.9200 best mean reward: 6343.0800 current episode reward: 9044.0000 episodes: 3368 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32309.9 seconds (8.97 hours) Timestep: 7340000 mean reward (100 episodes): 6184.6800 best mean reward: 6343.0800 current episode reward: 10040.0000 episodes: 3369 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32355.9 seconds (8.99 hours) Timestep: 7350000 mean reward (100 episodes): 6163.9800 best mean reward: 6343.0800 current episode reward: 6100.0000 episodes: 3371 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32401.7 seconds (9.00 hours) Timestep: 7360000 mean reward (100 episodes): 6122.5200 best mean reward: 6343.0800 current episode reward: 5182.0000 episodes: 3373 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32446.9 seconds (9.01 hours) Timestep: 7370000 mean reward (100 episodes): 6108.2000 best mean reward: 6343.0800 current episode reward: 4414.0000 episodes: 3375 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32492.0 seconds (9.03 hours) Timestep: 7380000 mean reward (100 episodes): 6103.1200 best mean reward: 6343.0800 current episode reward: 6978.0000 episodes: 3376 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32537.5 seconds (9.04 hours) Timestep: 7390000 mean reward (100 episodes): 6101.5400 best mean reward: 6343.0800 current episode reward: 6262.0000 episodes: 3378 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32582.7 seconds (9.05 hours) Timestep: 7400000 mean reward (100 episodes): 6111.7000 best mean reward: 6343.0800 current episode reward: 8820.0000 episodes: 3380 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32627.9 seconds (9.06 hours) Timestep: 7410000 mean reward (100 episodes): 6071.5400 best mean reward: 6343.0800 current episode reward: 4092.0000 episodes: 3381 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32673.2 seconds (9.08 hours) Timestep: 7420000 mean reward (100 episodes): 6115.5200 best mean reward: 6343.0800 current episode reward: 9288.0000 episodes: 3383 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32718.8 seconds (9.09 hours) Timestep: 7430000 mean reward (100 episodes): 6091.2800 best mean reward: 6343.0800 current episode reward: 6704.0000 episodes: 3385 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32763.9 seconds (9.10 hours) Timestep: 7440000 mean reward (100 episodes): 6105.0200 best mean reward: 6343.0800 current episode reward: 8346.0000 episodes: 3387 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32810.0 seconds (9.11 hours) Timestep: 7450000 mean reward (100 episodes): 6100.5200 best mean reward: 6343.0800 current episode reward: 4962.0000 episodes: 3389 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32855.5 seconds (9.13 hours) Timestep: 7460000 mean reward (100 episodes): 6077.9800 best mean reward: 6343.0800 current episode reward: 5310.0000 episodes: 3391 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32901.0 seconds (9.14 hours) Timestep: 7470000 mean reward (100 episodes): 6085.5800 best mean reward: 6343.0800 current episode reward: 6330.0000 episodes: 3393 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32946.6 seconds (9.15 hours) Timestep: 7480000 mean reward (100 episodes): 6095.7000 best mean reward: 6343.0800 current episode reward: 5182.0000 episodes: 3394 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32992.4 seconds (9.16 hours) Timestep: 7490000 mean reward (100 episodes): 6056.1800 best mean reward: 6343.0800 current episode reward: 2496.0000 episodes: 3397 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33037.7 seconds (9.18 hours) Timestep: 7500000 mean reward (100 episodes): 6059.7600 best mean reward: 6343.0800 current episode reward: 6534.0000 episodes: 3398 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33083.2 seconds (9.19 hours) Timestep: 7510000 mean reward (100 episodes): 6125.6800 best mean reward: 6343.0800 current episode reward: 7200.0000 episodes: 3400 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33129.4 seconds (9.20 hours) Timestep: 7520000 mean reward (100 episodes): 6098.1000 best mean reward: 6343.0800 current episode reward: 4734.0000 episodes: 3401 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33174.6 seconds (9.22 hours) Timestep: 7530000 mean reward (100 episodes): 6119.7200 best mean reward: 6343.0800 current episode reward: 1380.0000 episodes: 3403 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33219.7 seconds (9.23 hours) Timestep: 7540000 mean reward (100 episodes): 6199.3600 best mean reward: 6343.0800 current episode reward: 10610.0000 episodes: 3405 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33265.4 seconds (9.24 hours) Timestep: 7550000 mean reward (100 episodes): 6228.5400 best mean reward: 6343.0800 current episode reward: 8166.0000 episodes: 3406 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33310.8 seconds (9.25 hours) Timestep: 7560000 mean reward (100 episodes): 6254.4800 best mean reward: 6343.0800 current episode reward: 8940.0000 episodes: 3407 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33356.0 seconds (9.27 hours) Timestep: 7570000 mean reward (100 episodes): 6310.2400 best mean reward: 6343.0800 current episode reward: 5054.0000 episodes: 3409 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33401.0 seconds (9.28 hours) Timestep: 7580000 mean reward (100 episodes): 6340.7200 best mean reward: 6350.0400 current episode reward: 4764.0000 episodes: 3411 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33446.6 seconds (9.29 hours) Timestep: 7590000 mean reward (100 episodes): 6323.7800 best mean reward: 6350.0400 current episode reward: 7416.0000 episodes: 3413 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33492.2 seconds (9.30 hours) Timestep: 7600000 mean reward (100 episodes): 6397.2800 best mean reward: 6397.2800 current episode reward: 10350.0000 episodes: 3414 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33537.4 seconds (9.32 hours) Timestep: 7610000 mean reward (100 episodes): 6459.5600 best mean reward: 6459.5600 current episode reward: 6618.0000 episodes: 3416 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33583.3 seconds (9.33 hours) Timestep: 7620000 mean reward (100 episodes): 6463.9400 best mean reward: 6463.9400 current episode reward: 8022.0000 episodes: 3418 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33628.7 seconds (9.34 hours) Timestep: 7630000 mean reward (100 episodes): 6491.8400 best mean reward: 6491.8400 current episode reward: 7652.0000 episodes: 3419 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33673.4 seconds (9.35 hours) Timestep: 7640000 mean reward (100 episodes): 6542.7200 best mean reward: 6542.7200 current episode reward: 5880.0000 episodes: 3421 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33718.9 seconds (9.37 hours) Timestep: 7650000 mean reward (100 episodes): 6558.9200 best mean reward: 6558.9200 current episode reward: 9960.0000 episodes: 3422 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33764.8 seconds (9.38 hours) Timestep: 7660000 mean reward (100 episodes): 6529.7800 best mean reward: 6558.9200 current episode reward: 4220.0000 episodes: 3424 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33810.0 seconds (9.39 hours) Timestep: 7670000 mean reward (100 episodes): 6526.6200 best mean reward: 6558.9200 current episode reward: 7320.0000 episodes: 3426 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33855.3 seconds (9.40 hours) Timestep: 7680000 mean reward (100 episodes): 6507.6600 best mean reward: 6558.9200 current episode reward: 5340.0000 episodes: 3428 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33901.3 seconds (9.42 hours) Timestep: 7690000 mean reward (100 episodes): 6588.5600 best mean reward: 6588.5600 current episode reward: 12696.0000 episodes: 3429 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33947.1 seconds (9.43 hours) Timestep: 7700000 mean reward (100 episodes): 6592.8400 best mean reward: 6603.5200 current episode reward: 5880.0000 episodes: 3431 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33992.7 seconds (9.44 hours) Timestep: 7710000 mean reward (100 episodes): 6661.0000 best mean reward: 6661.0000 current episode reward: 8736.0000 episodes: 3433 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34037.6 seconds (9.45 hours) Timestep: 7720000 mean reward (100 episodes): 6691.4200 best mean reward: 6691.4200 current episode reward: 9282.0000 episodes: 3434 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34082.7 seconds (9.47 hours) Timestep: 7730000 mean reward (100 episodes): 6849.4800 best mean reward: 6849.4800 current episode reward: 5280.0000 episodes: 3436 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34128.2 seconds (9.48 hours) Timestep: 7740000 mean reward (100 episodes): 6954.3200 best mean reward: 6954.3200 current episode reward: 14594.0000 episodes: 3437 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34174.6 seconds (9.49 hours) Timestep: 7750000 mean reward (100 episodes): 6974.6800 best mean reward: 6974.6800 current episode reward: 8260.0000 episodes: 3438 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34219.7 seconds (9.51 hours) Timestep: 7760000 mean reward (100 episodes): 6951.8000 best mean reward: 6995.3600 current episode reward: 3420.0000 episodes: 3440 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34265.7 seconds (9.52 hours) Timestep: 7770000 mean reward (100 episodes): 6974.2200 best mean reward: 6995.3600 current episode reward: 5676.0000 episodes: 3442 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34311.5 seconds (9.53 hours) Timestep: 7780000 mean reward (100 episodes): 7023.2800 best mean reward: 7023.2800 current episode reward: 12068.0000 episodes: 3443 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34357.4 seconds (9.54 hours) Timestep: 7790000 mean reward (100 episodes): 7006.1200 best mean reward: 7025.4800 current episode reward: 7284.0000 episodes: 3445 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34403.0 seconds (9.56 hours) Timestep: 7800000 mean reward (100 episodes): 7035.0400 best mean reward: 7035.0400 current episode reward: 9636.0000 episodes: 3446 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34449.2 seconds (9.57 hours) Timestep: 7810000 mean reward (100 episodes): 7075.2400 best mean reward: 7075.2400 current episode reward: 4860.0000 episodes: 3448 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34495.1 seconds (9.58 hours) Timestep: 7820000 mean reward (100 episodes): 7103.4400 best mean reward: 7103.4400 current episode reward: 6390.0000 episodes: 3449 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34540.5 seconds (9.59 hours) Timestep: 7830000 mean reward (100 episodes): 7030.6400 best mean reward: 7103.4400 current episode reward: 6028.0000 episodes: 3451 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34586.5 seconds (9.61 hours) Timestep: 7840000 mean reward (100 episodes): 7002.7400 best mean reward: 7103.4400 current episode reward: 4156.0000 episodes: 3453 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34632.4 seconds (9.62 hours) Timestep: 7850000 mean reward (100 episodes): 6999.3200 best mean reward: 7103.4400 current episode reward: 6888.0000 episodes: 3455 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34677.8 seconds (9.63 hours) Timestep: 7860000 mean reward (100 episodes): 6959.1800 best mean reward: 7103.4400 current episode reward: 4796.0000 episodes: 3456 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34723.8 seconds (9.65 hours) Timestep: 7870000 mean reward (100 episodes): 7022.2200 best mean reward: 7103.4400 current episode reward: 12472.0000 episodes: 3457 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34770.0 seconds (9.66 hours) Timestep: 7880000 mean reward (100 episodes): 7021.0000 best mean reward: 7103.4400 current episode reward: 8164.0000 episodes: 3459 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34815.1 seconds (9.67 hours) Timestep: 7890000 mean reward (100 episodes): 6966.1000 best mean reward: 7103.4400 current episode reward: 4956.0000 episodes: 3461 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34861.1 seconds (9.68 hours) Timestep: 7900000 mean reward (100 episodes): 6958.3400 best mean reward: 7103.4400 current episode reward: 5182.0000 episodes: 3463 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34907.2 seconds (9.70 hours) Timestep: 7910000 mean reward (100 episodes): 6937.3200 best mean reward: 7103.4400 current episode reward: 2664.0000 episodes: 3465 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34952.5 seconds (9.71 hours) Timestep: 7920000 mean reward (100 episodes): 6958.6200 best mean reward: 7103.4400 current episode reward: 9720.0000 episodes: 3466 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34997.6 seconds (9.72 hours) Timestep: 7930000 mean reward (100 episodes): 6960.2400 best mean reward: 7103.4400 current episode reward: 6390.0000 episodes: 3468 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35042.9 seconds (9.73 hours) Timestep: 7940000 mean reward (100 episodes): 6908.3800 best mean reward: 7103.4400 current episode reward: 4860.0000 episodes: 3470 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35088.8 seconds (9.75 hours) Timestep: 7950000 mean reward (100 episodes): 6901.4200 best mean reward: 7103.4400 current episode reward: 5404.0000 episodes: 3471 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35135.0 seconds (9.76 hours) Timestep: 7960000 mean reward (100 episodes): 6932.2600 best mean reward: 7103.4400 current episode reward: 7112.0000 episodes: 3472 exploration: 0.01000 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6964.7600 best mean reward: 7103.4400 current episode reward: 7212.0000 episodes: 3481 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35407.3 seconds (9.84 hours) Timestep: 8020000 mean reward (100 episodes): 6915.3800 best mean reward: 7103.4400 current episode reward: 5200.0000 episodes: 3483 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35452.5 seconds (9.85 hours) Timestep: 8030000 mean reward (100 episodes): 6996.4200 best mean reward: 7103.4400 current episode reward: 9030.0000 episodes: 3485 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35497.6 seconds (9.86 hours) Timestep: 8040000 mean reward (100 episodes): 6915.2600 best mean reward: 7103.4400 current episode reward: 6840.0000 episodes: 3488 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35542.8 seconds (9.87 hours) Timestep: 8050000 mean reward (100 episodes): 6974.3600 best mean reward: 7103.4400 current episode reward: 10872.0000 episodes: 3489 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35588.8 seconds (9.89 hours) Timestep: 8060000 mean reward (100 episodes): 7019.4000 best mean reward: 7103.4400 current episode reward: 8916.0000 episodes: 3490 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35634.5 seconds (9.90 hours) Timestep: 8070000 mean reward (100 episodes): 7054.7800 best mean reward: 7103.4400 current episode reward: 6508.0000 episodes: 3492 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35679.9 seconds (9.91 hours) Timestep: 8080000 mean reward (100 episodes): 7105.6800 best mean reward: 7105.6800 current episode reward: 6732.0000 episodes: 3494 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35725.5 seconds (9.92 hours) Timestep: 8090000 mean reward (100 episodes): 7083.5000 best mean reward: 7105.6800 current episode reward: 5312.0000 episodes: 3495 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35771.4 seconds (9.94 hours) Timestep: 8100000 mean reward (100 episodes): 7163.2400 best mean reward: 7163.2400 current episode reward: 5216.0000 episodes: 3497 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35816.4 seconds (9.95 hours) Timestep: 8110000 mean reward (100 episodes): 7096.8400 best mean reward: 7163.2400 current episode reward: 4348.0000 episodes: 3499 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35861.4 seconds (9.96 hours) Timestep: 8120000 mean reward (100 episodes): 7094.2000 best mean reward: 7163.2400 current episode reward: 6936.0000 episodes: 3500 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35907.7 seconds (9.97 hours) Timestep: 8130000 mean reward (100 episodes): 7094.2200 best mean reward: 7163.2400 current episode reward: 4700.0000 episodes: 3502 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35953.1 seconds (9.99 hours) Timestep: 8140000 mean reward (100 episodes): 7153.0200 best mean reward: 7163.2400 current episode reward: 6996.0000 episodes: 3504 exploration: 0.01000 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7068.1200 best mean reward: 7163.2400 current episode reward: 6024.0000 episodes: 3513 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36223.9 seconds (10.06 hours) Timestep: 8200000 mean reward (100 episodes): 7030.7200 best mean reward: 7163.2400 current episode reward: 6610.0000 episodes: 3514 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36269.1 seconds (10.07 hours) Timestep: 8210000 mean reward (100 episodes): 7046.7000 best mean reward: 7163.2400 current episode reward: 8024.0000 episodes: 3516 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36314.9 seconds (10.09 hours) Timestep: 8220000 mean reward (100 episodes): 7094.1000 best mean reward: 7163.2400 current episode reward: 12030.0000 episodes: 3517 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36361.1 seconds (10.10 hours) Timestep: 8230000 mean reward (100 episodes): 7100.5200 best mean reward: 7163.2400 current episode reward: 7476.0000 episodes: 3519 exploration: 0.01000 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7198.9800 best mean reward: 7198.9800 current episode reward: 9780.0000 episodes: 3526 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36634.9 seconds (10.18 hours) Timestep: 8290000 mean reward (100 episodes): 7277.0400 best mean reward: 7277.0400 current episode reward: 7486.0000 episodes: 3528 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36680.6 seconds (10.19 hours) Timestep: 8300000 mean reward (100 episodes): 7261.7800 best mean reward: 7277.0400 current episode reward: 11170.0000 episodes: 3529 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36726.3 seconds (10.20 hours) Timestep: 8310000 mean reward (100 episodes): 7302.7200 best mean reward: 7302.7200 current episode reward: 9880.0000 episodes: 3530 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36771.8 seconds (10.21 hours) Timestep: 8320000 mean reward (100 episodes): 7398.3400 best mean reward: 7398.8800 current episode reward: 6186.0000 episodes: 3532 exploration: 0.01000 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7381.7800 best mean reward: 7398.8800 current episode reward: 5934.0000 episodes: 3540 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37045.0 seconds (10.29 hours) Timestep: 8380000 mean reward (100 episodes): 7415.6800 best mean reward: 7415.6800 current episode reward: 10368.0000 episodes: 3541 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37090.8 seconds (10.30 hours) Timestep: 8390000 mean reward (100 episodes): 7442.9800 best mean reward: 7442.9800 current episode reward: 8406.0000 episodes: 3542 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37135.6 seconds (10.32 hours) Timestep: 8400000 mean reward (100 episodes): 7419.6000 best mean reward: 7442.9800 current episode reward: 7492.0000 episodes: 3544 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37180.4 seconds (10.33 hours) Timestep: 8410000 mean reward (100 episodes): 7376.1000 best mean reward: 7442.9800 current episode reward: 5790.0000 episodes: 3546 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37225.8 seconds (10.34 hours) Timestep: 8420000 mean reward (100 episodes): 7393.1000 best mean reward: 7442.9800 current episode reward: 8316.0000 episodes: 3548 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37271.4 seconds (10.35 hours) Timestep: 8430000 mean reward (100 episodes): 7406.9600 best mean reward: 7442.9800 current episode reward: 7776.0000 episodes: 3549 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37317.4 seconds (10.37 hours) Timestep: 8440000 mean reward (100 episodes): 7365.9400 best mean reward: 7442.9800 current episode reward: 1588.0000 episodes: 3551 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37362.7 seconds (10.38 hours) Timestep: 8450000 mean reward (100 episodes): 7433.2800 best mean reward: 7442.9800 current episode reward: 4350.0000 episodes: 3553 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37408.4 seconds (10.39 hours) Timestep: 8460000 mean reward (100 episodes): 7413.2600 best mean reward: 7442.9800 current episode reward: 4320.0000 episodes: 3555 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37454.0 seconds (10.40 hours) Timestep: 8470000 mean reward (100 episodes): 7374.3600 best mean reward: 7442.9800 current episode reward: 6644.0000 episodes: 3557 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37499.0 seconds (10.42 hours) Timestep: 8480000 mean reward (100 episodes): 7349.8200 best mean reward: 7442.9800 current episode reward: 9468.0000 episodes: 3559 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37544.2 seconds (10.43 hours) Timestep: 8490000 mean reward (100 episodes): 7425.0600 best mean reward: 7442.9800 current episode reward: 11424.0000 episodes: 3560 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37590.2 seconds (10.44 hours) Timestep: 8500000 mean reward (100 episodes): 7486.3800 best mean reward: 7486.3800 current episode reward: 11088.0000 episodes: 3561 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37636.0 seconds (10.45 hours) Timestep: 8510000 mean reward (100 episodes): 7485.8600 best mean reward: 7486.3800 current episode reward: 6720.0000 episodes: 3563 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37681.1 seconds (10.47 hours) Timestep: 8520000 mean reward (100 episodes): 7509.4000 best mean reward: 7509.4000 current episode reward: 2888.0000 episodes: 3565 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37727.1 seconds (10.48 hours) Timestep: 8530000 mean reward (100 episodes): 7422.4600 best mean reward: 7509.4000 current episode reward: 6194.0000 episodes: 3567 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37772.7 seconds (10.49 hours) Timestep: 8540000 mean reward (100 episodes): 7425.5200 best mean reward: 7509.4000 current episode reward: 6606.0000 episodes: 3569 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37817.4 seconds (10.50 hours) Timestep: 8550000 mean reward (100 episodes): 7442.3000 best mean reward: 7509.4000 current episode reward: 6538.0000 episodes: 3570 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37862.9 seconds (10.52 hours) Timestep: 8560000 mean reward (100 episodes): 7463.7200 best mean reward: 7509.4000 current episode reward: 7866.0000 episodes: 3572 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37908.5 seconds (10.53 hours) Timestep: 8570000 mean reward (100 episodes): 7422.4200 best mean reward: 7509.4000 current episode reward: 7860.0000 episodes: 3574 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37954.0 seconds (10.54 hours) Timestep: 8580000 mean reward (100 episodes): 7443.1200 best mean reward: 7509.4000 current episode reward: 7950.0000 episodes: 3575 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38000.1 seconds (10.56 hours) Timestep: 8590000 mean reward (100 episodes): 7456.2000 best mean reward: 7509.4000 current episode reward: 6456.0000 episodes: 3576 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38045.8 seconds (10.57 hours) Timestep: 8600000 mean reward (100 episodes): 7551.1200 best mean reward: 7551.1200 current episode reward: 10130.0000 episodes: 3578 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38091.7 seconds (10.58 hours) Timestep: 8610000 mean reward (100 episodes): 7571.5000 best mean reward: 7571.5000 current episode reward: 9580.0000 episodes: 3579 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38137.2 seconds (10.59 hours) Timestep: 8620000 mean reward (100 episodes): 7560.2000 best mean reward: 7589.4800 current episode reward: 4284.0000 episodes: 3581 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38183.0 seconds (10.61 hours) Timestep: 8630000 mean reward (100 episodes): 7594.8400 best mean reward: 7594.8400 current episode reward: 9320.0000 episodes: 3583 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38228.4 seconds (10.62 hours) Timestep: 8640000 mean reward (100 episodes): 7581.8200 best mean reward: 7594.8400 current episode reward: 6636.0000 episodes: 3584 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38274.4 seconds (10.63 hours) Timestep: 8650000 mean reward (100 episodes): 7563.2200 best mean reward: 7594.8400 current episode reward: 7170.0000 episodes: 3585 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38319.9 seconds (10.64 hours) Timestep: 8660000 mean reward (100 episodes): 7682.1800 best mean reward: 7682.1800 current episode reward: 9540.0000 episodes: 3587 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38365.0 seconds (10.66 hours) Timestep: 8670000 mean reward (100 episodes): 7680.5600 best mean reward: 7682.1800 current episode reward: 6678.0000 episodes: 3588 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38410.0 seconds (10.67 hours) Timestep: 8680000 mean reward (100 episodes): 7676.6200 best mean reward: 7682.1800 current episode reward: 9800.0000 episodes: 3590 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38455.6 seconds (10.68 hours) Timestep: 8690000 mean reward (100 episodes): 7617.7200 best mean reward: 7682.1800 current episode reward: 5268.0000 episodes: 3592 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38501.3 seconds (10.69 hours) Timestep: 8700000 mean reward (100 episodes): 7609.3200 best mean reward: 7682.1800 current episode reward: 8022.0000 episodes: 3594 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38546.3 seconds (10.71 hours) Timestep: 8710000 mean reward (100 episodes): 7619.3200 best mean reward: 7682.1800 current episode reward: 6312.0000 episodes: 3595 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38591.1 seconds (10.72 hours) Timestep: 8720000 mean reward (100 episodes): 7615.7800 best mean reward: 7682.1800 current episode reward: 10030.0000 episodes: 3597 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38637.8 seconds (10.73 hours) Timestep: 8730000 mean reward (100 episodes): 7628.4600 best mean reward: 7682.1800 current episode reward: 5744.0000 episodes: 3598 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38682.8 seconds (10.75 hours) Timestep: 8740000 mean reward (100 episodes): 7685.5400 best mean reward: 7685.5400 current episode reward: 10602.0000 episodes: 3600 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38727.6 seconds (10.76 hours) Timestep: 8750000 mean reward (100 episodes): 7669.9400 best mean reward: 7685.5400 current episode reward: 9360.0000 episodes: 3601 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38773.2 seconds (10.77 hours) Timestep: 8760000 mean reward (100 episodes): 7663.4000 best mean reward: 7685.5400 current episode reward: 7470.0000 episodes: 3603 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38818.7 seconds (10.78 hours) Timestep: 8770000 mean reward (100 episodes): 7653.0400 best mean reward: 7685.5400 current episode reward: 5896.0000 episodes: 3605 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38864.4 seconds (10.80 hours) Timestep: 8780000 mean reward (100 episodes): 7631.5000 best mean reward: 7685.5400 current episode reward: 5756.0000 episodes: 3607 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38909.4 seconds (10.81 hours) Timestep: 8790000 mean reward (100 episodes): 7647.5600 best mean reward: 7685.5400 current episode reward: 7224.0000 episodes: 3609 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38954.3 seconds (10.82 hours) Timestep: 8800000 mean reward (100 episodes): 7706.2200 best mean reward: 7706.2200 current episode reward: 15274.0000 episodes: 3610 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38998.7 seconds (10.83 hours) Timestep: 8810000 mean reward (100 episodes): 7704.7800 best mean reward: 7706.2200 current episode reward: 6232.0000 episodes: 3612 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39043.8 seconds (10.85 hours) Timestep: 8820000 mean reward (100 episodes): 7705.7000 best mean reward: 7719.0000 current episode reward: 5280.0000 episodes: 3614 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39089.6 seconds (10.86 hours) Timestep: 8830000 mean reward (100 episodes): 7702.6400 best mean reward: 7730.4000 current episode reward: 5248.0000 episodes: 3616 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39135.0 seconds (10.87 hours) Timestep: 8840000 mean reward (100 episodes): 7648.6200 best mean reward: 7730.4000 current episode reward: 6628.0000 episodes: 3617 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39180.6 seconds (10.88 hours) Timestep: 8850000 mean reward (100 episodes): 7697.4000 best mean reward: 7730.4000 current episode reward: 9464.0000 episodes: 3619 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39226.4 seconds (10.90 hours) Timestep: 8860000 mean reward (100 episodes): 7644.1800 best mean reward: 7730.4000 current episode reward: 5132.0000 episodes: 3621 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39271.0 seconds (10.91 hours) Timestep: 8870000 mean reward (100 episodes): 7662.2600 best mean reward: 7730.4000 current episode reward: 9840.0000 episodes: 3622 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39316.0 seconds (10.92 hours) Timestep: 8880000 mean reward (100 episodes): 7671.8800 best mean reward: 7730.4000 current episode reward: 10230.0000 episodes: 3624 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39360.6 seconds (10.93 hours) Timestep: 8890000 mean reward (100 episodes): 7640.9200 best mean reward: 7730.4000 current episode reward: 8362.0000 episodes: 3626 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39406.0 seconds (10.95 hours) Timestep: 8900000 mean reward (100 episodes): 7537.9600 best mean reward: 7730.4000 current episode reward: 4110.0000 episodes: 3628 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39451.2 seconds (10.96 hours) Timestep: 8910000 mean reward (100 episodes): 7482.0800 best mean reward: 7730.4000 current episode reward: 7704.0000 episodes: 3630 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39496.9 seconds (10.97 hours) Timestep: 8920000 mean reward (100 episodes): 7414.2400 best mean reward: 7730.4000 current episode reward: 7398.0000 episodes: 3632 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39542.8 seconds (10.98 hours) Timestep: 8930000 mean reward (100 episodes): 7410.1400 best mean reward: 7730.4000 current episode reward: 3600.0000 episodes: 3634 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39588.7 seconds (11.00 hours) Timestep: 8940000 mean reward (100 episodes): 7365.2600 best mean reward: 7730.4000 current episode reward: 5662.0000 episodes: 3635 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39634.6 seconds (11.01 hours) Timestep: 8950000 mean reward (100 episodes): 7342.8600 best mean reward: 7730.4000 current episode reward: 11300.0000 episodes: 3637 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39680.1 seconds (11.02 hours) Timestep: 8960000 mean reward (100 episodes): 7228.4000 best mean reward: 7730.4000 current episode reward: 2160.0000 episodes: 3639 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39725.7 seconds (11.03 hours) Timestep: 8970000 mean reward (100 episodes): 7366.3600 best mean reward: 7730.4000 current episode reward: 19730.0000 episodes: 3640 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39771.6 seconds (11.05 hours) Timestep: 8980000 mean reward (100 episodes): 7340.1400 best mean reward: 7730.4000 current episode reward: 6292.0000 episodes: 3642 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39817.6 seconds (11.06 hours) Timestep: 8990000 mean reward (100 episodes): 7334.9000 best mean reward: 7730.4000 current episode reward: 8826.0000 episodes: 3643 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39864.0 seconds (11.07 hours) Timestep: 9000000 mean reward (100 episodes): 7314.0000 best mean reward: 7730.4000 current episode reward: 6516.0000 episodes: 3645 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39909.7 seconds (11.09 hours) Timestep: 9010000 mean reward (100 episodes): 7334.2800 best mean reward: 7730.4000 current episode reward: 7818.0000 episodes: 3646 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39955.6 seconds (11.10 hours) Timestep: 9020000 mean reward (100 episodes): 7381.6200 best mean reward: 7730.4000 current episode reward: 6266.0000 episodes: 3648 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40000.9 seconds (11.11 hours) Timestep: 9030000 mean reward (100 episodes): 7372.9200 best mean reward: 7730.4000 current episode reward: 6906.0000 episodes: 3649 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40045.5 seconds (11.12 hours) Timestep: 9040000 mean reward (100 episodes): 7498.3400 best mean reward: 7730.4000 current episode reward: 5540.0000 episodes: 3651 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40091.0 seconds (11.14 hours) Timestep: 9050000 mean reward (100 episodes): 7466.3200 best mean reward: 7730.4000 current episode reward: 8592.0000 episodes: 3653 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40136.4 seconds (11.15 hours) Timestep: 9060000 mean reward (100 episodes): 7475.4800 best mean reward: 7730.4000 current episode reward: 8764.0000 episodes: 3654 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40181.3 seconds (11.16 hours) Timestep: 9070000 mean reward (100 episodes): 7506.1600 best mean reward: 7730.4000 current episode reward: 5602.0000 episodes: 3656 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40227.1 seconds (11.17 hours) Timestep: 9080000 mean reward (100 episodes): 7605.4400 best mean reward: 7730.4000 current episode reward: 16572.0000 episodes: 3657 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40272.7 seconds (11.19 hours) Timestep: 9090000 mean reward (100 episodes): 7652.8400 best mean reward: 7730.4000 current episode reward: 8704.0000 episodes: 3658 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40318.0 seconds (11.20 hours) Timestep: 9100000 mean reward (100 episodes): 7578.5200 best mean reward: 7730.4000 current episode reward: 8178.0000 episodes: 3660 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40363.6 seconds (11.21 hours) Timestep: 9110000 mean reward (100 episodes): 7534.1600 best mean reward: 7730.4000 current episode reward: 6652.0000 episodes: 3661 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40409.3 seconds (11.22 hours) Timestep: 9120000 mean reward (100 episodes): 7642.4400 best mean reward: 7730.4000 current episode reward: 12000.0000 episodes: 3663 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40454.5 seconds (11.24 hours) Timestep: 9130000 mean reward (100 episodes): 7651.1600 best mean reward: 7730.4000 current episode reward: 6810.0000 episodes: 3665 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40499.7 seconds (11.25 hours) Timestep: 9140000 mean reward (100 episodes): 7687.2200 best mean reward: 7730.4000 current episode reward: 8148.0000 episodes: 3666 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40545.1 seconds (11.26 hours) Timestep: 9150000 mean reward (100 episodes): 7675.5000 best mean reward: 7730.4000 current episode reward: 7152.0000 episodes: 3668 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40590.3 seconds (11.28 hours) Timestep: 9160000 mean reward (100 episodes): 7722.7200 best mean reward: 7730.4000 current episode reward: 11328.0000 episodes: 3669 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40636.2 seconds (11.29 hours) Timestep: 9170000 mean reward (100 episodes): 7757.9600 best mean reward: 7773.7200 current episode reward: 5216.0000 episodes: 3671 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40682.1 seconds (11.30 hours) Timestep: 9180000 mean reward (100 episodes): 7747.5200 best mean reward: 7773.7200 current episode reward: 6822.0000 episodes: 3672 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40727.1 seconds (11.31 hours) Timestep: 9190000 mean reward (100 episodes): 7770.5600 best mean reward: 7773.7200 current episode reward: 7920.0000 episodes: 3674 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40771.9 seconds (11.33 hours) Timestep: 9200000 mean reward (100 episodes): 7771.0800 best mean reward: 7773.7200 current episode reward: 8002.0000 episodes: 3675 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40817.1 seconds (11.34 hours) Timestep: 9210000 mean reward (100 episodes): 7814.9800 best mean reward: 7825.8000 current episode reward: 9850.0000 episodes: 3677 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40862.6 seconds (11.35 hours) Timestep: 9220000 mean reward (100 episodes): 7807.2400 best mean reward: 7825.8000 current episode reward: 9356.0000 episodes: 3678 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40907.4 seconds (11.36 hours) Timestep: 9230000 mean reward (100 episodes): 7824.9000 best mean reward: 7825.8000 current episode reward: 11346.0000 episodes: 3679 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40953.6 seconds (11.38 hours) Timestep: 9240000 mean reward (100 episodes): 7845.0000 best mean reward: 7845.0000 current episode reward: 8832.0000 episodes: 3680 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40999.9 seconds (11.39 hours) Timestep: 9250000 mean reward (100 episodes): 7895.1200 best mean reward: 7915.2600 current episode reward: 5760.0000 episodes: 3682 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41046.6 seconds (11.40 hours) Timestep: 9260000 mean reward (100 episodes): 7824.3600 best mean reward: 7915.2600 current episode reward: 4020.0000 episodes: 3684 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41092.4 seconds (11.41 hours) Timestep: 9270000 mean reward (100 episodes): 7845.7200 best mean reward: 7915.2600 current episode reward: 6600.0000 episodes: 3686 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41138.2 seconds (11.43 hours) Timestep: 9280000 mean reward (100 episodes): 7814.7600 best mean reward: 7915.2600 current episode reward: 6444.0000 episodes: 3687 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41183.5 seconds (11.44 hours) Timestep: 9290000 mean reward (100 episodes): 7827.4000 best mean reward: 7915.2600 current episode reward: 7942.0000 episodes: 3688 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41229.6 seconds (11.45 hours) Timestep: 9300000 mean reward (100 episodes): 7892.1400 best mean reward: 7915.2600 current episode reward: 8208.0000 episodes: 3690 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41274.5 seconds (11.47 hours) Timestep: 9310000 mean reward (100 episodes): 7923.0200 best mean reward: 7923.0200 current episode reward: 6384.0000 episodes: 3692 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41319.9 seconds (11.48 hours) Timestep: 9320000 mean reward (100 episodes): 7915.9600 best mean reward: 7923.0200 current episode reward: 9266.0000 episodes: 3694 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41365.4 seconds (11.49 hours) Timestep: 9330000 mean reward (100 episodes): 7881.8400 best mean reward: 7923.0200 current episode reward: 2832.0000 episodes: 3696 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41412.1 seconds (11.50 hours) Timestep: 9340000 mean reward (100 episodes): 7834.6400 best mean reward: 7923.0200 current episode reward: 5310.0000 episodes: 3697 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41457.5 seconds (11.52 hours) Timestep: 9350000 mean reward (100 episodes): 7923.0000 best mean reward: 7923.6000 current episode reward: 6330.0000 episodes: 3699 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41503.5 seconds (11.53 hours) Timestep: 9360000 mean reward (100 episodes): 7876.2000 best mean reward: 7923.6000 current episode reward: 5922.0000 episodes: 3700 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41548.8 seconds (11.54 hours) Timestep: 9370000 mean reward (100 episodes): 7924.8000 best mean reward: 7924.8000 current episode reward: 9090.0000 episodes: 3702 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41594.1 seconds (11.55 hours) Timestep: 9380000 mean reward (100 episodes): 7923.9000 best mean reward: 7924.8000 current episode reward: 7380.0000 episodes: 3703 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41639.2 seconds (11.57 hours) Timestep: 9390000 mean reward (100 episodes): 7983.4400 best mean reward: 7983.4400 current episode reward: 7314.0000 episodes: 3705 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41685.6 seconds (11.58 hours) Timestep: 9400000 mean reward (100 episodes): 8004.8600 best mean reward: 8004.8600 current episode reward: 7260.0000 episodes: 3706 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41730.3 seconds (11.59 hours) Timestep: 9410000 mean reward (100 episodes): 7971.7000 best mean reward: 8027.8800 current episode reward: 7820.0000 episodes: 3709 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41775.8 seconds (11.60 hours) Timestep: 9420000 mean reward (100 episodes): 7971.7000 best mean reward: 8027.8800 current episode reward: 7820.0000 episodes: 3709 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41821.0 seconds (11.62 hours) Timestep: 9430000 mean reward (100 episodes): 8016.4400 best mean reward: 8027.8800 current episode reward: 12060.0000 episodes: 3711 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41866.2 seconds (11.63 hours) Timestep: 9440000 mean reward (100 episodes): 8087.1600 best mean reward: 8087.1600 current episode reward: 13304.0000 episodes: 3712 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41912.3 seconds (11.64 hours) Timestep: 9450000 mean reward (100 episodes): 8147.3000 best mean reward: 8147.3000 current episode reward: 13460.0000 episodes: 3713 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41958.5 seconds (11.66 hours) Timestep: 9460000 mean reward (100 episodes): 8186.5000 best mean reward: 8186.5000 current episode reward: 9200.0000 episodes: 3714 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42004.3 seconds (11.67 hours) Timestep: 9470000 mean reward (100 episodes): 8217.9600 best mean reward: 8217.9600 current episode reward: 11088.0000 episodes: 3715 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42049.9 seconds (11.68 hours) Timestep: 9480000 mean reward (100 episodes): 8218.5600 best mean reward: 8225.3000 current episode reward: 5954.0000 episodes: 3717 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42095.0 seconds (11.69 hours) Timestep: 9490000 mean reward (100 episodes): 8166.4400 best mean reward: 8225.3000 current episode reward: 5828.0000 episodes: 3719 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42140.7 seconds (11.71 hours) Timestep: 9500000 mean reward (100 episodes): 8189.6800 best mean reward: 8225.3000 current episode reward: 4156.0000 episodes: 3721 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42186.5 seconds (11.72 hours) Timestep: 9510000 mean reward (100 episodes): 8176.8600 best mean reward: 8225.3000 current episode reward: 8518.0000 episodes: 3723 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42232.6 seconds (11.73 hours) Timestep: 9520000 mean reward (100 episodes): 8190.6200 best mean reward: 8225.3000 current episode reward: 7488.0000 episodes: 3725 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42278.8 seconds (11.74 hours) Timestep: 9530000 mean reward (100 episodes): 8190.6200 best mean reward: 8225.3000 current episode reward: 7488.0000 episodes: 3725 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42325.0 seconds (11.76 hours) Timestep: 9540000 mean reward (100 episodes): 8311.3600 best mean reward: 8311.3600 current episode reward: 6096.0000 episodes: 3728 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42370.8 seconds (11.77 hours) Timestep: 9550000 mean reward (100 episodes): 8305.6600 best mean reward: 8311.3600 current episode reward: 7188.0000 episodes: 3729 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42416.3 seconds (11.78 hours) Timestep: 9560000 mean reward (100 episodes): 8392.5400 best mean reward: 8392.5400 current episode reward: 16392.0000 episodes: 3730 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42461.9 seconds (11.79 hours) Timestep: 9570000 mean reward (100 episodes): 8423.2000 best mean reward: 8424.9400 current episode reward: 7224.0000 episodes: 3732 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42507.3 seconds (11.81 hours) Timestep: 9580000 mean reward (100 episodes): 8081.9800 best mean reward: 8424.9400 current episode reward: 352.0000 episodes: 3737 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42552.6 seconds (11.82 hours) Timestep: 9590000 mean reward (100 episodes): 7657.0800 best mean reward: 8424.9400 current episode reward: 440.0000 episodes: 3742 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42596.8 seconds (11.83 hours) Timestep: 9600000 mean reward (100 episodes): 7534.4800 best mean reward: 8424.9400 current episode reward: 852.0000 episodes: 3744 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42642.6 seconds (11.85 hours) Timestep: 9610000 mean reward (100 episodes): 7410.4600 best mean reward: 8424.9400 current episode reward: 4542.0000 episodes: 3747 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42687.6 seconds (11.86 hours) Timestep: 9620000 mean reward (100 episodes): 7343.5400 best mean reward: 8424.9400 current episode reward: 4320.0000 episodes: 3749 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42733.5 seconds (11.87 hours) Timestep: 9630000 mean reward (100 episodes): 7293.9400 best mean reward: 8424.9400 current episode reward: 6028.0000 episodes: 3751 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42779.0 seconds (11.88 hours) Timestep: 9640000 mean reward (100 episodes): 7288.9000 best mean reward: 8424.9400 current episode reward: 8916.0000 episodes: 3753 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42824.7 seconds (11.90 hours) Timestep: 9650000 mean reward (100 episodes): 7285.4600 best mean reward: 8424.9400 current episode reward: 8420.0000 episodes: 3754 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42871.2 seconds (11.91 hours) Timestep: 9660000 mean reward (100 episodes): 7270.9200 best mean reward: 8424.9400 current episode reward: 5582.0000 episodes: 3756 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42915.7 seconds (11.92 hours) Timestep: 9670000 mean reward (100 episodes): 7134.9800 best mean reward: 8424.9400 current episode reward: 6720.0000 episodes: 3758 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42960.7 seconds (11.93 hours) Timestep: 9680000 mean reward (100 episodes): 7144.4000 best mean reward: 8424.9400 current episode reward: 5582.0000 episodes: 3760 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43005.7 seconds (11.95 hours) Timestep: 9690000 mean reward (100 episodes): 7096.2800 best mean reward: 8424.9400 current episode reward: 7476.0000 episodes: 3762 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43051.0 seconds (11.96 hours) Timestep: 9700000 mean reward (100 episodes): 7026.5800 best mean reward: 8424.9400 current episode reward: 5480.0000 episodes: 3764 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43096.8 seconds (11.97 hours) Timestep: 9710000 mean reward (100 episodes): 7015.0800 best mean reward: 8424.9400 current episode reward: 4668.0000 episodes: 3766 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43142.1 seconds (11.98 hours) Timestep: 9720000 mean reward (100 episodes): 7057.6800 best mean reward: 8424.9400 current episode reward: 11430.0000 episodes: 3767 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43188.0 seconds (12.00 hours) Timestep: 9730000 mean reward (100 episodes): 6987.9600 best mean reward: 8424.9400 current episode reward: 5216.0000 episodes: 3769 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43232.9 seconds (12.01 hours) Timestep: 9740000 mean reward (100 episodes): 6926.2600 best mean reward: 8424.9400 current episode reward: 3964.0000 episodes: 3771 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43278.8 seconds (12.02 hours) Timestep: 9750000 mean reward (100 episodes): 6814.2200 best mean reward: 8424.9400 current episode reward: 3420.0000 episodes: 3774 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43324.6 seconds (12.03 hours) Timestep: 9760000 mean reward (100 episodes): 6800.8000 best mean reward: 8424.9400 current episode reward: 6660.0000 episodes: 3775 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43370.5 seconds (12.05 hours) Timestep: 9770000 mean reward (100 episodes): 6732.9600 best mean reward: 8424.9400 current episode reward: 7080.0000 episodes: 3777 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43415.4 seconds (12.06 hours) Timestep: 9780000 mean reward (100 episodes): 6640.4200 best mean reward: 8424.9400 current episode reward: 4740.0000 episodes: 3779 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43461.2 seconds (12.07 hours) Timestep: 9790000 mean reward (100 episodes): 6625.9000 best mean reward: 8424.9400 current episode reward: 7380.0000 episodes: 3780 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43506.5 seconds (12.09 hours) Timestep: 9800000 mean reward (100 episodes): 6677.9000 best mean reward: 8424.9400 current episode reward: 9130.0000 episodes: 3782 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43552.4 seconds (12.10 hours) Timestep: 9810000 mean reward (100 episodes): 6818.3000 best mean reward: 8424.9400 current episode reward: 18900.0000 episodes: 3783 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43598.5 seconds (12.11 hours) Timestep: 9820000 mean reward (100 episodes): 6789.2800 best mean reward: 8424.9400 current episode reward: 4800.0000 episodes: 3785 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43643.1 seconds (12.12 hours) Timestep: 9830000 mean reward (100 episodes): 6788.0600 best mean reward: 8424.9400 current episode reward: 6478.0000 episodes: 3786 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43688.4 seconds (12.14 hours) Timestep: 9840000 mean reward (100 episodes): 6809.0600 best mean reward: 8424.9400 current episode reward: 5896.0000 episodes: 3788 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43734.1 seconds (12.15 hours) Timestep: 9850000 mean reward (100 episodes): 6650.8000 best mean reward: 8424.9400 current episode reward: 3750.0000 episodes: 3790 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43779.4 seconds (12.16 hours) Timestep: 9860000 mean reward (100 episodes): 6676.4400 best mean reward: 8424.9400 current episode reward: 9300.0000 episodes: 3792 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43824.3 seconds (12.17 hours) Timestep: 9870000 mean reward (100 episodes): 6702.9000 best mean reward: 8424.9400 current episode reward: 8436.0000 episodes: 3793 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43869.7 seconds (12.19 hours) Timestep: 9880000 mean reward (100 episodes): 6683.9800 best mean reward: 8424.9400 current episode reward: 5922.0000 episodes: 3795 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43914.7 seconds (12.20 hours) Timestep: 9890000 mean reward (100 episodes): 6698.5400 best mean reward: 8424.9400 current episode reward: 2776.0000 episodes: 3797 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43959.3 seconds (12.21 hours) Timestep: 9900000 mean reward (100 episodes): 6637.8800 best mean reward: 8424.9400 current episode reward: 6300.0000 episodes: 3799 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44004.5 seconds (12.22 hours) Timestep: 9910000 mean reward (100 episodes): 6644.5400 best mean reward: 8424.9400 current episode reward: 10430.0000 episodes: 3801 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44050.3 seconds (12.24 hours) Timestep: 9920000 mean reward (100 episodes): 6660.6400 best mean reward: 8424.9400 current episode reward: 10700.0000 episodes: 3802 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44095.8 seconds (12.25 hours) Timestep: 9930000 mean reward (100 episodes): 6614.2000 best mean reward: 8424.9400 current episode reward: 6720.0000 episodes: 3804 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44140.3 seconds (12.26 hours) Timestep: 9935208 mean reward (100 episodes): 6646.6600 best mean reward: 8424.9400 current episode reward: 10560.0000 episodes: 3805 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44163.9 seconds (12.27 hours) ================================================ FILE: dqn/logs_text/BeamRider_s002.text ================================================ ('AVAILABLE GPUS: ', [u'device: 0, name: TITAN X (Pascal), pci bus id: 0000:01:00.0']) task = Task Timestep: 60000 mean reward (100 episodes): 361.7778 best mean reward: -inf current episode reward: 220.0000 episodes: 45 exploration: 0.94600 learning_rate: 0.00010 elapsed time: 91.4 seconds (0.03 hours) Timestep: 70000 mean reward (100 episodes): 367.2308 best mean reward: -inf current episode reward: 220.0000 episodes: 52 exploration: 0.93700 learning_rate: 0.00010 elapsed time: 123.4 seconds (0.03 hours) Timestep: 80000 mean reward (100 episodes): 367.6610 best mean reward: -inf current episode reward: 352.0000 episodes: 59 exploration: 0.92800 learning_rate: 0.00010 elapsed time: 155.9 seconds (0.04 hours) Timestep: 90000 mean reward (100 episodes): 368.4179 best mean reward: -inf current episode reward: 440.0000 episodes: 67 exploration: 0.91900 learning_rate: 0.00010 elapsed time: 190.2 seconds (0.05 hours) Timestep: 100000 mean reward (100 episodes): 361.9733 best mean reward: -inf current episode reward: 176.0000 episodes: 75 exploration: 0.91000 learning_rate: 0.00010 elapsed time: 223.0 seconds (0.06 hours) Timestep: 110000 mean reward (100 episodes): 363.8049 best mean reward: -inf current episode reward: 440.0000 episodes: 82 exploration: 0.90100 learning_rate: 0.00010 elapsed time: 255.8 seconds (0.07 hours) Timestep: 120000 mean reward (100 episodes): 356.8352 best mean reward: -inf current episode reward: 308.0000 episodes: 91 exploration: 0.89200 learning_rate: 0.00010 elapsed time: 288.6 seconds (0.08 hours) Timestep: 130000 mean reward (100 episodes): 360.0000 best mean reward: -inf current episode reward: 132.0000 episodes: 99 exploration: 0.88300 learning_rate: 0.00010 elapsed time: 321.2 seconds (0.09 hours) Timestep: 140000 mean reward (100 episodes): 361.6800 best mean reward: 362.5600 current episode reward: 440.0000 episodes: 107 exploration: 0.87400 learning_rate: 0.00010 elapsed time: 354.1 seconds (0.10 hours) Timestep: 150000 mean reward (100 episodes): 370.4800 best mean reward: 371.3600 current episode reward: 132.0000 episodes: 114 exploration: 0.86500 learning_rate: 0.00010 elapsed time: 386.8 seconds (0.11 hours) Timestep: 160000 mean reward (100 episodes): 371.3600 best mean reward: 373.1200 current episode reward: 352.0000 episodes: 121 exploration: 0.85600 learning_rate: 0.00010 elapsed time: 419.9 seconds (0.12 hours) Timestep: 170000 mean reward (100 episodes): 374.8800 best mean reward: 374.8800 current episode reward: 308.0000 episodes: 127 exploration: 0.84700 learning_rate: 0.00010 elapsed time: 455.1 seconds (0.13 hours) Timestep: 180000 mean reward (100 episodes): 372.2400 best mean reward: 376.2000 current episode reward: 264.0000 episodes: 135 exploration: 0.83800 learning_rate: 0.00010 elapsed time: 488.8 seconds (0.14 hours) Timestep: 190000 mean reward (100 episodes): 367.8400 best mean reward: 376.2000 current episode reward: 308.0000 episodes: 144 exploration: 0.82900 learning_rate: 0.00010 elapsed time: 522.5 seconds (0.15 hours) Timestep: 200000 mean reward (100 episodes): 360.8000 best mean reward: 376.2000 current episode reward: 352.0000 episodes: 152 exploration: 0.82000 learning_rate: 0.00010 elapsed time: 556.6 seconds (0.15 hours) Timestep: 210000 mean reward (100 episodes): 367.4000 best mean reward: 376.2000 current episode reward: 352.0000 episodes: 159 exploration: 0.81100 learning_rate: 0.00010 elapsed time: 590.6 seconds (0.16 hours) Timestep: 220000 mean reward (100 episodes): 372.6800 best mean reward: 376.2000 current episode reward: 220.0000 episodes: 166 exploration: 0.80200 learning_rate: 0.00010 elapsed time: 624.8 seconds (0.17 hours) Timestep: 230000 mean reward (100 episodes): 385.0000 best mean reward: 385.0000 current episode reward: 616.0000 episodes: 172 exploration: 0.79300 learning_rate: 0.00010 elapsed time: 658.8 seconds (0.18 hours) Timestep: 240000 mean reward (100 episodes): 379.7200 best mean reward: 389.4000 current episode reward: 176.0000 episodes: 179 exploration: 0.78400 learning_rate: 0.00010 elapsed time: 692.9 seconds (0.19 hours) Timestep: 250000 mean reward (100 episodes): 389.8400 best mean reward: 389.8400 current episode reward: 352.0000 episodes: 186 exploration: 0.77500 learning_rate: 0.00010 elapsed time: 727.4 seconds (0.20 hours) Timestep: 260000 mean reward (100 episodes): 385.0000 best mean reward: 389.8400 current episode reward: 396.0000 episodes: 194 exploration: 0.76600 learning_rate: 0.00010 elapsed time: 762.2 seconds (0.21 hours) Timestep: 270000 mean reward (100 episodes): 384.1200 best mean reward: 389.8400 current episode reward: 396.0000 episodes: 203 exploration: 0.75700 learning_rate: 0.00010 elapsed time: 796.6 seconds (0.22 hours) Timestep: 280000 mean reward (100 episodes): 377.0800 best mean reward: 389.8400 current episode reward: 308.0000 episodes: 210 exploration: 0.74800 learning_rate: 0.00010 elapsed time: 831.3 seconds (0.23 hours) Timestep: 290000 mean reward (100 episodes): 375.3200 best mean reward: 389.8400 current episode reward: 308.0000 episodes: 217 exploration: 0.73900 learning_rate: 0.00010 elapsed time: 867.1 seconds (0.24 hours) Timestep: 300000 mean reward (100 episodes): 366.9600 best mean reward: 389.8400 current episode reward: 396.0000 episodes: 225 exploration: 0.73000 learning_rate: 0.00010 elapsed time: 903.4 seconds (0.25 hours) Timestep: 310000 mean reward (100 episodes): 363.8800 best mean reward: 389.8400 current episode reward: 440.0000 episodes: 232 exploration: 0.72100 learning_rate: 0.00010 elapsed time: 939.2 seconds (0.26 hours) Timestep: 320000 mean reward (100 episodes): 359.9200 best mean reward: 389.8400 current episode reward: 396.0000 episodes: 241 exploration: 0.71200 learning_rate: 0.00010 elapsed time: 975.0 seconds (0.27 hours) Timestep: 330000 mean reward (100 episodes): 365.6400 best mean reward: 389.8400 current episode reward: 440.0000 episodes: 248 exploration: 0.70300 learning_rate: 0.00010 elapsed time: 1011.1 seconds (0.28 hours) Timestep: 340000 mean reward (100 episodes): 360.3600 best mean reward: 389.8400 current episode reward: 132.0000 episodes: 255 exploration: 0.69400 learning_rate: 0.00010 elapsed time: 1047.7 seconds (0.29 hours) Timestep: 350000 mean reward (100 episodes): 345.8400 best mean reward: 389.8400 current episode reward: 484.0000 episodes: 264 exploration: 0.68500 learning_rate: 0.00010 elapsed time: 1083.8 seconds (0.30 hours) Timestep: 360000 mean reward (100 episodes): 340.5600 best mean reward: 389.8400 current episode reward: 440.0000 episodes: 270 exploration: 0.67600 learning_rate: 0.00010 elapsed time: 1120.4 seconds (0.31 hours) Timestep: 370000 mean reward (100 episodes): 333.9600 best mean reward: 389.8400 current episode reward: 484.0000 episodes: 277 exploration: 0.66700 learning_rate: 0.00010 elapsed time: 1156.4 seconds (0.32 hours) Timestep: 380000 mean reward (100 episodes): 335.3200 best mean reward: 389.8400 current episode reward: 708.0000 episodes: 283 exploration: 0.65800 learning_rate: 0.00010 elapsed time: 1193.4 seconds (0.33 hours) Timestep: 390000 mean reward (100 episodes): 346.3200 best mean reward: 389.8400 current episode reward: 660.0000 episodes: 290 exploration: 0.64900 learning_rate: 0.00010 elapsed time: 1229.9 seconds (0.34 hours) Timestep: 400000 mean reward (100 episodes): 349.4000 best mean reward: 389.8400 current episode reward: 396.0000 episodes: 297 exploration: 0.64000 learning_rate: 0.00010 elapsed time: 1266.6 seconds (0.35 hours) Timestep: 410000 mean reward (100 episodes): 341.4800 best mean reward: 389.8400 current episode reward: 484.0000 episodes: 304 exploration: 0.63100 learning_rate: 0.00010 elapsed time: 1303.1 seconds (0.36 hours) Timestep: 420000 mean reward (100 episodes): 341.9200 best mean reward: 389.8400 current episode reward: 308.0000 episodes: 311 exploration: 0.62200 learning_rate: 0.00010 elapsed time: 1340.2 seconds (0.37 hours) Timestep: 430000 mean reward (100 episodes): 345.4400 best mean reward: 389.8400 current episode reward: 440.0000 episodes: 319 exploration: 0.61300 learning_rate: 0.00010 elapsed time: 1377.9 seconds (0.38 hours) Timestep: 440000 mean reward (100 episodes): 344.5600 best mean reward: 389.8400 current episode reward: 528.0000 episodes: 326 exploration: 0.60400 learning_rate: 0.00010 elapsed time: 1415.2 seconds (0.39 hours) Timestep: 450000 mean reward (100 episodes): 353.8000 best mean reward: 389.8400 current episode reward: 308.0000 episodes: 333 exploration: 0.59500 learning_rate: 0.00010 elapsed time: 1452.9 seconds (0.40 hours) Timestep: 460000 mean reward (100 episodes): 355.5600 best mean reward: 389.8400 current episode reward: 132.0000 episodes: 340 exploration: 0.58600 learning_rate: 0.00010 elapsed time: 1490.4 seconds (0.41 hours) Timestep: 470000 mean reward (100 episodes): 349.8400 best mean reward: 389.8400 current episode reward: 264.0000 episodes: 347 exploration: 0.57700 learning_rate: 0.00010 elapsed time: 1529.3 seconds (0.42 hours) Timestep: 480000 mean reward (100 episodes): 352.9200 best mean reward: 389.8400 current episode reward: 572.0000 episodes: 356 exploration: 0.56800 learning_rate: 0.00010 elapsed time: 1567.3 seconds (0.44 hours) Timestep: 490000 mean reward (100 episodes): 356.0000 best mean reward: 389.8400 current episode reward: 220.0000 episodes: 363 exploration: 0.55900 learning_rate: 0.00010 elapsed time: 1606.0 seconds (0.45 hours) Timestep: 500000 mean reward (100 episodes): 359.0800 best mean reward: 389.8400 current episode reward: 660.0000 episodes: 370 exploration: 0.55000 learning_rate: 0.00010 elapsed time: 1644.7 seconds (0.46 hours) Timestep: 510000 mean reward (100 episodes): 360.8400 best mean reward: 389.8400 current episode reward: 308.0000 episodes: 376 exploration: 0.54100 learning_rate: 0.00010 elapsed time: 1683.6 seconds (0.47 hours) Timestep: 520000 mean reward (100 episodes): 362.1200 best mean reward: 389.8400 current episode reward: 528.0000 episodes: 383 exploration: 0.53200 learning_rate: 0.00010 elapsed time: 1721.9 seconds (0.48 hours) Timestep: 530000 mean reward (100 episodes): 342.3200 best mean reward: 389.8400 current episode reward: 308.0000 episodes: 391 exploration: 0.52300 learning_rate: 0.00010 elapsed time: 1760.1 seconds (0.49 hours) Timestep: 540000 mean reward (100 episodes): 348.9200 best mean reward: 389.8400 current episode reward: 308.0000 episodes: 398 exploration: 0.51400 learning_rate: 0.00010 elapsed time: 1798.1 seconds (0.50 hours) Timestep: 550000 mean reward (100 episodes): 355.0800 best mean reward: 389.8400 current episode reward: 396.0000 episodes: 404 exploration: 0.50500 learning_rate: 0.00010 elapsed time: 1836.8 seconds (0.51 hours) Timestep: 560000 mean reward (100 episodes): 352.8800 best mean reward: 389.8400 current episode reward: 308.0000 episodes: 411 exploration: 0.49600 learning_rate: 0.00010 elapsed time: 1875.2 seconds (0.52 hours) Timestep: 570000 mean reward (100 episodes): 348.0400 best mean reward: 389.8400 current episode reward: 176.0000 episodes: 417 exploration: 0.48700 learning_rate: 0.00010 elapsed time: 1914.3 seconds (0.53 hours) Timestep: 580000 mean reward (100 episodes): 355.9600 best mean reward: 389.8400 current episode reward: 176.0000 episodes: 422 exploration: 0.47800 learning_rate: 0.00010 elapsed time: 1953.6 seconds (0.54 hours) Timestep: 590000 mean reward (100 episodes): 355.0800 best mean reward: 389.8400 current episode reward: 176.0000 episodes: 429 exploration: 0.46900 learning_rate: 0.00010 elapsed time: 1992.2 seconds (0.55 hours) Timestep: 600000 mean reward (100 episodes): 359.9200 best mean reward: 389.8400 current episode reward: 220.0000 episodes: 436 exploration: 0.46000 learning_rate: 0.00010 elapsed time: 2031.3 seconds (0.56 hours) Timestep: 610000 mean reward (100 episodes): 361.2800 best mean reward: 389.8400 current episode reward: 220.0000 episodes: 442 exploration: 0.45100 learning_rate: 0.00010 elapsed time: 2070.5 seconds (0.58 hours) Timestep: 620000 mean reward (100 episodes): 360.8400 best mean reward: 389.8400 current episode reward: 660.0000 episodes: 449 exploration: 0.44200 learning_rate: 0.00010 elapsed time: 2110.8 seconds (0.59 hours) Timestep: 630000 mean reward (100 episodes): 368.7600 best mean reward: 389.8400 current episode reward: 220.0000 episodes: 455 exploration: 0.43300 learning_rate: 0.00010 elapsed time: 2150.5 seconds (0.60 hours) Timestep: 640000 mean reward (100 episodes): 370.5200 best mean reward: 389.8400 current episode reward: 132.0000 episodes: 462 exploration: 0.42400 learning_rate: 0.00010 elapsed time: 2189.9 seconds (0.61 hours) Timestep: 650000 mean reward (100 episodes): 373.2400 best mean reward: 389.8400 current episode reward: 132.0000 episodes: 469 exploration: 0.41500 learning_rate: 0.00010 elapsed time: 2230.1 seconds (0.62 hours) Timestep: 660000 mean reward (100 episodes): 368.8400 best mean reward: 389.8400 current episode reward: 220.0000 episodes: 476 exploration: 0.40600 learning_rate: 0.00010 elapsed time: 2270.0 seconds (0.63 hours) Timestep: 670000 mean reward (100 episodes): 364.8800 best mean reward: 389.8400 current episode reward: 176.0000 episodes: 483 exploration: 0.39700 learning_rate: 0.00010 elapsed time: 2310.2 seconds (0.64 hours) Timestep: 680000 mean reward (100 episodes): 377.2000 best mean reward: 389.8400 current episode reward: 308.0000 episodes: 489 exploration: 0.38800 learning_rate: 0.00010 elapsed time: 2350.5 seconds (0.65 hours) Timestep: 690000 mean reward (100 episodes): 382.9200 best mean reward: 389.8400 current episode reward: 352.0000 episodes: 496 exploration: 0.37900 learning_rate: 0.00010 elapsed time: 2390.2 seconds (0.66 hours) Timestep: 700000 mean reward (100 episodes): 379.8400 best mean reward: 389.8400 current episode reward: 484.0000 episodes: 502 exploration: 0.37000 learning_rate: 0.00010 elapsed time: 2430.0 seconds (0.67 hours) Timestep: 710000 mean reward (100 episodes): 380.7200 best mean reward: 389.8400 current episode reward: 132.0000 episodes: 508 exploration: 0.36100 learning_rate: 0.00010 elapsed time: 2470.0 seconds (0.69 hours) Timestep: 720000 mean reward (100 episodes): 389.0800 best mean reward: 389.8400 current episode reward: 528.0000 episodes: 515 exploration: 0.35200 learning_rate: 0.00010 elapsed time: 2511.1 seconds (0.70 hours) Timestep: 730000 mean reward (100 episodes): 387.3200 best mean reward: 390.8400 current episode reward: 220.0000 episodes: 522 exploration: 0.34300 learning_rate: 0.00010 elapsed time: 2552.1 seconds (0.71 hours) Timestep: 740000 mean reward (100 episodes): 379.8400 best mean reward: 390.8400 current episode reward: 528.0000 episodes: 530 exploration: 0.33400 learning_rate: 0.00010 elapsed time: 2592.8 seconds (0.72 hours) Timestep: 750000 mean reward (100 episodes): 376.3200 best mean reward: 390.8400 current episode reward: 352.0000 episodes: 536 exploration: 0.32500 learning_rate: 0.00010 elapsed time: 2633.4 seconds (0.73 hours) Timestep: 760000 mean reward (100 episodes): 372.3200 best mean reward: 390.8400 current episode reward: 308.0000 episodes: 544 exploration: 0.31600 learning_rate: 0.00010 elapsed time: 2675.0 seconds (0.74 hours) Timestep: 770000 mean reward (100 episodes): 366.6000 best mean reward: 390.8400 current episode reward: 440.0000 episodes: 552 exploration: 0.30700 learning_rate: 0.00010 elapsed time: 2717.4 seconds (0.75 hours) Timestep: 780000 mean reward (100 episodes): 361.3200 best mean reward: 390.8400 current episode reward: 396.0000 episodes: 559 exploration: 0.29800 learning_rate: 0.00010 elapsed time: 2759.3 seconds (0.77 hours) Timestep: 790000 mean reward (100 episodes): 359.5200 best mean reward: 390.8400 current episode reward: 660.0000 episodes: 565 exploration: 0.28900 learning_rate: 0.00010 elapsed time: 2801.2 seconds (0.78 hours) Timestep: 800000 mean reward (100 episodes): 360.3600 best mean reward: 390.8400 current episode reward: 132.0000 episodes: 571 exploration: 0.28000 learning_rate: 0.00010 elapsed time: 2842.9 seconds (0.79 hours) Timestep: 810000 mean reward (100 episodes): 355.0800 best mean reward: 390.8400 current episode reward: 44.0000 episodes: 579 exploration: 0.27100 learning_rate: 0.00010 elapsed time: 2885.3 seconds (0.80 hours) Timestep: 820000 mean reward (100 episodes): 360.8000 best mean reward: 390.8400 current episode reward: 528.0000 episodes: 585 exploration: 0.26200 learning_rate: 0.00010 elapsed time: 2927.3 seconds (0.81 hours) Timestep: 830000 mean reward (100 episodes): 356.4000 best mean reward: 390.8400 current episode reward: 264.0000 episodes: 591 exploration: 0.25300 learning_rate: 0.00010 elapsed time: 2969.4 seconds (0.82 hours) Timestep: 840000 mean reward (100 episodes): 345.8400 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seconds (0.88 hours) Timestep: 890000 mean reward (100 episodes): 332.2000 best mean reward: 390.8400 current episode reward: 660.0000 episodes: 633 exploration: 0.19900 learning_rate: 0.00010 elapsed time: 3223.5 seconds (0.90 hours) Timestep: 900000 mean reward (100 episodes): 334.8400 best mean reward: 390.8400 current episode reward: 132.0000 episodes: 639 exploration: 0.19000 learning_rate: 0.00010 elapsed time: 3266.3 seconds (0.91 hours) Timestep: 910000 mean reward (100 episodes): 337.4800 best mean reward: 390.8400 current episode reward: 352.0000 episodes: 645 exploration: 0.18100 learning_rate: 0.00010 elapsed time: 3308.9 seconds (0.92 hours) Timestep: 920000 mean reward (100 episodes): 335.2800 best mean reward: 390.8400 current episode reward: 484.0000 episodes: 653 exploration: 0.17200 learning_rate: 0.00010 elapsed time: 3352.6 seconds (0.93 hours) Timestep: 930000 mean reward (100 episodes): 332.6400 best mean reward: 390.8400 current episode reward: 660.0000 episodes: 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best mean reward: 390.8400 current episode reward: 396.0000 episodes: 690 exploration: 0.11800 learning_rate: 0.00010 elapsed time: 3612.3 seconds (1.00 hours) Timestep: 990000 mean reward (100 episodes): 358.6400 best mean reward: 390.8400 current episode reward: 132.0000 episodes: 697 exploration: 0.10900 learning_rate: 0.00010 elapsed time: 3656.2 seconds (1.02 hours) Timestep: 1000000 mean reward (100 episodes): 358.6400 best mean reward: 390.8400 current episode reward: 264.0000 episodes: 703 exploration: 0.10000 learning_rate: 0.00010 elapsed time: 3700.5 seconds (1.03 hours) Timestep: 1010000 mean reward (100 episodes): 362.6000 best mean reward: 390.8400 current episode reward: 528.0000 episodes: 710 exploration: 0.09978 learning_rate: 0.00010 elapsed time: 3744.9 seconds (1.04 hours) Timestep: 1020000 mean reward (100 episodes): 362.1600 best mean reward: 390.8400 current episode reward: 616.0000 episodes: 717 exploration: 0.09955 learning_rate: 0.00010 elapsed time: 3788.4 seconds (1.05 hours) Timestep: 1030000 mean reward (100 episodes): 360.8400 best mean reward: 390.8400 current episode reward: 176.0000 episodes: 723 exploration: 0.09933 learning_rate: 0.00010 elapsed time: 3832.9 seconds (1.06 hours) Timestep: 1040000 mean reward (100 episodes): 365.2400 best mean reward: 390.8400 current episode reward: 660.0000 episodes: 730 exploration: 0.09910 learning_rate: 0.00010 elapsed time: 3878.5 seconds (1.08 hours) Timestep: 1050000 mean reward (100 episodes): 356.8800 best mean reward: 390.8400 current episode reward: 264.0000 episodes: 737 exploration: 0.09888 learning_rate: 0.00010 elapsed time: 3923.4 seconds (1.09 hours) Timestep: 1060000 mean reward (100 episodes): 353.8000 best mean reward: 390.8400 current episode reward: 264.0000 episodes: 744 exploration: 0.09865 learning_rate: 0.00010 elapsed time: 3967.9 seconds (1.10 hours) Timestep: 1070000 mean reward (100 episodes): 364.8000 best mean reward: 390.8400 current episode reward: 440.0000 episodes: 751 exploration: 0.09842 learning_rate: 0.00010 elapsed time: 4012.0 seconds (1.11 hours) Timestep: 1080000 mean reward (100 episodes): 368.3200 best mean reward: 390.8400 current episode reward: 308.0000 episodes: 756 exploration: 0.09820 learning_rate: 0.00010 elapsed time: 4056.2 seconds (1.13 hours) Timestep: 1090000 mean reward (100 episodes): 361.2400 best mean reward: 390.8400 current episode reward: 264.0000 episodes: 763 exploration: 0.09798 learning_rate: 0.00010 elapsed time: 4100.6 seconds (1.14 hours) Timestep: 1100000 mean reward (100 episodes): 355.9600 best mean reward: 390.8400 current episode reward: 440.0000 episodes: 769 exploration: 0.09775 learning_rate: 0.00010 elapsed time: 4145.4 seconds (1.15 hours) Timestep: 1110000 mean reward (100 episodes): 351.5600 best mean reward: 390.8400 current episode reward: 176.0000 episodes: 776 exploration: 0.09753 learning_rate: 0.00010 elapsed time: 4189.8 seconds (1.16 hours) Timestep: 1120000 mean reward (100 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elapsed time: 4412.3 seconds (1.23 hours) Timestep: 1170000 mean reward (100 episodes): 369.6000 best mean reward: 390.8400 current episode reward: 264.0000 episodes: 814 exploration: 0.09618 learning_rate: 0.00010 elapsed time: 4457.1 seconds (1.24 hours) Timestep: 1180000 mean reward (100 episodes): 370.9200 best mean reward: 390.8400 current episode reward: 308.0000 episodes: 819 exploration: 0.09595 learning_rate: 0.00010 elapsed time: 4501.7 seconds (1.25 hours) Timestep: 1190000 mean reward (100 episodes): 381.9200 best mean reward: 390.8400 current episode reward: 484.0000 episodes: 825 exploration: 0.09573 learning_rate: 0.00010 elapsed time: 4546.8 seconds (1.26 hours) Timestep: 1200000 mean reward (100 episodes): 370.0400 best mean reward: 390.8400 current episode reward: 88.0000 episodes: 833 exploration: 0.09550 learning_rate: 0.00010 elapsed time: 4590.6 seconds (1.28 hours) Timestep: 1210000 mean reward (100 episodes): 381.0400 best mean reward: 390.8400 current episode reward: 660.0000 episodes: 839 exploration: 0.09527 learning_rate: 0.00010 elapsed time: 4635.1 seconds (1.29 hours) Timestep: 1220000 mean reward (100 episodes): 389.8400 best mean reward: 390.8400 current episode reward: 660.0000 episodes: 844 exploration: 0.09505 learning_rate: 0.00010 elapsed time: 4678.8 seconds (1.30 hours) Timestep: 1230000 mean reward (100 episodes): 399.5600 best mean reward: 401.3200 current episode reward: 484.0000 episodes: 850 exploration: 0.09483 learning_rate: 0.00010 elapsed time: 4724.3 seconds (1.31 hours) Timestep: 1240000 mean reward (100 episodes): 399.1200 best mean reward: 401.3200 current episode reward: 440.0000 episodes: 856 exploration: 0.09460 learning_rate: 0.00010 elapsed time: 4768.8 seconds (1.32 hours) Timestep: 1250000 mean reward (100 episodes): 391.2000 best mean reward: 401.3200 current episode reward: 132.0000 episodes: 863 exploration: 0.09438 learning_rate: 0.00010 elapsed time: 4812.5 seconds (1.34 hours) Timestep: 1260000 mean 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hours) Timestep: 1400000 mean reward (100 episodes): 438.2000 best mean reward: 447.9200 current episode reward: 440.0000 episodes: 949 exploration: 0.09100 learning_rate: 0.00010 elapsed time: 5475.9 seconds (1.52 hours) Timestep: 1410000 mean reward (100 episodes): 444.4000 best mean reward: 447.9200 current episode reward: 352.0000 episodes: 955 exploration: 0.09078 learning_rate: 0.00009 elapsed time: 5519.5 seconds (1.53 hours) Timestep: 1420000 mean reward (100 episodes): 457.6000 best mean reward: 457.6000 current episode reward: 660.0000 episodes: 961 exploration: 0.09055 learning_rate: 0.00009 elapsed time: 5563.3 seconds (1.55 hours) Timestep: 1430000 mean reward (100 episodes): 454.9600 best mean reward: 459.8000 current episode reward: 352.0000 episodes: 968 exploration: 0.09033 learning_rate: 0.00009 elapsed time: 5607.2 seconds (1.56 hours) Timestep: 1440000 mean reward (100 episodes): 460.3200 best mean reward: 460.3200 current episode reward: 660.0000 episodes: 974 exploration: 0.09010 learning_rate: 0.00009 elapsed time: 5651.7 seconds (1.57 hours) Timestep: 1450000 mean reward (100 episodes): 472.2400 best mean reward: 472.2400 current episode reward: 396.0000 episodes: 980 exploration: 0.08988 learning_rate: 0.00009 elapsed time: 5695.7 seconds (1.58 hours) Timestep: 1460000 mean reward (100 episodes): 478.4800 best mean reward: 478.4800 current episode reward: 756.0000 episodes: 984 exploration: 0.08965 learning_rate: 0.00009 elapsed time: 5740.8 seconds (1.59 hours) Timestep: 1470000 mean reward (100 episodes): 484.0800 best mean reward: 484.0800 current episode reward: 484.0000 episodes: 990 exploration: 0.08943 learning_rate: 0.00009 elapsed time: 5785.5 seconds (1.61 hours) Timestep: 1480000 mean reward (100 episodes): 489.3600 best mean reward: 489.3600 current episode reward: 660.0000 episodes: 995 exploration: 0.08920 learning_rate: 0.00009 elapsed time: 5830.1 seconds (1.62 hours) Timestep: 1490000 mean reward (100 episodes): 489.3600 best mean reward: 489.8000 current episode reward: 804.0000 episodes: 1000 exploration: 0.08897 learning_rate: 0.00009 elapsed time: 5875.0 seconds (1.63 hours) Timestep: 1500000 mean reward (100 episodes): 499.6000 best mean reward: 499.6000 current episode reward: 660.0000 episodes: 1004 exploration: 0.08875 learning_rate: 0.00009 elapsed time: 5920.5 seconds (1.64 hours) Timestep: 1510000 mean reward (100 episodes): 498.3600 best mean reward: 501.0000 current episode reward: 396.0000 episodes: 1009 exploration: 0.08853 learning_rate: 0.00009 elapsed time: 5964.8 seconds (1.66 hours) Timestep: 1520000 mean reward (100 episodes): 509.9200 best mean reward: 509.9200 current episode reward: 484.0000 episodes: 1015 exploration: 0.08830 learning_rate: 0.00009 elapsed time: 6009.3 seconds (1.67 hours) Timestep: 1530000 mean reward (100 episodes): 517.2000 best mean reward: 522.4800 current episode reward: 132.0000 episodes: 1020 exploration: 0.08808 learning_rate: 0.00009 elapsed time: 6054.2 seconds (1.68 hours) Timestep: 1540000 mean reward (100 episodes): 523.7200 best mean reward: 524.6800 current episode reward: 660.0000 episodes: 1025 exploration: 0.08785 learning_rate: 0.00009 elapsed time: 6098.8 seconds (1.69 hours) Timestep: 1550000 mean reward (100 episodes): 531.4800 best mean reward: 531.4800 current episode reward: 900.0000 episodes: 1028 exploration: 0.08763 learning_rate: 0.00009 elapsed time: 6143.1 seconds (1.71 hours) Timestep: 1560000 mean reward (100 episodes): 544.9200 best mean reward: 545.8000 current episode reward: 220.0000 episodes: 1033 exploration: 0.08740 learning_rate: 0.00009 elapsed time: 6187.2 seconds (1.72 hours) Timestep: 1570000 mean reward (100 episodes): 548.1600 best mean reward: 549.9200 current episode reward: 528.0000 episodes: 1038 exploration: 0.08718 learning_rate: 0.00009 elapsed time: 6231.4 seconds (1.73 hours) Timestep: 1580000 mean reward (100 episodes): 549.4400 best mean reward: 549.9200 current episode reward: 660.0000 episodes: 1043 exploration: 0.08695 learning_rate: 0.00009 elapsed time: 6275.6 seconds (1.74 hours) Timestep: 1590000 mean reward (100 episodes): 551.7200 best mean reward: 553.4800 current episode reward: 352.0000 episodes: 1049 exploration: 0.08673 learning_rate: 0.00009 elapsed time: 6319.2 seconds (1.76 hours) Timestep: 1600000 mean reward (100 episodes): 551.4400 best mean reward: 553.6400 current episode reward: 352.0000 episodes: 1054 exploration: 0.08650 learning_rate: 0.00009 elapsed time: 6363.8 seconds (1.77 hours) Timestep: 1610000 mean reward (100 episodes): 558.4800 best mean reward: 558.4800 current episode reward: 660.0000 episodes: 1058 exploration: 0.08628 learning_rate: 0.00009 elapsed time: 6407.8 seconds (1.78 hours) Timestep: 1620000 mean reward (100 episodes): 574.2000 best mean reward: 574.2000 current episode reward: 660.0000 episodes: 1062 exploration: 0.08605 learning_rate: 0.00009 elapsed time: 6451.9 seconds (1.79 hours) Timestep: 1630000 mean reward (100 episodes): 592.3600 best mean reward: 592.3600 current episode reward: 708.0000 episodes: 1067 exploration: 0.08582 learning_rate: 0.00009 elapsed time: 6496.5 seconds (1.80 hours) Timestep: 1640000 mean reward (100 episodes): 593.1600 best mean reward: 595.4400 current episode reward: 396.0000 episodes: 1071 exploration: 0.08560 learning_rate: 0.00009 elapsed time: 6540.7 seconds (1.82 hours) Timestep: 1650000 mean reward (100 episodes): 605.2800 best mean reward: 605.2800 current episode reward: 900.0000 episodes: 1074 exploration: 0.08538 learning_rate: 0.00009 elapsed time: 6585.0 seconds (1.83 hours) Timestep: 1660000 mean reward (100 episodes): 619.2000 best mean reward: 619.2000 current episode reward: 1140.0000 episodes: 1078 exploration: 0.08515 learning_rate: 0.00009 elapsed time: 6629.6 seconds (1.84 hours) Timestep: 1670000 mean reward (100 episodes): 621.8400 best mean reward: 623.6000 current episode reward: 528.0000 episodes: 1083 exploration: 0.08493 learning_rate: 0.00009 elapsed time: 6674.7 seconds (1.85 hours) Timestep: 1680000 mean reward (100 episodes): 626.4000 best mean reward: 627.7200 current episode reward: 660.0000 episodes: 1088 exploration: 0.08470 learning_rate: 0.00009 elapsed time: 6719.5 seconds (1.87 hours) Timestep: 1690000 mean reward (100 episodes): 622.8800 best mean reward: 627.7200 current episode reward: 572.0000 episodes: 1093 exploration: 0.08448 learning_rate: 0.00009 elapsed time: 6764.0 seconds (1.88 hours) Timestep: 1700000 mean reward (100 episodes): 626.2400 best mean reward: 627.7200 current episode reward: 852.0000 episodes: 1096 exploration: 0.08425 learning_rate: 0.00009 elapsed time: 6808.5 seconds (1.89 hours) Timestep: 1710000 mean reward (100 episodes): 623.7600 best mean reward: 631.8000 current episode reward: 616.0000 episodes: 1101 exploration: 0.08403 learning_rate: 0.00009 elapsed time: 6853.0 seconds (1.90 hours) Timestep: 1720000 mean reward (100 episodes): 625.6000 best mean reward: 631.8000 current episode reward: 440.0000 episodes: 1107 exploration: 0.08380 learning_rate: 0.00009 elapsed time: 6897.3 seconds (1.92 hours) Timestep: 1730000 mean reward (100 episodes): 632.6400 best mean reward: 632.6400 current episode reward: 852.0000 episodes: 1111 exploration: 0.08358 learning_rate: 0.00009 elapsed time: 6941.9 seconds (1.93 hours) Timestep: 1740000 mean reward (100 episodes): 629.8800 best mean reward: 632.6400 current episode reward: 660.0000 episodes: 1115 exploration: 0.08335 learning_rate: 0.00009 elapsed time: 6985.8 seconds (1.94 hours) Timestep: 1750000 mean reward (100 episodes): 639.2800 best mean reward: 639.2800 current episode reward: 484.0000 episodes: 1119 exploration: 0.08313 learning_rate: 0.00009 elapsed time: 7030.4 seconds (1.95 hours) Timestep: 1760000 mean reward (100 episodes): 651.2800 best mean reward: 651.2800 current episode reward: 1044.0000 episodes: 1124 exploration: 0.08290 learning_rate: 0.00009 elapsed time: 7074.4 seconds (1.97 hours) Timestep: 1770000 mean reward (100 episodes): 655.1200 best mean reward: 655.6000 current episode reward: 948.0000 episodes: 1128 exploration: 0.08267 learning_rate: 0.00009 elapsed time: 7118.6 seconds (1.98 hours) Timestep: 1780000 mean reward (100 episodes): 657.2400 best mean reward: 659.4400 current episode reward: 440.0000 episodes: 1132 exploration: 0.08245 learning_rate: 0.00009 elapsed time: 7163.6 seconds (1.99 hours) Timestep: 1790000 mean reward (100 episodes): 666.4000 best mean reward: 666.4000 current episode reward: 1236.0000 episodes: 1136 exploration: 0.08223 learning_rate: 0.00009 elapsed time: 7208.0 seconds (2.00 hours) Timestep: 1800000 mean reward (100 episodes): 673.5200 best mean reward: 673.5200 current episode reward: 756.0000 episodes: 1140 exploration: 0.08200 learning_rate: 0.00009 elapsed time: 7253.1 seconds (2.01 hours) Timestep: 1810000 mean reward (100 episodes): 685.4400 best mean reward: 685.4400 current episode reward: 1380.0000 episodes: 1144 exploration: 0.08178 learning_rate: 0.00009 elapsed time: 7296.9 seconds (2.03 hours) Timestep: 1820000 mean reward (100 episodes): 698.2800 best mean reward: 698.2800 current episode reward: 804.0000 episodes: 1148 exploration: 0.08155 learning_rate: 0.00009 elapsed time: 7341.3 seconds (2.04 hours) Timestep: 1830000 mean reward (100 episodes): 702.9600 best mean reward: 702.9600 current episode reward: 352.0000 episodes: 1154 exploration: 0.08133 learning_rate: 0.00009 elapsed time: 7386.0 seconds (2.05 hours) Timestep: 1840000 mean reward (100 episodes): 689.9200 best mean reward: 702.9600 current episode reward: 660.0000 episodes: 1160 exploration: 0.08110 learning_rate: 0.00009 elapsed time: 7430.3 seconds (2.06 hours) Timestep: 1850000 mean reward (100 episodes): 684.6000 best mean reward: 702.9600 current episode reward: 660.0000 episodes: 1164 exploration: 0.08088 learning_rate: 0.00009 elapsed time: 7474.5 seconds (2.08 hours) Timestep: 1860000 mean reward (100 episodes): 686.5200 best mean reward: 702.9600 current episode reward: 852.0000 episodes: 1167 exploration: 0.08065 learning_rate: 0.00009 elapsed time: 7519.1 seconds (2.09 hours) Timestep: 1870000 mean reward (100 episodes): 692.8000 best mean reward: 702.9600 current episode reward: 308.0000 episodes: 1172 exploration: 0.08042 learning_rate: 0.00009 elapsed time: 7563.0 seconds (2.10 hours) Timestep: 1880000 mean reward (100 episodes): 692.8000 best mean reward: 702.9600 current episode reward: 756.0000 episodes: 1175 exploration: 0.08020 learning_rate: 0.00009 elapsed time: 7607.5 seconds (2.11 hours) Timestep: 1890000 mean reward (100 episodes): 694.8400 best mean reward: 704.9200 current episode reward: 396.0000 episodes: 1179 exploration: 0.07998 learning_rate: 0.00009 elapsed time: 7651.8 seconds (2.13 hours) Timestep: 1900000 mean reward (100 episodes): 703.6400 best mean reward: 704.9200 current episode reward: 756.0000 episodes: 1183 exploration: 0.07975 learning_rate: 0.00009 elapsed time: 7696.3 seconds (2.14 hours) Timestep: 1910000 mean reward (100 episodes): 712.0400 best mean reward: 712.0400 current episode reward: 756.0000 episodes: 1186 exploration: 0.07952 learning_rate: 0.00009 elapsed time: 7740.2 seconds (2.15 hours) Timestep: 1920000 mean reward (100 episodes): 735.4000 best mean reward: 735.4000 current episode reward: 2108.0000 episodes: 1189 exploration: 0.07930 learning_rate: 0.00009 elapsed time: 7783.8 seconds (2.16 hours) Timestep: 1930000 mean reward (100 episodes): 752.8000 best mean reward: 752.8000 current episode reward: 440.0000 episodes: 1192 exploration: 0.07908 learning_rate: 0.00009 elapsed time: 7828.0 seconds (2.17 hours) Timestep: 1940000 mean reward (100 episodes): 763.7600 best mean reward: 764.2400 current episode reward: 756.0000 episodes: 1195 exploration: 0.07885 learning_rate: 0.00009 elapsed time: 7872.6 seconds (2.19 hours) Timestep: 1950000 mean reward (100 episodes): 778.1600 best mean reward: 778.1600 current episode reward: 1092.0000 episodes: 1199 exploration: 0.07863 learning_rate: 0.00009 elapsed time: 7916.7 seconds (2.20 hours) Timestep: 1960000 mean reward (100 episodes): 783.3600 best mean reward: 784.7600 current episode reward: 528.0000 episodes: 1203 exploration: 0.07840 learning_rate: 0.00009 elapsed time: 7960.6 seconds (2.21 hours) Timestep: 1970000 mean reward (100 episodes): 794.5600 best mean reward: 794.5600 current episode reward: 1588.0000 episodes: 1205 exploration: 0.07818 learning_rate: 0.00009 elapsed time: 8005.3 seconds (2.22 hours) Timestep: 1980000 mean reward (100 episodes): 810.0400 best mean reward: 810.0400 current episode reward: 1380.0000 episodes: 1208 exploration: 0.07795 learning_rate: 0.00009 elapsed time: 8049.2 seconds (2.24 hours) Timestep: 1990000 mean reward (100 episodes): 816.0000 best mean reward: 816.0000 current episode reward: 1380.0000 episodes: 1211 exploration: 0.07773 learning_rate: 0.00009 elapsed time: 8093.7 seconds (2.25 hours) Timestep: 2000000 mean reward (100 episodes): 833.0400 best mean reward: 833.0400 current episode reward: 1380.0000 episodes: 1215 exploration: 0.07750 learning_rate: 0.00009 elapsed time: 8138.0 seconds (2.26 hours) Timestep: 2010000 mean reward (100 episodes): 832.4000 best mean reward: 833.0400 current episode reward: 660.0000 episodes: 1219 exploration: 0.07728 learning_rate: 0.00009 elapsed time: 8182.9 seconds (2.27 hours) Timestep: 2020000 mean reward (100 episodes): 851.2000 best mean reward: 851.2000 current episode reward: 1380.0000 episodes: 1221 exploration: 0.07705 learning_rate: 0.00009 elapsed time: 8227.8 seconds (2.29 hours) Timestep: 2030000 mean reward (100 episodes): 862.9200 best mean reward: 862.9200 current episode reward: 1380.0000 episodes: 1224 exploration: 0.07683 learning_rate: 0.00009 elapsed time: 8272.3 seconds (2.30 hours) Timestep: 2040000 mean reward (100 episodes): 870.6400 best mean reward: 870.6400 current episode reward: 660.0000 episodes: 1227 exploration: 0.07660 learning_rate: 0.00009 elapsed time: 8317.0 seconds (2.31 hours) Timestep: 2050000 mean reward (100 episodes): 869.7200 best mean reward: 874.4800 current episode reward: 948.0000 episodes: 1231 exploration: 0.07637 learning_rate: 0.00009 elapsed time: 8361.0 seconds (2.32 hours) Timestep: 2060000 mean reward (100 episodes): 869.3200 best mean reward: 874.4800 current episode reward: 660.0000 episodes: 1235 exploration: 0.07615 learning_rate: 0.00009 elapsed time: 8405.3 seconds (2.33 hours) Timestep: 2070000 mean reward (100 episodes): 871.0800 best mean reward: 874.4800 current episode reward: 660.0000 episodes: 1239 exploration: 0.07593 learning_rate: 0.00009 elapsed time: 8450.0 seconds (2.35 hours) Timestep: 2080000 mean reward (100 episodes): 882.4400 best mean reward: 882.4400 current episode reward: 1380.0000 episodes: 1242 exploration: 0.07570 learning_rate: 0.00009 elapsed time: 8494.0 seconds (2.36 hours) Timestep: 2090000 mean reward (100 episodes): 895.1600 best mean reward: 895.1600 current episode reward: 1692.0000 episodes: 1245 exploration: 0.07548 learning_rate: 0.00009 elapsed time: 8539.3 seconds (2.37 hours) Timestep: 2100000 mean reward (100 episodes): 901.2000 best mean reward: 901.2000 current episode reward: 1744.0000 episodes: 1248 exploration: 0.07525 learning_rate: 0.00009 elapsed time: 8583.6 seconds (2.38 hours) Timestep: 2110000 mean reward (100 episodes): 928.0800 best mean reward: 928.0800 current episode reward: 756.0000 episodes: 1251 exploration: 0.07503 learning_rate: 0.00009 elapsed time: 8628.1 seconds (2.40 hours) Timestep: 2120000 mean reward (100 episodes): 944.1600 best mean reward: 944.1600 current episode reward: 1284.0000 episodes: 1254 exploration: 0.07480 learning_rate: 0.00009 elapsed time: 8673.0 seconds (2.41 hours) Timestep: 2130000 mean reward (100 episodes): 958.2000 best mean reward: 958.2000 current episode reward: 1432.0000 episodes: 1257 exploration: 0.07458 learning_rate: 0.00009 elapsed time: 8716.9 seconds (2.42 hours) Timestep: 2140000 mean reward (100 episodes): 995.6600 best mean reward: 995.6600 current episode reward: 4230.0000 episodes: 1258 exploration: 0.07435 learning_rate: 0.00009 elapsed time: 8761.6 seconds (2.43 hours) Timestep: 2150000 mean reward (100 episodes): 1008.2200 best mean reward: 1008.2200 current episode reward: 756.0000 episodes: 1261 exploration: 0.07412 learning_rate: 0.00009 elapsed time: 8806.1 seconds (2.45 hours) Timestep: 2160000 mean reward (100 episodes): 1028.2600 best mean reward: 1028.2600 current episode reward: 1380.0000 episodes: 1265 exploration: 0.07390 learning_rate: 0.00009 elapsed time: 8850.7 seconds (2.46 hours) Timestep: 2170000 mean reward (100 episodes): 1032.7400 best mean reward: 1032.7400 current episode reward: 1536.0000 episodes: 1268 exploration: 0.07368 learning_rate: 0.00009 elapsed time: 8895.9 seconds (2.47 hours) Timestep: 2180000 mean reward (100 episodes): 1024.7800 best mean reward: 1032.7400 current episode reward: 660.0000 episodes: 1272 exploration: 0.07345 learning_rate: 0.00009 elapsed time: 8940.2 seconds (2.48 hours) Timestep: 2190000 mean reward (100 episodes): 1044.4200 best mean reward: 1044.4200 current episode reward: 852.0000 episodes: 1275 exploration: 0.07322 learning_rate: 0.00009 elapsed time: 8984.8 seconds (2.50 hours) Timestep: 2200000 mean reward (100 episodes): 1033.5800 best mean reward: 1044.4200 current episode reward: 352.0000 episodes: 1279 exploration: 0.07300 learning_rate: 0.00009 elapsed time: 9029.7 seconds (2.51 hours) Timestep: 2210000 mean reward (100 episodes): 1054.0200 best mean reward: 1054.0200 current episode reward: 2160.0000 episodes: 1281 exploration: 0.07278 learning_rate: 0.00008 elapsed time: 9074.4 seconds (2.52 hours) Timestep: 2220000 mean reward (100 episodes): 1074.1000 best mean reward: 1074.1000 current episode reward: 2860.0000 episodes: 1284 exploration: 0.07255 learning_rate: 0.00008 elapsed time: 9119.3 seconds (2.53 hours) Timestep: 2230000 mean reward (100 episodes): 1084.7000 best mean reward: 1086.6200 current episode reward: 1092.0000 episodes: 1287 exploration: 0.07233 learning_rate: 0.00008 elapsed time: 9164.0 seconds (2.55 hours) Timestep: 2240000 mean reward (100 episodes): 1083.5400 best mean reward: 1097.5400 current episode reward: 708.0000 episodes: 1289 exploration: 0.07210 learning_rate: 0.00008 elapsed time: 9209.4 seconds (2.56 hours) Timestep: 2250000 mean reward (100 episodes): 1083.5400 best mean reward: 1097.5400 current episode reward: 1284.0000 episodes: 1292 exploration: 0.07187 learning_rate: 0.00008 elapsed time: 9253.5 seconds (2.57 hours) Timestep: 2260000 mean reward (100 episodes): 1087.9000 best mean reward: 1097.5400 current episode reward: 1380.0000 episodes: 1294 exploration: 0.07165 learning_rate: 0.00008 elapsed time: 9298.8 seconds (2.58 hours) Timestep: 2270000 mean reward (100 episodes): 1134.2600 best mean reward: 1134.2600 current episode reward: 2272.0000 episodes: 1296 exploration: 0.07143 learning_rate: 0.00008 elapsed time: 9344.4 seconds (2.60 hours) Timestep: 2280000 mean reward (100 episodes): 1169.8400 best mean reward: 1173.2000 current episode reward: 756.0000 episodes: 1299 exploration: 0.07120 learning_rate: 0.00008 elapsed time: 9389.4 seconds (2.61 hours) Timestep: 2290000 mean reward (100 episodes): 1178.9600 best mean reward: 1178.9600 current episode reward: 1380.0000 episodes: 1301 exploration: 0.07097 learning_rate: 0.00008 elapsed time: 9433.9 seconds (2.62 hours) Timestep: 2300000 mean reward (100 episodes): 1217.1600 best mean reward: 1217.1600 current episode reward: 2552.0000 episodes: 1303 exploration: 0.07075 learning_rate: 0.00008 elapsed time: 9478.1 seconds (2.63 hours) Timestep: 2310000 mean reward (100 episodes): 1205.9600 best mean reward: 1217.1600 current episode reward: 996.0000 episodes: 1306 exploration: 0.07053 learning_rate: 0.00008 elapsed time: 9523.1 seconds (2.65 hours) Timestep: 2320000 mean reward (100 episodes): 1217.8800 best mean reward: 1217.8800 current episode reward: 1044.0000 episodes: 1309 exploration: 0.07030 learning_rate: 0.00008 elapsed time: 9567.2 seconds (2.66 hours) Timestep: 2330000 mean reward (100 episodes): 1218.8400 best mean reward: 1223.6400 current episode reward: 996.0000 episodes: 1312 exploration: 0.07007 learning_rate: 0.00008 elapsed time: 9611.8 seconds (2.67 hours) Timestep: 2340000 mean reward (100 episodes): 1250.4400 best mean reward: 1250.4400 current episode reward: 2108.0000 episodes: 1314 exploration: 0.06985 learning_rate: 0.00008 elapsed time: 9656.5 seconds (2.68 hours) Timestep: 2350000 mean reward (100 episodes): 1261.5600 best mean reward: 1261.5600 current episode reward: 1484.0000 episodes: 1317 exploration: 0.06962 learning_rate: 0.00008 elapsed time: 9700.8 seconds (2.69 hours) Timestep: 2360000 mean reward (100 episodes): 1253.3600 best mean reward: 1261.5600 current episode reward: 1432.0000 episodes: 1320 exploration: 0.06940 learning_rate: 0.00008 elapsed time: 9745.3 seconds (2.71 hours) Timestep: 2370000 mean reward (100 episodes): 1258.6400 best mean reward: 1261.5600 current episode reward: 1140.0000 episodes: 1323 exploration: 0.06918 learning_rate: 0.00008 elapsed time: 9789.7 seconds (2.72 hours) Timestep: 2380000 mean reward (100 episodes): 1255.7200 best mean reward: 1261.5600 current episode reward: 1380.0000 episodes: 1326 exploration: 0.06895 learning_rate: 0.00008 elapsed time: 9835.0 seconds (2.73 hours) Timestep: 2390000 mean reward (100 episodes): 1263.3600 best mean reward: 1263.3600 current episode reward: 1380.0000 episodes: 1329 exploration: 0.06873 learning_rate: 0.00008 elapsed time: 9879.2 seconds (2.74 hours) Timestep: 2400000 mean reward (100 episodes): 1271.5200 best mean reward: 1271.5200 current episode reward: 1044.0000 episodes: 1331 exploration: 0.06850 learning_rate: 0.00008 elapsed time: 9923.7 seconds (2.76 hours) Timestep: 2410000 mean reward (100 episodes): 1300.9200 best mean reward: 1300.9200 current episode reward: 1044.0000 episodes: 1334 exploration: 0.06828 learning_rate: 0.00008 elapsed time: 9967.6 seconds (2.77 hours) Timestep: 2420000 mean reward (100 episodes): 1315.4400 best mean reward: 1315.9200 current episode reward: 1092.0000 episodes: 1336 exploration: 0.06805 learning_rate: 0.00008 elapsed time: 10012.9 seconds (2.78 hours) Timestep: 2430000 mean reward (100 episodes): 1347.3600 best mean reward: 1347.3600 current episode reward: 2160.0000 episodes: 1339 exploration: 0.06782 learning_rate: 0.00008 elapsed time: 10057.7 seconds (2.79 hours) Timestep: 2440000 mean reward (100 episodes): 1367.6800 best mean reward: 1367.6800 current episode reward: 2692.0000 episodes: 1341 exploration: 0.06760 learning_rate: 0.00008 elapsed time: 10102.3 seconds (2.81 hours) Timestep: 2450000 mean reward (100 episodes): 1356.7200 best mean reward: 1368.2000 current episode reward: 616.0000 episodes: 1344 exploration: 0.06738 learning_rate: 0.00008 elapsed time: 10146.5 seconds (2.82 hours) Timestep: 2460000 mean reward (100 episodes): 1384.0000 best mean reward: 1384.0000 current episode reward: 1380.0000 episodes: 1347 exploration: 0.06715 learning_rate: 0.00008 elapsed time: 10191.4 seconds (2.83 hours) Timestep: 2470000 mean reward (100 episodes): 1371.1600 best mean reward: 1388.1600 current episode reward: 756.0000 episodes: 1350 exploration: 0.06693 learning_rate: 0.00008 elapsed time: 10235.6 seconds (2.84 hours) Timestep: 2480000 mean reward (100 episodes): 1380.6400 best mean reward: 1388.1600 current episode reward: 2160.0000 episodes: 1353 exploration: 0.06670 learning_rate: 0.00008 elapsed time: 10281.0 seconds (2.86 hours) Timestep: 2490000 mean reward (100 episodes): 1391.6400 best mean reward: 1391.6400 current episode reward: 1952.0000 episodes: 1355 exploration: 0.06648 learning_rate: 0.00008 elapsed time: 10325.7 seconds (2.87 hours) Timestep: 2500000 mean reward (100 episodes): 1375.6600 best mean reward: 1403.6800 current episode reward: 2056.0000 episodes: 1358 exploration: 0.06625 learning_rate: 0.00008 elapsed time: 10370.6 seconds (2.88 hours) Timestep: 2510000 mean reward (100 episodes): 1396.9000 best mean reward: 1403.6800 current episode reward: 2056.0000 episodes: 1360 exploration: 0.06603 learning_rate: 0.00008 elapsed time: 10415.6 seconds (2.89 hours) Timestep: 2520000 mean reward (100 episodes): 1419.9400 best mean reward: 1419.9400 current episode reward: 3060.0000 episodes: 1361 exploration: 0.06580 learning_rate: 0.00008 elapsed time: 10460.5 seconds (2.91 hours) Timestep: 2530000 mean reward (100 episodes): 1433.9800 best mean reward: 1433.9800 current episode reward: 1332.0000 episodes: 1364 exploration: 0.06557 learning_rate: 0.00008 elapsed time: 10504.7 seconds (2.92 hours) Timestep: 2540000 mean reward (100 episodes): 1432.6200 best mean reward: 1433.9800 current episode reward: 1692.0000 episodes: 1368 exploration: 0.06535 learning_rate: 0.00008 elapsed time: 10549.3 seconds (2.93 hours) Timestep: 2550000 mean reward (100 episodes): 1449.6200 best mean reward: 1449.6200 current episode reward: 1692.0000 episodes: 1370 exploration: 0.06513 learning_rate: 0.00008 elapsed time: 10593.5 seconds (2.94 hours) Timestep: 2560000 mean reward (100 episodes): 1465.2600 best mean reward: 1465.2600 current episode reward: 1744.0000 episodes: 1373 exploration: 0.06490 learning_rate: 0.00008 elapsed time: 10638.1 seconds (2.96 hours) Timestep: 2570000 mean reward (100 episodes): 1459.5400 best mean reward: 1465.2600 current episode reward: 852.0000 episodes: 1376 exploration: 0.06468 learning_rate: 0.00008 elapsed time: 10682.4 seconds (2.97 hours) Timestep: 2580000 mean reward (100 episodes): 1482.4200 best mean reward: 1483.3800 current episode reward: 708.0000 episodes: 1378 exploration: 0.06445 learning_rate: 0.00008 elapsed time: 10726.5 seconds (2.98 hours) Timestep: 2590000 mean reward (100 episodes): 1488.8600 best mean reward: 1494.7800 current episode reward: 996.0000 episodes: 1380 exploration: 0.06423 learning_rate: 0.00008 elapsed time: 10771.7 seconds (2.99 hours) Timestep: 2600000 mean reward (100 episodes): 1534.1200 best mean reward: 1534.1200 current episode reward: 2692.0000 episodes: 1383 exploration: 0.06400 learning_rate: 0.00008 elapsed time: 10816.4 seconds (3.00 hours) Timestep: 2610000 mean reward (100 episodes): 1515.6000 best mean reward: 1534.1200 current episode reward: 1332.0000 episodes: 1386 exploration: 0.06377 learning_rate: 0.00008 elapsed time: 10861.3 seconds (3.02 hours) Timestep: 2620000 mean reward (100 episodes): 1543.3800 best mean reward: 1543.3800 current episode reward: 3870.0000 episodes: 1387 exploration: 0.06355 learning_rate: 0.00008 elapsed time: 10905.6 seconds (3.03 hours) Timestep: 2630000 mean reward (100 episodes): 1577.0200 best mean reward: 1577.0200 current episode reward: 2972.0000 episodes: 1389 exploration: 0.06333 learning_rate: 0.00008 elapsed time: 10950.2 seconds (3.04 hours) Timestep: 2640000 mean reward (100 episodes): 1581.2200 best mean reward: 1581.2200 current episode reward: 1484.0000 episodes: 1391 exploration: 0.06310 learning_rate: 0.00008 elapsed time: 10994.4 seconds (3.05 hours) Timestep: 2650000 mean reward (100 episodes): 1613.0200 best mean reward: 1613.0200 current episode reward: 3084.0000 episodes: 1394 exploration: 0.06288 learning_rate: 0.00008 elapsed time: 11038.9 seconds (3.07 hours) Timestep: 2660000 mean reward (100 episodes): 1587.7000 best mean reward: 1613.0200 current episode reward: 2860.0000 episodes: 1396 exploration: 0.06265 learning_rate: 0.00008 elapsed time: 11084.0 seconds (3.08 hours) Timestep: 2670000 mean reward (100 episodes): 1578.2400 best mean reward: 1613.0200 current episode reward: 2108.0000 episodes: 1399 exploration: 0.06243 learning_rate: 0.00008 elapsed time: 11128.6 seconds (3.09 hours) Timestep: 2680000 mean reward (100 episodes): 1576.6000 best mean reward: 1613.0200 current episode reward: 2916.0000 episodes: 1402 exploration: 0.06220 learning_rate: 0.00008 elapsed time: 11173.6 seconds (3.10 hours) Timestep: 2690000 mean reward (100 episodes): 1573.3600 best mean reward: 1613.0200 current episode reward: 1796.0000 episodes: 1405 exploration: 0.06198 learning_rate: 0.00008 elapsed time: 11217.5 seconds (3.12 hours) Timestep: 2700000 mean reward (100 episodes): 1579.3200 best mean reward: 1613.0200 current episode reward: 2160.0000 episodes: 1408 exploration: 0.06175 learning_rate: 0.00008 elapsed time: 11263.0 seconds (3.13 hours) Timestep: 2710000 mean reward (100 episodes): 1578.3600 best mean reward: 1613.0200 current episode reward: 1284.0000 episodes: 1410 exploration: 0.06153 learning_rate: 0.00008 elapsed time: 11307.9 seconds (3.14 hours) Timestep: 2720000 mean reward (100 episodes): 1575.7200 best mean reward: 1613.0200 current episode reward: 660.0000 episodes: 1414 exploration: 0.06130 learning_rate: 0.00008 elapsed time: 11352.6 seconds (3.15 hours) Timestep: 2730000 mean reward (100 episodes): 1599.6000 best mean reward: 1613.0200 current episode reward: 2108.0000 episodes: 1416 exploration: 0.06108 learning_rate: 0.00008 elapsed time: 11396.5 seconds (3.17 hours) Timestep: 2740000 mean reward (100 episodes): 1630.4800 best mean reward: 1630.4800 current episode reward: 3900.0000 episodes: 1418 exploration: 0.06085 learning_rate: 0.00008 elapsed time: 11441.4 seconds (3.18 hours) Timestep: 2750000 mean reward (100 episodes): 1627.5600 best mean reward: 1634.8000 current episode reward: 1044.0000 episodes: 1422 exploration: 0.06062 learning_rate: 0.00008 elapsed time: 11485.9 seconds (3.19 hours) Timestep: 2760000 mean reward (100 episodes): 1643.6000 best mean reward: 1643.6000 current episode reward: 2888.0000 episodes: 1425 exploration: 0.06040 learning_rate: 0.00008 elapsed time: 11531.2 seconds (3.20 hours) Timestep: 2770000 mean reward (100 episodes): 1645.7200 best mean reward: 1646.2000 current episode reward: 1140.0000 episodes: 1427 exploration: 0.06017 learning_rate: 0.00008 elapsed time: 11576.2 seconds (3.22 hours) Timestep: 2780000 mean reward (100 episodes): 1673.4000 best mean reward: 1673.4000 current episode reward: 3000.0000 episodes: 1429 exploration: 0.05995 learning_rate: 0.00008 elapsed time: 11621.4 seconds (3.23 hours) Timestep: 2790000 mean reward (100 episodes): 1666.0800 best mean reward: 1676.7600 current episode reward: 1092.0000 episodes: 1432 exploration: 0.05973 learning_rate: 0.00008 elapsed time: 11666.0 seconds (3.24 hours) Timestep: 2800000 mean reward (100 episodes): 1693.6400 best mean reward: 1694.1600 current episode reward: 2108.0000 episodes: 1435 exploration: 0.05950 learning_rate: 0.00008 elapsed time: 11710.5 seconds (3.25 hours) Timestep: 2810000 mean reward (100 episodes): 1698.2400 best mean reward: 1698.2400 current episode reward: 1900.0000 episodes: 1438 exploration: 0.05928 learning_rate: 0.00008 elapsed time: 11755.8 seconds (3.27 hours) Timestep: 2820000 mean reward (100 episodes): 1704.4800 best mean reward: 1704.4800 current episode reward: 2004.0000 episodes: 1440 exploration: 0.05905 learning_rate: 0.00008 elapsed time: 11801.3 seconds (3.28 hours) Timestep: 2830000 mean reward (100 episodes): 1713.0000 best mean reward: 1713.0000 current episode reward: 1188.0000 episodes: 1443 exploration: 0.05883 learning_rate: 0.00008 elapsed time: 11847.1 seconds (3.29 hours) Timestep: 2840000 mean reward (100 episodes): 1723.5200 best mean reward: 1740.4800 current episode reward: 1380.0000 episodes: 1446 exploration: 0.05860 learning_rate: 0.00008 elapsed time: 11891.2 seconds (3.30 hours) Timestep: 2850000 mean reward (100 episodes): 1760.1200 best mean reward: 1760.1200 current episode reward: 2972.0000 episodes: 1449 exploration: 0.05837 learning_rate: 0.00008 elapsed time: 11936.0 seconds (3.32 hours) Timestep: 2860000 mean reward (100 episodes): 1782.6400 best mean reward: 1786.4800 current episode reward: 804.0000 episodes: 1452 exploration: 0.05815 learning_rate: 0.00008 elapsed time: 11981.4 seconds (3.33 hours) Timestep: 2870000 mean reward (100 episodes): 1759.5200 best mean reward: 1786.4800 current episode reward: 852.0000 episodes: 1456 exploration: 0.05792 learning_rate: 0.00008 elapsed time: 12026.3 seconds (3.34 hours) Timestep: 2880000 mean reward (100 episodes): 1768.8400 best mean reward: 1786.4800 current episode reward: 1380.0000 episodes: 1459 exploration: 0.05770 learning_rate: 0.00008 elapsed time: 12070.9 seconds (3.35 hours) Timestep: 2890000 mean reward (100 episodes): 1778.5200 best mean reward: 1786.4800 current episode reward: 3420.0000 episodes: 1461 exploration: 0.05748 learning_rate: 0.00008 elapsed time: 12115.9 seconds (3.37 hours) Timestep: 2900000 mean reward (100 episodes): 1805.1600 best mean reward: 1805.1600 current episode reward: 2888.0000 episodes: 1464 exploration: 0.05725 learning_rate: 0.00008 elapsed time: 12160.8 seconds (3.38 hours) Timestep: 2910000 mean reward (100 episodes): 1816.9200 best mean reward: 1820.0400 current episode reward: 1380.0000 episodes: 1468 exploration: 0.05702 learning_rate: 0.00008 elapsed time: 12205.3 seconds (3.39 hours) Timestep: 2920000 mean reward (100 episodes): 1834.8000 best mean reward: 1834.8000 current episode reward: 1380.0000 episodes: 1471 exploration: 0.05680 learning_rate: 0.00008 elapsed time: 12249.7 seconds (3.40 hours) Timestep: 2930000 mean reward (100 episodes): 1838.8400 best mean reward: 1842.0000 current episode reward: 1380.0000 episodes: 1475 exploration: 0.05658 learning_rate: 0.00008 elapsed time: 12294.3 seconds (3.42 hours) Timestep: 2940000 mean reward (100 episodes): 1856.6800 best mean reward: 1856.6800 current episode reward: 1380.0000 episodes: 1478 exploration: 0.05635 learning_rate: 0.00008 elapsed time: 12340.3 seconds (3.43 hours) Timestep: 2950000 mean reward (100 episodes): 1840.5800 best mean reward: 1868.7200 current episode reward: 1536.0000 episodes: 1481 exploration: 0.05613 learning_rate: 0.00008 elapsed time: 12385.0 seconds (3.44 hours) Timestep: 2960000 mean reward (100 episodes): 1848.6600 best mean reward: 1868.7200 current episode reward: 3900.0000 episodes: 1483 exploration: 0.05590 learning_rate: 0.00008 elapsed time: 12430.0 seconds (3.45 hours) Timestep: 2970000 mean reward (100 episodes): 1916.3200 best mean reward: 1916.3200 current episode reward: 3480.0000 episodes: 1485 exploration: 0.05568 learning_rate: 0.00008 elapsed time: 12475.5 seconds (3.47 hours) Timestep: 2980000 mean reward (100 episodes): 1913.2000 best mean reward: 1946.5000 current episode reward: 1380.0000 episodes: 1488 exploration: 0.05545 learning_rate: 0.00008 elapsed time: 12520.2 seconds (3.48 hours) Timestep: 2990000 mean reward (100 episodes): 1896.2800 best mean reward: 1946.5000 current episode reward: 1380.0000 episodes: 1492 exploration: 0.05523 learning_rate: 0.00008 elapsed time: 12564.3 seconds (3.49 hours) Timestep: 3000000 mean reward (100 episodes): 1876.0800 best mean reward: 1946.5000 current episode reward: 2160.0000 episodes: 1496 exploration: 0.05500 learning_rate: 0.00008 elapsed time: 12610.4 seconds (3.50 hours) Timestep: 3010000 mean reward (100 episodes): 1921.7400 best mean reward: 1946.5000 current episode reward: 2160.0000 episodes: 1498 exploration: 0.05478 learning_rate: 0.00007 elapsed time: 12654.7 seconds (3.52 hours) Timestep: 3020000 mean reward (100 episodes): 1977.1800 best mean reward: 1977.1800 current episode reward: 4448.0000 episodes: 1500 exploration: 0.05455 learning_rate: 0.00007 elapsed time: 12699.4 seconds (3.53 hours) Timestep: 3030000 mean reward (100 episodes): 2006.2800 best mean reward: 2008.3200 current episode reward: 2160.0000 episodes: 1503 exploration: 0.05433 learning_rate: 0.00007 elapsed time: 12744.4 seconds (3.54 hours) Timestep: 3040000 mean reward (100 episodes): 2028.4400 best mean reward: 2028.4400 current episode reward: 1692.0000 episodes: 1506 exploration: 0.05410 learning_rate: 0.00007 elapsed time: 12788.9 seconds (3.55 hours) Timestep: 3050000 mean reward (100 episodes): 2056.7200 best mean reward: 2056.7200 current episode reward: 3364.0000 episodes: 1509 exploration: 0.05388 learning_rate: 0.00007 elapsed time: 12834.3 seconds (3.57 hours) Timestep: 3060000 mean reward (100 episodes): 2062.0800 best mean reward: 2071.6400 current episode reward: 1952.0000 episodes: 1512 exploration: 0.05365 learning_rate: 0.00007 elapsed time: 12879.3 seconds (3.58 hours) Timestep: 3070000 mean reward (100 episodes): 2059.4000 best mean reward: 2075.5200 current episode reward: 1332.0000 episodes: 1515 exploration: 0.05343 learning_rate: 0.00007 elapsed time: 12924.1 seconds (3.59 hours) Timestep: 3080000 mean reward (100 episodes): 2051.9200 best mean reward: 2075.5200 current episode reward: 1640.0000 episodes: 1518 exploration: 0.05320 learning_rate: 0.00007 elapsed time: 12969.4 seconds (3.60 hours) Timestep: 3090000 mean reward (100 episodes): 2102.0800 best mean reward: 2102.0800 current episode reward: 2056.0000 episodes: 1521 exploration: 0.05298 learning_rate: 0.00007 elapsed time: 13014.2 seconds (3.62 hours) Timestep: 3100000 mean reward (100 episodes): 2106.1200 best mean reward: 2121.2000 current episode reward: 1380.0000 episodes: 1525 exploration: 0.05275 learning_rate: 0.00007 elapsed time: 13059.2 seconds (3.63 hours) Timestep: 3110000 mean reward (100 episodes): 2106.0800 best mean reward: 2121.2000 current episode reward: 1332.0000 episodes: 1528 exploration: 0.05253 learning_rate: 0.00007 elapsed time: 13105.3 seconds (3.64 hours) Timestep: 3120000 mean reward (100 episodes): 2143.3200 best mean reward: 2143.3200 current episode reward: 4740.0000 episodes: 1530 exploration: 0.05230 learning_rate: 0.00007 elapsed time: 13150.1 seconds (3.65 hours) Timestep: 3130000 mean reward (100 episodes): 2196.7000 best mean reward: 2196.7000 current episode reward: 3840.0000 episodes: 1533 exploration: 0.05208 learning_rate: 0.00007 elapsed time: 13195.5 seconds (3.67 hours) Timestep: 3140000 mean reward (100 episodes): 2205.9200 best mean reward: 2205.9200 current episode reward: 4478.0000 episodes: 1535 exploration: 0.05185 learning_rate: 0.00007 elapsed time: 13240.2 seconds (3.68 hours) Timestep: 3150000 mean reward (100 episodes): 2259.2000 best mean reward: 2263.8800 current episode reward: 1692.0000 episodes: 1539 exploration: 0.05163 learning_rate: 0.00007 elapsed time: 13285.5 seconds (3.69 hours) Timestep: 3160000 mean reward (100 episodes): 2295.0000 best mean reward: 2295.0000 current episode reward: 4350.0000 episodes: 1541 exploration: 0.05140 learning_rate: 0.00007 elapsed time: 13331.2 seconds (3.70 hours) Timestep: 3170000 mean reward (100 episodes): 2366.1000 best mean reward: 2366.1000 current episode reward: 5474.0000 episodes: 1543 exploration: 0.05117 learning_rate: 0.00007 elapsed time: 13376.1 seconds (3.72 hours) Timestep: 3180000 mean reward (100 episodes): 2396.8400 best mean reward: 2396.8400 current episode reward: 2860.0000 episodes: 1545 exploration: 0.05095 learning_rate: 0.00007 elapsed time: 13420.8 seconds (3.73 hours) Timestep: 3190000 mean reward (100 episodes): 2452.5200 best mean reward: 2452.5200 current episode reward: 5268.0000 episodes: 1547 exploration: 0.05072 learning_rate: 0.00007 elapsed time: 13465.8 seconds (3.74 hours) Timestep: 3200000 mean reward (100 episodes): 2493.6200 best mean reward: 2493.6200 current episode reward: 3660.0000 episodes: 1549 exploration: 0.05050 learning_rate: 0.00007 elapsed time: 13511.0 seconds (3.75 hours) Timestep: 3210000 mean reward (100 episodes): 2509.2600 best mean reward: 2509.2600 current episode reward: 2664.0000 episodes: 1551 exploration: 0.05028 learning_rate: 0.00007 elapsed time: 13556.2 seconds (3.77 hours) Timestep: 3220000 mean reward (100 episodes): 2568.1800 best mean reward: 2568.1800 current episode reward: 2328.0000 episodes: 1554 exploration: 0.05005 learning_rate: 0.00007 elapsed time: 13601.4 seconds (3.78 hours) Timestep: 3230000 mean reward (100 episodes): 2612.7800 best mean reward: 2612.7800 current episode reward: 2552.0000 episodes: 1556 exploration: 0.04983 learning_rate: 0.00007 elapsed time: 13646.3 seconds (3.79 hours) Timestep: 3240000 mean reward (100 episodes): 2676.4800 best mean reward: 2676.4800 current episode reward: 2160.0000 episodes: 1559 exploration: 0.04960 learning_rate: 0.00007 elapsed time: 13692.0 seconds (3.80 hours) Timestep: 3250000 mean reward (100 episodes): 2672.0000 best mean reward: 2679.0000 current episode reward: 2720.0000 episodes: 1561 exploration: 0.04938 learning_rate: 0.00007 elapsed time: 13736.6 seconds (3.82 hours) Timestep: 3260000 mean reward (100 episodes): 2704.2600 best mean reward: 2704.2600 current episode reward: 2440.0000 episodes: 1563 exploration: 0.04915 learning_rate: 0.00007 elapsed time: 13781.1 seconds (3.83 hours) Timestep: 3270000 mean reward (100 episodes): 2736.7400 best mean reward: 2736.7400 current episode reward: 4156.0000 episodes: 1565 exploration: 0.04892 learning_rate: 0.00007 elapsed time: 13826.4 seconds (3.84 hours) Timestep: 3280000 mean reward (100 episodes): 2797.8200 best mean reward: 2797.8200 current episode reward: 4320.0000 episodes: 1567 exploration: 0.04870 learning_rate: 0.00007 elapsed time: 13872.4 seconds (3.85 hours) Timestep: 3290000 mean reward (100 episodes): 2836.3400 best mean reward: 2836.3400 current episode reward: 4156.0000 episodes: 1570 exploration: 0.04847 learning_rate: 0.00007 elapsed time: 13917.2 seconds (3.87 hours) Timestep: 3300000 mean reward (100 episodes): 2938.6200 best mean reward: 2938.6200 current episode reward: 5734.0000 episodes: 1572 exploration: 0.04825 learning_rate: 0.00007 elapsed time: 13962.0 seconds (3.88 hours) Timestep: 3310000 mean reward (100 episodes): 2986.0600 best mean reward: 2986.0600 current episode reward: 3420.0000 episodes: 1575 exploration: 0.04802 learning_rate: 0.00007 elapsed time: 14007.2 seconds (3.89 hours) Timestep: 3320000 mean reward (100 episodes): 2990.8200 best mean reward: 2994.4600 current episode reward: 2440.0000 episodes: 1577 exploration: 0.04780 learning_rate: 0.00007 elapsed time: 14051.9 seconds (3.90 hours) Timestep: 3330000 mean reward (100 episodes): 3036.9400 best mean reward: 3036.9400 current episode reward: 2004.0000 episodes: 1580 exploration: 0.04757 learning_rate: 0.00007 elapsed time: 14096.3 seconds (3.92 hours) Timestep: 3340000 mean reward (100 episodes): 3095.7200 best mean reward: 3095.7200 current episode reward: 5602.0000 episodes: 1582 exploration: 0.04735 learning_rate: 0.00007 elapsed time: 14141.6 seconds (3.93 hours) Timestep: 3350000 mean reward (100 episodes): 3084.9000 best mean reward: 3112.1600 current episode reward: 3000.0000 episodes: 1584 exploration: 0.04713 learning_rate: 0.00007 elapsed time: 14186.7 seconds (3.94 hours) Timestep: 3360000 mean reward (100 episodes): 3073.5400 best mean reward: 3112.1600 current episode reward: 1744.0000 episodes: 1587 exploration: 0.04690 learning_rate: 0.00007 elapsed time: 14232.2 seconds (3.95 hours) Timestep: 3370000 mean reward (100 episodes): 3129.7800 best mean reward: 3129.7800 current episode reward: 4320.0000 episodes: 1589 exploration: 0.04667 learning_rate: 0.00007 elapsed time: 14276.5 seconds (3.97 hours) Timestep: 3380000 mean reward (100 episodes): 3184.7800 best mean reward: 3184.7800 current episode reward: 3900.0000 episodes: 1592 exploration: 0.04645 learning_rate: 0.00007 elapsed time: 14321.2 seconds (3.98 hours) Timestep: 3390000 mean reward (100 episodes): 3270.9800 best mean reward: 3270.9800 current episode reward: 5508.0000 episodes: 1594 exploration: 0.04622 learning_rate: 0.00007 elapsed time: 14366.0 seconds (3.99 hours) Timestep: 3400000 mean reward (100 episodes): 3290.1400 best mean reward: 3293.7800 current episode reward: 1796.0000 episodes: 1596 exploration: 0.04600 learning_rate: 0.00007 elapsed time: 14410.4 seconds (4.00 hours) Timestep: 3410000 mean reward (100 episodes): 3270.1400 best mean reward: 3293.7800 current episode reward: 4734.0000 episodes: 1599 exploration: 0.04577 learning_rate: 0.00007 elapsed time: 14455.7 seconds (4.02 hours) Timestep: 3420000 mean reward (100 episodes): 3285.8400 best mean reward: 3293.7800 current episode reward: 4960.0000 episodes: 1601 exploration: 0.04555 learning_rate: 0.00007 elapsed time: 14500.0 seconds (4.03 hours) Timestep: 3430000 mean reward (100 episodes): 3327.6200 best mean reward: 3327.6200 current episode reward: 5310.0000 episodes: 1604 exploration: 0.04532 learning_rate: 0.00007 elapsed time: 14544.9 seconds (4.04 hours) Timestep: 3440000 mean reward (100 episodes): 3329.2600 best mean reward: 3329.2600 current episode reward: 2888.0000 episodes: 1608 exploration: 0.04510 learning_rate: 0.00007 elapsed time: 14589.1 seconds (4.05 hours) Timestep: 3450000 mean reward (100 episodes): 3343.9000 best mean reward: 3343.9000 current episode reward: 3000.0000 episodes: 1610 exploration: 0.04487 learning_rate: 0.00007 elapsed time: 14634.1 seconds (4.07 hours) Timestep: 3460000 mean reward (100 episodes): 3408.1400 best mean reward: 3408.1400 current episode reward: 5340.0000 episodes: 1612 exploration: 0.04465 learning_rate: 0.00007 elapsed time: 14679.7 seconds (4.08 hours) Timestep: 3470000 mean reward (100 episodes): 3431.7800 best mean reward: 3431.7800 current episode reward: 1848.0000 episodes: 1614 exploration: 0.04442 learning_rate: 0.00007 elapsed time: 14724.5 seconds (4.09 hours) Timestep: 3480000 mean reward (100 episodes): 3485.1200 best mean reward: 3495.0800 current episode reward: 2004.0000 episodes: 1617 exploration: 0.04420 learning_rate: 0.00007 elapsed time: 14770.1 seconds (4.10 hours) Timestep: 3490000 mean reward (100 episodes): 3552.4400 best mean reward: 3552.4400 current episode reward: 6596.0000 episodes: 1619 exploration: 0.04397 learning_rate: 0.00007 elapsed time: 14814.7 seconds (4.12 hours) Timestep: 3500000 mean reward (100 episodes): 3603.9800 best mean reward: 3603.9800 current episode reward: 5190.0000 episodes: 1621 exploration: 0.04375 learning_rate: 0.00007 elapsed time: 14859.7 seconds (4.13 hours) Timestep: 3510000 mean reward (100 episodes): 3692.8200 best mean reward: 3692.8200 current episode reward: 5676.0000 episodes: 1623 exploration: 0.04353 learning_rate: 0.00007 elapsed time: 14905.3 seconds (4.14 hours) Timestep: 3520000 mean reward (100 episodes): 3719.1000 best mean reward: 3719.1000 current episode reward: 4320.0000 episodes: 1625 exploration: 0.04330 learning_rate: 0.00007 elapsed time: 14950.5 seconds (4.15 hours) Timestep: 3530000 mean reward (100 episodes): 3836.7400 best mean reward: 3836.7400 current episode reward: 5662.0000 episodes: 1628 exploration: 0.04308 learning_rate: 0.00007 elapsed time: 14994.8 seconds (4.17 hours) Timestep: 3540000 mean reward (100 episodes): 3768.2400 best mean reward: 3836.7400 current episode reward: 2160.0000 episodes: 1631 exploration: 0.04285 learning_rate: 0.00007 elapsed time: 15039.9 seconds (4.18 hours) Timestep: 3550000 mean reward (100 episodes): 3773.7600 best mean reward: 3836.7400 current episode reward: 1640.0000 episodes: 1634 exploration: 0.04263 learning_rate: 0.00007 elapsed time: 15085.1 seconds (4.19 hours) Timestep: 3560000 mean reward (100 episodes): 3772.7000 best mean reward: 3836.7400 current episode reward: 4050.0000 episodes: 1636 exploration: 0.04240 learning_rate: 0.00007 elapsed time: 15130.0 seconds (4.20 hours) Timestep: 3570000 mean reward (100 episodes): 3802.3800 best mean reward: 3836.7400 current episode reward: 5884.0000 episodes: 1638 exploration: 0.04218 learning_rate: 0.00007 elapsed time: 15175.5 seconds (4.22 hours) Timestep: 3580000 mean reward (100 episodes): 3833.0800 best mean reward: 3836.7400 current episode reward: 4604.0000 episodes: 1640 exploration: 0.04195 learning_rate: 0.00007 elapsed time: 15220.7 seconds (4.23 hours) Timestep: 3590000 mean reward (100 episodes): 3856.1600 best mean reward: 3856.1600 current episode reward: 5312.0000 episodes: 1642 exploration: 0.04173 learning_rate: 0.00007 elapsed time: 15265.5 seconds (4.24 hours) Timestep: 3600000 mean reward (100 episodes): 3834.9600 best mean reward: 3856.1600 current episode reward: 5312.0000 episodes: 1644 exploration: 0.04150 learning_rate: 0.00007 elapsed time: 15310.2 seconds (4.25 hours) Timestep: 3610000 mean reward (100 episodes): 3832.2800 best mean reward: 3859.1600 current episode reward: 3000.0000 episodes: 1647 exploration: 0.04127 learning_rate: 0.00007 elapsed time: 15355.4 seconds (4.27 hours) Timestep: 3620000 mean reward (100 episodes): 3800.6200 best mean reward: 3859.1600 current episode reward: 4540.0000 episodes: 1650 exploration: 0.04105 learning_rate: 0.00007 elapsed time: 15400.1 seconds (4.28 hours) Timestep: 3630000 mean reward (100 episodes): 3807.1000 best mean reward: 3859.1600 current episode reward: 3900.0000 episodes: 1652 exploration: 0.04083 learning_rate: 0.00007 elapsed time: 15444.9 seconds (4.29 hours) Timestep: 3640000 mean reward (100 episodes): 3836.9400 best mean reward: 3859.1600 current episode reward: 3420.0000 episodes: 1655 exploration: 0.04060 learning_rate: 0.00007 elapsed time: 15490.7 seconds (4.30 hours) Timestep: 3650000 mean reward (100 episodes): 3859.9400 best mean reward: 3860.0200 current episode reward: 5718.0000 episodes: 1657 exploration: 0.04038 learning_rate: 0.00007 elapsed time: 15535.2 seconds (4.32 hours) Timestep: 3660000 mean reward (100 episodes): 3876.3000 best mean reward: 3876.3000 current episode reward: 4640.0000 episodes: 1659 exploration: 0.04015 learning_rate: 0.00007 elapsed time: 15580.4 seconds (4.33 hours) Timestep: 3670000 mean reward (100 episodes): 3909.2000 best mean reward: 3909.2000 current episode reward: 5446.0000 episodes: 1661 exploration: 0.03993 learning_rate: 0.00007 elapsed time: 15626.2 seconds (4.34 hours) Timestep: 3680000 mean reward (100 episodes): 3944.6800 best mean reward: 3944.6800 current episode reward: 5726.0000 episodes: 1664 exploration: 0.03970 learning_rate: 0.00007 elapsed time: 15670.9 seconds (4.35 hours) Timestep: 3690000 mean reward (100 episodes): 3948.2800 best mean reward: 3948.2800 current episode reward: 4604.0000 episodes: 1666 exploration: 0.03947 learning_rate: 0.00007 elapsed time: 15716.3 seconds (4.37 hours) Timestep: 3700000 mean reward (100 episodes): 3947.1200 best mean reward: 3948.2800 current episode reward: 4140.0000 episodes: 1668 exploration: 0.03925 learning_rate: 0.00007 elapsed time: 15761.9 seconds (4.38 hours) Timestep: 3710000 mean reward (100 episodes): 3915.7800 best mean reward: 3977.6000 current episode reward: 1952.0000 episodes: 1671 exploration: 0.03902 learning_rate: 0.00007 elapsed time: 15807.2 seconds (4.39 hours) Timestep: 3720000 mean reward (100 episodes): 3937.4600 best mean reward: 3977.6000 current episode reward: 5786.0000 episodes: 1673 exploration: 0.03880 learning_rate: 0.00007 elapsed time: 15852.2 seconds (4.40 hours) Timestep: 3730000 mean reward (100 episodes): 3931.9000 best mean reward: 3977.6000 current episode reward: 3060.0000 episodes: 1674 exploration: 0.03857 learning_rate: 0.00007 elapsed time: 15897.5 seconds (4.42 hours) Timestep: 3740000 mean reward (100 episodes): 4043.3200 best mean reward: 4043.3200 current episode reward: 8512.0000 episodes: 1676 exploration: 0.03835 learning_rate: 0.00007 elapsed time: 15942.8 seconds (4.43 hours) Timestep: 3750000 mean reward (100 episodes): 4017.7600 best mean reward: 4045.5600 current episode reward: 1952.0000 episodes: 1680 exploration: 0.03812 learning_rate: 0.00007 elapsed time: 15987.0 seconds (4.44 hours) Timestep: 3760000 mean reward (100 episodes): 4038.6000 best mean reward: 4045.5600 current episode reward: 6082.0000 episodes: 1682 exploration: 0.03790 learning_rate: 0.00007 elapsed time: 16033.3 seconds (4.45 hours) Timestep: 3770000 mean reward (100 episodes): 4045.3400 best mean reward: 4045.5600 current episode reward: 5378.0000 episodes: 1684 exploration: 0.03768 learning_rate: 0.00007 elapsed time: 16079.1 seconds (4.47 hours) Timestep: 3780000 mean reward (100 episodes): 4040.3200 best mean reward: 4045.5600 current episode reward: 3900.0000 episodes: 1687 exploration: 0.03745 learning_rate: 0.00007 elapsed time: 16123.5 seconds (4.48 hours) Timestep: 3790000 mean reward (100 episodes): 4046.7200 best mean reward: 4059.9200 current episode reward: 3000.0000 episodes: 1689 exploration: 0.03722 learning_rate: 0.00007 elapsed time: 16168.0 seconds (4.49 hours) Timestep: 3800000 mean reward (100 episodes): 4086.8800 best mean reward: 4086.8800 current episode reward: 6576.0000 episodes: 1691 exploration: 0.03700 learning_rate: 0.00007 elapsed time: 16212.9 seconds (4.50 hours) Timestep: 3810000 mean reward (100 episodes): 4062.3200 best mean reward: 4086.8800 current episode reward: 4960.0000 episodes: 1694 exploration: 0.03678 learning_rate: 0.00006 elapsed time: 16256.9 seconds (4.52 hours) Timestep: 3820000 mean reward (100 episodes): 4100.0600 best mean reward: 4100.0600 current episode reward: 3900.0000 episodes: 1696 exploration: 0.03655 learning_rate: 0.00006 elapsed time: 16301.2 seconds (4.53 hours) Timestep: 3830000 mean reward (100 episodes): 4082.9000 best mean reward: 4110.2000 current episode reward: 2004.0000 episodes: 1699 exploration: 0.03632 learning_rate: 0.00006 elapsed time: 16345.9 seconds (4.54 hours) Timestep: 3840000 mean reward (100 episodes): 4064.1400 best mean reward: 4110.2000 current episode reward: 3660.0000 episodes: 1702 exploration: 0.03610 learning_rate: 0.00006 elapsed time: 16390.9 seconds (4.55 hours) Timestep: 3850000 mean reward (100 episodes): 4058.4400 best mean reward: 4110.2000 current episode reward: 3900.0000 episodes: 1704 exploration: 0.03587 learning_rate: 0.00006 elapsed time: 16435.6 seconds (4.57 hours) Timestep: 3860000 mean reward (100 episodes): 4135.4400 best mean reward: 4135.4400 current episode reward: 4156.0000 episodes: 1707 exploration: 0.03565 learning_rate: 0.00006 elapsed time: 16480.9 seconds (4.58 hours) Timestep: 3870000 mean reward (100 episodes): 4150.8200 best mean reward: 4165.4600 current episode reward: 1536.0000 episodes: 1710 exploration: 0.03542 learning_rate: 0.00006 elapsed time: 16526.5 seconds (4.59 hours) Timestep: 3880000 mean reward (100 episodes): 4146.6800 best mean reward: 4165.4600 current episode reward: 4448.0000 episodes: 1712 exploration: 0.03520 learning_rate: 0.00006 elapsed time: 16571.5 seconds (4.60 hours) Timestep: 3890000 mean reward (100 episodes): 4199.0600 best mean reward: 4199.0600 current episode reward: 6894.0000 episodes: 1714 exploration: 0.03497 learning_rate: 0.00006 elapsed time: 16617.0 seconds (4.62 hours) Timestep: 3900000 mean reward (100 episodes): 4227.4400 best mean reward: 4227.4400 current episode reward: 6978.0000 episodes: 1715 exploration: 0.03475 learning_rate: 0.00006 elapsed time: 16661.8 seconds (4.63 hours) Timestep: 3910000 mean reward (100 episodes): 4259.0000 best mean reward: 4259.0000 current episode reward: 6164.0000 episodes: 1717 exploration: 0.03453 learning_rate: 0.00006 elapsed time: 16706.9 seconds (4.64 hours) Timestep: 3920000 mean reward (100 episodes): 4187.5800 best mean reward: 4259.0000 current episode reward: 2160.0000 episodes: 1720 exploration: 0.03430 learning_rate: 0.00006 elapsed time: 16751.8 seconds (4.65 hours) Timestep: 3930000 mean reward (100 episodes): 4171.6600 best mean reward: 4259.0000 current episode reward: 4350.0000 episodes: 1722 exploration: 0.03407 learning_rate: 0.00006 elapsed time: 16796.3 seconds (4.67 hours) Timestep: 3940000 mean reward (100 episodes): 4200.7800 best mean reward: 4259.0000 current episode reward: 4996.0000 episodes: 1724 exploration: 0.03385 learning_rate: 0.00006 elapsed time: 16841.3 seconds (4.68 hours) Timestep: 3950000 mean reward (100 episodes): 4214.2400 best mean reward: 4259.0000 current episode reward: 4768.0000 episodes: 1726 exploration: 0.03362 learning_rate: 0.00006 elapsed time: 16887.2 seconds (4.69 hours) Timestep: 3960000 mean reward (100 episodes): 4199.1800 best mean reward: 4259.0000 current episode reward: 4092.0000 episodes: 1728 exploration: 0.03340 learning_rate: 0.00006 elapsed time: 16931.6 seconds (4.70 hours) Timestep: 3970000 mean reward (100 episodes): 4241.4600 best mean reward: 4259.0000 current episode reward: 3900.0000 episodes: 1731 exploration: 0.03317 learning_rate: 0.00006 elapsed time: 16976.5 seconds (4.72 hours) Timestep: 3980000 mean reward (100 episodes): 4270.8600 best mean reward: 4270.8600 current episode reward: 6780.0000 episodes: 1732 exploration: 0.03295 learning_rate: 0.00006 elapsed time: 17022.1 seconds (4.73 hours) Timestep: 3990000 mean reward (100 episodes): 4341.7600 best mean reward: 4341.7600 current episode reward: 4220.0000 episodes: 1734 exploration: 0.03272 learning_rate: 0.00006 elapsed time: 17066.8 seconds (4.74 hours) Timestep: 4000000 mean reward (100 episodes): 4367.6000 best mean reward: 4367.6000 current episode reward: 3720.0000 episodes: 1737 exploration: 0.03250 learning_rate: 0.00006 elapsed time: 17112.4 seconds (4.75 hours) Timestep: 4010000 mean reward (100 episodes): 4378.9400 best mean reward: 4379.2400 current episode reward: 4670.0000 episodes: 1739 exploration: 0.03227 learning_rate: 0.00006 elapsed time: 17157.9 seconds (4.77 hours) Timestep: 4020000 mean reward (100 episodes): 4393.4800 best mean reward: 4417.1400 current episode reward: 2944.0000 episodes: 1741 exploration: 0.03205 learning_rate: 0.00006 elapsed time: 17203.2 seconds (4.78 hours) Timestep: 4030000 mean reward (100 episodes): 4423.6000 best mean reward: 4423.6000 current episode reward: 5440.0000 episodes: 1743 exploration: 0.03183 learning_rate: 0.00006 elapsed time: 17247.8 seconds (4.79 hours) Timestep: 4040000 mean reward (100 episodes): 4413.9800 best mean reward: 4424.5800 current episode reward: 4220.0000 episodes: 1745 exploration: 0.03160 learning_rate: 0.00006 elapsed time: 17293.0 seconds (4.80 hours) Timestep: 4050000 mean reward (100 episodes): 4435.3800 best mean reward: 4435.3800 current episode reward: 5952.0000 episodes: 1747 exploration: 0.03138 learning_rate: 0.00006 elapsed time: 17337.7 seconds (4.82 hours) Timestep: 4060000 mean reward (100 episodes): 4485.5600 best mean reward: 4485.5600 current episode reward: 5064.0000 episodes: 1750 exploration: 0.03115 learning_rate: 0.00006 elapsed time: 17382.4 seconds (4.83 hours) Timestep: 4070000 mean reward (100 episodes): 4522.8600 best mean reward: 4522.8600 current episode reward: 6562.0000 episodes: 1751 exploration: 0.03092 learning_rate: 0.00006 elapsed time: 17428.1 seconds (4.84 hours) Timestep: 4080000 mean reward (100 episodes): 4580.2400 best mean reward: 4580.2400 current episode reward: 6840.0000 episodes: 1753 exploration: 0.03070 learning_rate: 0.00006 elapsed time: 17473.2 seconds (4.85 hours) Timestep: 4090000 mean reward (100 episodes): 4625.4200 best mean reward: 4625.4200 current episode reward: 4832.0000 episodes: 1755 exploration: 0.03048 learning_rate: 0.00006 elapsed time: 17518.7 seconds (4.87 hours) Timestep: 4100000 mean reward (100 episodes): 4581.6000 best mean reward: 4625.4200 current episode reward: 2160.0000 episodes: 1758 exploration: 0.03025 learning_rate: 0.00006 elapsed time: 17564.4 seconds (4.88 hours) Timestep: 4110000 mean reward (100 episodes): 4597.2800 best mean reward: 4625.4200 current episode reward: 5084.0000 episodes: 1760 exploration: 0.03002 learning_rate: 0.00006 elapsed time: 17609.6 seconds (4.89 hours) Timestep: 4120000 mean reward (100 episodes): 4591.1600 best mean reward: 4625.4200 current episode reward: 4410.0000 episodes: 1762 exploration: 0.02980 learning_rate: 0.00006 elapsed time: 17655.1 seconds (4.90 hours) Timestep: 4130000 mean reward (100 episodes): 4611.1000 best mean reward: 4625.4200 current episode reward: 6780.0000 episodes: 1764 exploration: 0.02958 learning_rate: 0.00006 elapsed time: 17700.2 seconds (4.92 hours) Timestep: 4140000 mean reward (100 episodes): 4646.6800 best mean reward: 4646.6800 current episode reward: 7834.0000 episodes: 1766 exploration: 0.02935 learning_rate: 0.00006 elapsed time: 17745.7 seconds (4.93 hours) Timestep: 4150000 mean reward (100 episodes): 4655.6000 best mean reward: 4655.6000 current episode reward: 4576.0000 episodes: 1768 exploration: 0.02912 learning_rate: 0.00006 elapsed time: 17790.7 seconds (4.94 hours) Timestep: 4160000 mean reward (100 episodes): 4689.7400 best mean reward: 4689.7400 current episode reward: 8214.0000 episodes: 1770 exploration: 0.02890 learning_rate: 0.00006 elapsed time: 17834.9 seconds (4.95 hours) Timestep: 4170000 mean reward (100 episodes): 4685.8200 best mean reward: 4713.7200 current episode reward: 3240.0000 episodes: 1773 exploration: 0.02867 learning_rate: 0.00006 elapsed time: 17879.9 seconds (4.97 hours) Timestep: 4180000 mean reward (100 episodes): 4641.6400 best mean reward: 4713.7200 current episode reward: 4092.0000 episodes: 1775 exploration: 0.02845 learning_rate: 0.00006 elapsed time: 17925.6 seconds (4.98 hours) Timestep: 4190000 mean reward (100 episodes): 4628.2400 best mean reward: 4713.7200 current episode reward: 4704.0000 episodes: 1777 exploration: 0.02823 learning_rate: 0.00006 elapsed time: 17970.6 seconds (4.99 hours) Timestep: 4200000 mean reward (100 episodes): 4694.8400 best mean reward: 4713.7200 current episode reward: 5408.0000 episodes: 1779 exploration: 0.02800 learning_rate: 0.00006 elapsed time: 18015.7 seconds (5.00 hours) Timestep: 4210000 mean reward (100 episodes): 4718.8000 best mean reward: 4739.1200 current episode reward: 4050.0000 episodes: 1782 exploration: 0.02777 learning_rate: 0.00006 elapsed time: 18061.0 seconds (5.02 hours) Timestep: 4220000 mean reward (100 episodes): 4743.8200 best mean reward: 4743.8200 current episode reward: 6342.0000 episodes: 1783 exploration: 0.02755 learning_rate: 0.00006 elapsed time: 18106.1 seconds (5.03 hours) Timestep: 4230000 mean reward (100 episodes): 4843.8200 best mean reward: 4843.8200 current episode reward: 6728.0000 episodes: 1785 exploration: 0.02733 learning_rate: 0.00006 elapsed time: 18151.3 seconds (5.04 hours) Timestep: 4240000 mean reward (100 episodes): 4868.9400 best mean reward: 4873.7400 current episode reward: 5700.0000 episodes: 1788 exploration: 0.02710 learning_rate: 0.00006 elapsed time: 18196.5 seconds (5.05 hours) Timestep: 4250000 mean reward (100 episodes): 4917.0200 best mean reward: 4917.0200 current episode reward: 6810.0000 episodes: 1790 exploration: 0.02687 learning_rate: 0.00006 elapsed time: 18241.3 seconds (5.07 hours) Timestep: 4260000 mean reward (100 episodes): 4937.8600 best mean reward: 4937.8600 current episode reward: 8660.0000 episodes: 1791 exploration: 0.02665 learning_rate: 0.00006 elapsed time: 18286.7 seconds (5.08 hours) Timestep: 4270000 mean reward (100 episodes): 4950.8600 best mean reward: 4950.8600 current episode reward: 5744.0000 episodes: 1793 exploration: 0.02642 learning_rate: 0.00006 elapsed time: 18332.0 seconds (5.09 hours) Timestep: 4280000 mean reward (100 episodes): 4941.8800 best mean reward: 4950.8600 current episode reward: 4260.0000 episodes: 1795 exploration: 0.02620 learning_rate: 0.00006 elapsed time: 18377.4 seconds (5.10 hours) Timestep: 4290000 mean reward (100 episodes): 4975.3000 best mean reward: 4975.3000 current episode reward: 5926.0000 episodes: 1797 exploration: 0.02597 learning_rate: 0.00006 elapsed time: 18423.2 seconds (5.12 hours) Timestep: 4300000 mean reward (100 episodes): 5046.3600 best mean reward: 5046.3600 current episode reward: 4200.0000 episodes: 1799 exploration: 0.02575 learning_rate: 0.00006 elapsed time: 18468.7 seconds (5.13 hours) Timestep: 4310000 mean reward (100 episodes): 5088.2400 best mean reward: 5088.2400 current episode reward: 2832.0000 episodes: 1801 exploration: 0.02552 learning_rate: 0.00006 elapsed time: 18513.8 seconds (5.14 hours) Timestep: 4320000 mean reward (100 episodes): 5159.4400 best mean reward: 5159.4400 current episode reward: 5200.0000 episodes: 1803 exploration: 0.02530 learning_rate: 0.00006 elapsed time: 18558.2 seconds (5.16 hours) Timestep: 4330000 mean reward (100 episodes): 5175.3200 best mean reward: 5179.7800 current episode reward: 5054.0000 episodes: 1805 exploration: 0.02508 learning_rate: 0.00006 elapsed time: 18603.7 seconds (5.17 hours) Timestep: 4340000 mean reward (100 episodes): 5210.8400 best mean reward: 5210.8400 current episode reward: 5820.0000 episodes: 1807 exploration: 0.02485 learning_rate: 0.00006 elapsed time: 18649.2 seconds (5.18 hours) Timestep: 4350000 mean reward (100 episodes): 5216.5600 best mean reward: 5217.4800 current episode reward: 5378.0000 episodes: 1809 exploration: 0.02462 learning_rate: 0.00006 elapsed time: 18694.2 seconds (5.19 hours) Timestep: 4360000 mean reward (100 episodes): 5267.9000 best mean reward: 5267.9000 current episode reward: 5484.0000 episodes: 1811 exploration: 0.02440 learning_rate: 0.00006 elapsed time: 18738.6 seconds (5.21 hours) Timestep: 4370000 mean reward (100 episodes): 5342.8200 best mean reward: 5342.8200 current episode reward: 10302.0000 episodes: 1813 exploration: 0.02417 learning_rate: 0.00006 elapsed time: 18784.3 seconds (5.22 hours) Timestep: 4380000 mean reward (100 episodes): 5258.9600 best mean reward: 5342.8200 current episode reward: 1900.0000 episodes: 1816 exploration: 0.02395 learning_rate: 0.00006 elapsed time: 18829.5 seconds (5.23 hours) Timestep: 4390000 mean reward (100 episodes): 5283.2000 best mean reward: 5342.8200 current episode reward: 7182.0000 episodes: 1818 exploration: 0.02372 learning_rate: 0.00006 elapsed time: 18874.9 seconds (5.24 hours) Timestep: 4400000 mean reward (100 episodes): 5305.2000 best mean reward: 5342.8200 current episode reward: 6100.0000 episodes: 1819 exploration: 0.02350 learning_rate: 0.00006 elapsed time: 18919.7 seconds (5.26 hours) Timestep: 4410000 mean reward (100 episodes): 5366.6200 best mean reward: 5374.6000 current episode reward: 4542.0000 episodes: 1821 exploration: 0.02327 learning_rate: 0.00006 elapsed time: 18964.6 seconds (5.27 hours) Timestep: 4420000 mean reward (100 episodes): 5359.5600 best mean reward: 5374.6000 current episode reward: 4156.0000 episodes: 1823 exploration: 0.02305 learning_rate: 0.00006 elapsed time: 19010.1 seconds (5.28 hours) Timestep: 4430000 mean reward (100 episodes): 5412.8000 best mean reward: 5412.8000 current episode reward: 10320.0000 episodes: 1824 exploration: 0.02282 learning_rate: 0.00006 elapsed time: 19054.5 seconds (5.29 hours) Timestep: 4440000 mean reward (100 episodes): 5429.3200 best mean reward: 5442.4000 current episode reward: 4732.0000 episodes: 1827 exploration: 0.02260 learning_rate: 0.00006 elapsed time: 19100.2 seconds (5.31 hours) Timestep: 4450000 mean reward (100 episodes): 5481.8000 best mean reward: 5481.8000 current episode reward: 7800.0000 episodes: 1829 exploration: 0.02237 learning_rate: 0.00006 elapsed time: 19144.8 seconds (5.32 hours) Timestep: 4460000 mean reward (100 episodes): 5531.2200 best mean reward: 5536.0200 current episode reward: 3420.0000 episodes: 1831 exploration: 0.02215 learning_rate: 0.00006 elapsed time: 19189.9 seconds (5.33 hours) Timestep: 4470000 mean reward (100 episodes): 5491.9400 best mean reward: 5536.0200 current episode reward: 5602.0000 episodes: 1833 exploration: 0.02192 learning_rate: 0.00006 elapsed time: 19235.0 seconds (5.34 hours) Timestep: 4480000 mean reward (100 episodes): 5512.6800 best mean reward: 5536.0200 current episode reward: 4860.0000 episodes: 1835 exploration: 0.02170 learning_rate: 0.00006 elapsed time: 19280.7 seconds (5.36 hours) Timestep: 4490000 mean reward (100 episodes): 5552.7600 best mean reward: 5552.7600 current episode reward: 5540.0000 episodes: 1837 exploration: 0.02147 learning_rate: 0.00006 elapsed time: 19326.0 seconds (5.37 hours) Timestep: 4500000 mean reward (100 episodes): 5510.5800 best mean reward: 5552.7600 current episode reward: 5340.0000 episodes: 1839 exploration: 0.02125 learning_rate: 0.00006 elapsed time: 19371.4 seconds (5.38 hours) Timestep: 4510000 mean reward (100 episodes): 5544.8600 best mean reward: 5552.7600 current episode reward: 9486.0000 episodes: 1841 exploration: 0.02103 learning_rate: 0.00006 elapsed time: 19416.3 seconds (5.39 hours) Timestep: 4520000 mean reward (100 episodes): 5528.9400 best mean reward: 5552.7600 current episode reward: 6732.0000 episodes: 1843 exploration: 0.02080 learning_rate: 0.00006 elapsed time: 19461.1 seconds (5.41 hours) Timestep: 4530000 mean reward (100 episodes): 5551.9600 best mean reward: 5552.7600 current episode reward: 5744.0000 episodes: 1846 exploration: 0.02057 learning_rate: 0.00006 elapsed time: 19506.8 seconds (5.42 hours) Timestep: 4540000 mean reward (100 episodes): 5526.4600 best mean reward: 5552.7600 current episode reward: 4560.0000 episodes: 1848 exploration: 0.02035 learning_rate: 0.00006 elapsed time: 19551.7 seconds (5.43 hours) Timestep: 4550000 mean reward (100 episodes): 5532.4600 best mean reward: 5552.7600 current episode reward: 4320.0000 episodes: 1849 exploration: 0.02013 learning_rate: 0.00006 elapsed time: 19596.1 seconds (5.44 hours) Timestep: 4560000 mean reward (100 episodes): 5588.2400 best mean reward: 5603.6200 current episode reward: 5024.0000 episodes: 1851 exploration: 0.01990 learning_rate: 0.00006 elapsed time: 19641.5 seconds (5.46 hours) Timestep: 4570000 mean reward (100 episodes): 5584.2000 best mean reward: 5603.6200 current episode reward: 7164.0000 episodes: 1852 exploration: 0.01967 learning_rate: 0.00006 elapsed time: 19686.5 seconds (5.47 hours) Timestep: 4580000 mean reward (100 episodes): 5593.0600 best mean reward: 5610.3800 current episode reward: 6300.0000 episodes: 1854 exploration: 0.01945 learning_rate: 0.00006 elapsed time: 19731.5 seconds (5.48 hours) Timestep: 4590000 mean reward (100 episodes): 5625.1600 best mean reward: 5625.1600 current episode reward: 3060.0000 episodes: 1856 exploration: 0.01923 learning_rate: 0.00006 elapsed time: 19776.4 seconds (5.49 hours) Timestep: 4600000 mean reward (100 episodes): 5637.3000 best mean reward: 5637.3000 current episode reward: 5154.0000 episodes: 1859 exploration: 0.01900 learning_rate: 0.00006 elapsed time: 19821.3 seconds (5.51 hours) Timestep: 4610000 mean reward (100 episodes): 5633.4000 best mean reward: 5639.2600 current episode reward: 4478.0000 episodes: 1861 exploration: 0.01878 learning_rate: 0.00005 elapsed time: 19866.2 seconds (5.52 hours) Timestep: 4620000 mean reward (100 episodes): 5682.3000 best mean reward: 5687.9000 current episode reward: 4640.0000 episodes: 1863 exploration: 0.01855 learning_rate: 0.00005 elapsed time: 19912.0 seconds (5.53 hours) Timestep: 4630000 mean reward (100 episodes): 5701.6200 best mean reward: 5701.6200 current episode reward: 8712.0000 episodes: 1864 exploration: 0.01832 learning_rate: 0.00005 elapsed time: 19958.1 seconds (5.54 hours) Timestep: 4640000 mean reward (100 episodes): 5699.1400 best mean reward: 5725.3200 current episode reward: 5216.0000 episodes: 1866 exploration: 0.01810 learning_rate: 0.00005 elapsed time: 20003.8 seconds (5.56 hours) Timestep: 4650000 mean reward (100 episodes): 5742.2200 best mean reward: 5742.2200 current episode reward: 5700.0000 episodes: 1869 exploration: 0.01788 learning_rate: 0.00005 elapsed time: 20048.9 seconds (5.57 hours) Timestep: 4660000 mean reward (100 episodes): 5715.0600 best mean reward: 5742.2200 current episode reward: 5756.0000 episodes: 1871 exploration: 0.01765 learning_rate: 0.00005 elapsed time: 20094.9 seconds (5.58 hours) Timestep: 4670000 mean reward (100 episodes): 5762.2800 best mean reward: 5762.2800 current episode reward: 6062.0000 episodes: 1873 exploration: 0.01742 learning_rate: 0.00005 elapsed time: 20140.4 seconds (5.59 hours) Timestep: 4680000 mean reward (100 episodes): 5780.3800 best mean reward: 5780.3800 current episode reward: 5410.0000 episodes: 1874 exploration: 0.01720 learning_rate: 0.00005 elapsed time: 20185.9 seconds (5.61 hours) Timestep: 4690000 mean reward (100 episodes): 5826.4600 best mean reward: 5881.3400 current episode reward: 1796.0000 episodes: 1877 exploration: 0.01697 learning_rate: 0.00005 elapsed time: 20230.8 seconds (5.62 hours) Timestep: 4700000 mean reward (100 episodes): 5834.4400 best mean reward: 5881.3400 current episode reward: 7098.0000 episodes: 1878 exploration: 0.01675 learning_rate: 0.00005 elapsed time: 20276.2 seconds (5.63 hours) Timestep: 4710000 mean reward (100 episodes): 5843.0800 best mean reward: 5881.3400 current episode reward: 5280.0000 episodes: 1881 exploration: 0.01652 learning_rate: 0.00005 elapsed time: 20321.2 seconds (5.64 hours) Timestep: 4720000 mean reward (100 episodes): 5816.5800 best mean reward: 5881.3400 current episode reward: 2552.0000 episodes: 1883 exploration: 0.01630 learning_rate: 0.00005 elapsed time: 20366.0 seconds (5.66 hours) Timestep: 4730000 mean reward (100 episodes): 5783.9600 best mean reward: 5881.3400 current episode reward: 6096.0000 episodes: 1885 exploration: 0.01607 learning_rate: 0.00005 elapsed time: 20411.4 seconds (5.67 hours) Timestep: 4740000 mean reward (100 episodes): 5790.5800 best mean reward: 5881.3400 current episode reward: 4796.0000 episodes: 1887 exploration: 0.01585 learning_rate: 0.00005 elapsed time: 20456.4 seconds (5.68 hours) Timestep: 4750000 mean reward (100 episodes): 5786.0800 best mean reward: 5881.3400 current episode reward: 4576.0000 episodes: 1889 exploration: 0.01562 learning_rate: 0.00005 elapsed time: 20502.6 seconds (5.70 hours) Timestep: 4760000 mean reward (100 episodes): 5785.3000 best mean reward: 5881.3400 current episode reward: 6732.0000 episodes: 1890 exploration: 0.01540 learning_rate: 0.00005 elapsed time: 20547.6 seconds (5.71 hours) Timestep: 4770000 mean reward (100 episodes): 5876.4000 best mean reward: 5881.3400 current episode reward: 5880.0000 episodes: 1892 exploration: 0.01517 learning_rate: 0.00005 elapsed time: 20592.6 seconds (5.72 hours) Timestep: 4780000 mean reward (100 episodes): 5871.4800 best mean reward: 5881.3400 current episode reward: 5280.0000 episodes: 1894 exploration: 0.01495 learning_rate: 0.00005 elapsed time: 20638.4 seconds (5.73 hours) Timestep: 4790000 mean reward (100 episodes): 5914.6400 best mean reward: 5927.1800 current episode reward: 4412.0000 episodes: 1896 exploration: 0.01472 learning_rate: 0.00005 elapsed time: 20684.0 seconds (5.75 hours) Timestep: 4800000 mean reward (100 episodes): 5885.0600 best mean reward: 5927.1800 current episode reward: 3300.0000 episodes: 1898 exploration: 0.01450 learning_rate: 0.00005 elapsed time: 20729.7 seconds (5.76 hours) Timestep: 4810000 mean reward (100 episodes): 5907.4600 best mean reward: 5927.1800 current episode reward: 10230.0000 episodes: 1900 exploration: 0.01427 learning_rate: 0.00005 elapsed time: 20775.7 seconds (5.77 hours) Timestep: 4820000 mean reward (100 episodes): 5930.6800 best mean reward: 5930.6800 current episode reward: 5154.0000 episodes: 1901 exploration: 0.01405 learning_rate: 0.00005 elapsed time: 20820.8 seconds (5.78 hours) Timestep: 4830000 mean reward (100 episodes): 5914.8200 best mean reward: 5942.7600 current episode reward: 3140.0000 episodes: 1904 exploration: 0.01382 learning_rate: 0.00005 elapsed time: 20866.0 seconds (5.80 hours) Timestep: 4840000 mean reward (100 episodes): 5902.0400 best mean reward: 5942.7600 current episode reward: 3360.0000 episodes: 1906 exploration: 0.01360 learning_rate: 0.00005 elapsed time: 20911.7 seconds (5.81 hours) Timestep: 4850000 mean reward (100 episodes): 5865.5600 best mean reward: 5942.7600 current episode reward: 3000.0000 episodes: 1908 exploration: 0.01337 learning_rate: 0.00005 elapsed time: 20957.1 seconds (5.82 hours) Timestep: 4860000 mean reward (100 episodes): 5867.4000 best mean reward: 5942.7600 current episode reward: 5246.0000 episodes: 1910 exploration: 0.01315 learning_rate: 0.00005 elapsed time: 21002.2 seconds (5.83 hours) Timestep: 4870000 mean reward (100 episodes): 5814.4000 best mean reward: 5942.7600 current episode reward: 4448.0000 episodes: 1913 exploration: 0.01292 learning_rate: 0.00005 elapsed time: 21047.3 seconds (5.85 hours) Timestep: 4880000 mean reward (100 episodes): 5855.0800 best mean reward: 5942.7600 current episode reward: 8224.0000 episodes: 1914 exploration: 0.01270 learning_rate: 0.00005 elapsed time: 21092.6 seconds (5.86 hours) Timestep: 4890000 mean reward (100 episodes): 5963.2600 best mean reward: 5963.2600 current episode reward: 8024.0000 episodes: 1916 exploration: 0.01247 learning_rate: 0.00005 elapsed time: 21137.7 seconds (5.87 hours) Timestep: 4900000 mean reward (100 episodes): 5949.6000 best mean reward: 5963.2600 current episode reward: 5820.0000 episodes: 1918 exploration: 0.01225 learning_rate: 0.00005 elapsed time: 21182.8 seconds (5.88 hours) Timestep: 4910000 mean reward (100 episodes): 5856.1400 best mean reward: 5963.2600 current episode reward: 3600.0000 episodes: 1921 exploration: 0.01202 learning_rate: 0.00005 elapsed time: 21227.8 seconds (5.90 hours) Timestep: 4920000 mean reward (100 episodes): 5854.2000 best mean reward: 5963.2600 current episode reward: 6002.0000 episodes: 1923 exploration: 0.01180 learning_rate: 0.00005 elapsed time: 21272.9 seconds (5.91 hours) Timestep: 4930000 mean reward (100 episodes): 5786.5000 best mean reward: 5963.2600 current episode reward: 3840.0000 episodes: 1925 exploration: 0.01157 learning_rate: 0.00005 elapsed time: 21318.1 seconds (5.92 hours) Timestep: 4940000 mean reward (100 episodes): 5831.2200 best mean reward: 5963.2600 current episode reward: 5340.0000 episodes: 1927 exploration: 0.01135 learning_rate: 0.00005 elapsed time: 21363.9 seconds (5.93 hours) Timestep: 4950000 mean reward (100 episodes): 5846.7800 best mean reward: 5963.2600 current episode reward: 7188.0000 episodes: 1928 exploration: 0.01112 learning_rate: 0.00005 elapsed time: 21408.5 seconds (5.95 hours) Timestep: 4960000 mean reward (100 episodes): 5866.6000 best mean reward: 5963.2600 current episode reward: 5280.0000 episodes: 1930 exploration: 0.01090 learning_rate: 0.00005 elapsed time: 21453.5 seconds (5.96 hours) Timestep: 4970000 mean reward (100 episodes): 5930.1400 best mean reward: 5963.2600 current episode reward: 6096.0000 episodes: 1932 exploration: 0.01067 learning_rate: 0.00005 elapsed time: 21498.7 seconds (5.97 hours) Timestep: 4980000 mean reward (100 episodes): 5962.8200 best mean reward: 5963.2600 current episode reward: 8870.0000 episodes: 1933 exploration: 0.01045 learning_rate: 0.00005 elapsed time: 21544.1 seconds (5.98 hours) Timestep: 4990000 mean reward (100 episodes): 5947.0800 best mean reward: 5963.2600 current episode reward: 5152.0000 episodes: 1935 exploration: 0.01022 learning_rate: 0.00005 elapsed time: 21589.3 seconds (6.00 hours) Timestep: 5000000 mean reward (100 episodes): 5961.7400 best mean reward: 5975.5800 current episode reward: 4156.0000 episodes: 1937 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 21634.4 seconds (6.01 hours) Timestep: 5010000 mean reward (100 episodes): 5998.3800 best mean reward: 5998.3800 current episode reward: 5824.0000 episodes: 1938 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 21679.3 seconds (6.02 hours) Timestep: 5020000 mean reward (100 episodes): 5998.7800 best mean reward: 6041.5200 current episode reward: 5212.0000 episodes: 1941 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 21723.8 seconds (6.03 hours) Timestep: 5030000 mean reward (100 episodes): 6043.2400 best mean reward: 6043.2400 current episode reward: 7446.0000 episodes: 1942 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 21768.5 seconds (6.05 hours) Timestep: 5040000 mean reward (100 episodes): 6072.0600 best mean reward: 6072.0600 current episode reward: 4508.0000 episodes: 1944 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 21813.7 seconds (6.06 hours) Timestep: 5050000 mean reward (100 episodes): 6091.6000 best mean reward: 6110.6400 current episode reward: 3840.0000 episodes: 1946 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 21858.7 seconds (6.07 hours) Timestep: 5060000 mean reward (100 episodes): 6095.9200 best mean reward: 6110.6400 current episode reward: 5200.0000 episodes: 1947 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 21903.9 seconds (6.08 hours) Timestep: 5070000 mean reward (100 episodes): 6153.2200 best mean reward: 6153.2200 current episode reward: 10290.0000 episodes: 1948 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 21950.1 seconds (6.10 hours) Timestep: 5080000 mean reward (100 episodes): 6159.4000 best mean reward: 6245.2600 current episode reward: 3240.0000 episodes: 1951 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 21995.6 seconds (6.11 hours) Timestep: 5090000 mean reward (100 episodes): 6174.3600 best mean reward: 6245.2600 current episode reward: 8660.0000 episodes: 1952 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22041.1 seconds (6.12 hours) Timestep: 5100000 mean reward (100 episodes): 6159.7600 best mean reward: 6245.2600 current episode reward: 7998.0000 episodes: 1953 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22086.0 seconds (6.14 hours) Timestep: 5110000 mean reward (100 episodes): 6218.2400 best mean reward: 6245.2600 current episode reward: 9960.0000 episodes: 1955 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22131.1 seconds (6.15 hours) Timestep: 5120000 mean reward (100 episodes): 6290.2400 best mean reward: 6290.2400 current episode reward: 10260.0000 episodes: 1956 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22177.1 seconds (6.16 hours) Timestep: 5130000 mean reward (100 episodes): 6394.8600 best mean reward: 6394.8600 current episode reward: 6852.0000 episodes: 1958 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22222.9 seconds (6.17 hours) Timestep: 5140000 mean reward (100 episodes): 6442.0200 best mean reward: 6442.0200 current episode reward: 9870.0000 episodes: 1959 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22267.9 seconds (6.19 hours) Timestep: 5150000 mean reward (100 episodes): 6496.6800 best mean reward: 6496.6800 current episode reward: 9030.0000 episodes: 1961 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22313.5 seconds (6.20 hours) Timestep: 5160000 mean reward (100 episodes): 6484.3600 best mean reward: 6496.6800 current episode reward: 8628.0000 episodes: 1962 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22359.0 seconds (6.21 hours) Timestep: 5170000 mean reward (100 episodes): 6525.5400 best mean reward: 6525.5400 current episode reward: 12510.0000 episodes: 1964 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22405.0 seconds (6.22 hours) Timestep: 5180000 mean reward (100 episodes): 6534.8400 best mean reward: 6534.8400 current episode reward: 8232.0000 episodes: 1966 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22450.5 seconds (6.24 hours) Timestep: 5190000 mean reward (100 episodes): 6543.5800 best mean reward: 6543.5800 current episode reward: 6456.0000 episodes: 1967 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22497.0 seconds (6.25 hours) Timestep: 5200000 mean reward (100 episodes): 6611.6400 best mean reward: 6611.6400 current episode reward: 8388.0000 episodes: 1969 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22542.0 seconds (6.26 hours) Timestep: 5210000 mean reward (100 episodes): 6638.9000 best mean reward: 6638.9000 current episode reward: 7128.0000 episodes: 1971 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22587.3 seconds (6.27 hours) Timestep: 5220000 mean reward (100 episodes): 6654.6200 best mean reward: 6654.6200 current episode reward: 7192.0000 episodes: 1972 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22633.7 seconds (6.29 hours) Timestep: 5230000 mean reward (100 episodes): 6687.8600 best mean reward: 6687.8600 current episode reward: 7112.0000 episodes: 1974 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22679.3 seconds (6.30 hours) Timestep: 5240000 mean reward (100 episodes): 6594.3000 best mean reward: 6687.8600 current episode reward: 4832.0000 episodes: 1975 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22724.6 seconds (6.31 hours) Timestep: 5250000 mean reward (100 episodes): 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learning_rate: 0.00005 elapsed time: 22950.5 seconds (6.38 hours) Timestep: 5300000 mean reward (100 episodes): 6886.1200 best mean reward: 6886.1200 current episode reward: 9390.0000 episodes: 1986 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22995.7 seconds (6.39 hours) Timestep: 5310000 mean reward (100 episodes): 6897.4400 best mean reward: 6897.4400 current episode reward: 7620.0000 episodes: 1988 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23041.5 seconds (6.40 hours) Timestep: 5320000 mean reward (100 episodes): 6916.1800 best mean reward: 6928.1200 current episode reward: 5538.0000 episodes: 1990 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23086.6 seconds (6.41 hours) Timestep: 5330000 mean reward (100 episodes): 6797.7400 best mean reward: 6928.1200 current episode reward: 6810.0000 episodes: 1992 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23131.6 seconds (6.43 hours) Timestep: 5340000 mean reward (100 episodes): 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6987.9800 best mean reward: 7018.8400 current episode reward: 2160.0000 episodes: 2010 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23584.8 seconds (6.55 hours) Timestep: 5440000 mean reward (100 episodes): 7002.6200 best mean reward: 7018.8400 current episode reward: 8820.0000 episodes: 2011 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23630.1 seconds (6.56 hours) Timestep: 5450000 mean reward (100 episodes): 7096.1400 best mean reward: 7096.1400 current episode reward: 5130.0000 episodes: 2013 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23675.2 seconds (6.58 hours) Timestep: 5460000 mean reward (100 episodes): 7059.2000 best mean reward: 7096.1400 current episode reward: 8584.0000 episodes: 2015 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23720.0 seconds (6.59 hours) Timestep: 5470000 mean reward (100 episodes): 7073.6000 best mean reward: 7096.1400 current episode reward: 8482.0000 episodes: 2017 exploration: 0.01000 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7866.4400 best mean reward: 8318.5800 current episode reward: 7692.0000 episodes: 2168 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28059.8 seconds (7.79 hours) Timestep: 6430000 mean reward (100 episodes): 7929.8000 best mean reward: 8318.5800 current episode reward: 9396.0000 episodes: 2170 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28105.3 seconds (7.81 hours) Timestep: 6440000 mean reward (100 episodes): 7996.6400 best mean reward: 8318.5800 current episode reward: 12924.0000 episodes: 2171 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28151.6 seconds (7.82 hours) Timestep: 6450000 mean reward (100 episodes): 7971.5800 best mean reward: 8318.5800 current episode reward: 6546.0000 episodes: 2173 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28197.4 seconds (7.83 hours) Timestep: 6460000 mean reward (100 episodes): 8042.2200 best mean reward: 8318.5800 current episode reward: 13312.0000 episodes: 2174 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28243.4 seconds (7.85 hours) Timestep: 6470000 mean reward (100 episodes): 8100.6200 best mean reward: 8318.5800 current episode reward: 9804.0000 episodes: 2175 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28289.1 seconds (7.86 hours) Timestep: 6480000 mean reward (100 episodes): 7991.6000 best mean reward: 8318.5800 current episode reward: 4680.0000 episodes: 2177 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28334.6 seconds (7.87 hours) Timestep: 6490000 mean reward (100 episodes): 8020.2400 best mean reward: 8318.5800 current episode reward: 8720.0000 episodes: 2179 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28379.7 seconds (7.88 hours) Timestep: 6500000 mean reward (100 episodes): 8088.0800 best mean reward: 8318.5800 current episode reward: 15444.0000 episodes: 2180 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28424.8 seconds (7.90 hours) Timestep: 6510000 mean reward (100 episodes): 8186.5000 best mean reward: 8318.5800 current episode reward: 12842.0000 episodes: 2181 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28470.7 seconds (7.91 hours) Timestep: 6520000 mean reward (100 episodes): 8284.7800 best mean reward: 8318.5800 current episode reward: 5070.0000 episodes: 2183 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28516.4 seconds (7.92 hours) Timestep: 6530000 mean reward (100 episodes): 8277.8000 best mean reward: 8318.5800 current episode reward: 9716.0000 episodes: 2185 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28561.1 seconds (7.93 hours) Timestep: 6540000 mean reward (100 episodes): 8274.1400 best mean reward: 8318.5800 current episode reward: 6660.0000 episodes: 2186 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28606.2 seconds (7.95 hours) Timestep: 6550000 mean reward (100 episodes): 8284.1600 best mean reward: 8318.5800 current episode reward: 7652.0000 episodes: 2188 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28651.5 seconds (7.96 hours) Timestep: 6560000 mean reward (100 episodes): 8304.7800 best mean reward: 8318.5800 current episode reward: 7470.0000 episodes: 2189 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28696.2 seconds (7.97 hours) Timestep: 6570000 mean reward (100 episodes): 8302.2800 best mean reward: 8318.5800 current episode reward: 9640.0000 episodes: 2191 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28741.1 seconds (7.98 hours) Timestep: 6580000 mean reward (100 episodes): 8274.1200 best mean reward: 8318.5800 current episode reward: 6096.0000 episodes: 2193 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28786.2 seconds (8.00 hours) Timestep: 6590000 mean reward (100 episodes): 8261.6000 best mean reward: 8318.5800 current episode reward: 7920.0000 episodes: 2195 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28831.5 seconds (8.01 hours) Timestep: 6600000 mean reward (100 episodes): 8336.0000 best mean reward: 8336.0000 current episode reward: 18930.0000 episodes: 2196 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28876.9 seconds (8.02 hours) Timestep: 6610000 mean reward (100 episodes): 8327.2800 best mean reward: 8336.0000 current episode reward: 5636.0000 episodes: 2197 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28921.4 seconds (8.03 hours) Timestep: 6620000 mean reward (100 episodes): 8324.1400 best mean reward: 8336.0000 current episode reward: 7692.0000 episodes: 2199 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28967.1 seconds (8.05 hours) Timestep: 6630000 mean reward (100 episodes): 8294.5400 best mean reward: 8345.0800 current episode reward: 6126.0000 episodes: 2201 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29011.8 seconds (8.06 hours) Timestep: 6640000 mean reward (100 episodes): 8303.9600 best mean reward: 8345.0800 current episode reward: 8886.0000 episodes: 2203 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29057.0 seconds (8.07 hours) Timestep: 6650000 mean reward (100 episodes): 8299.0400 best mean reward: 8345.0800 current episode reward: 10200.0000 episodes: 2204 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29102.5 seconds (8.08 hours) Timestep: 6660000 mean reward (100 episodes): 8248.5200 best mean reward: 8345.0800 current episode reward: 9010.0000 episodes: 2206 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29147.7 seconds (8.10 hours) Timestep: 6670000 mean reward (100 episodes): 8284.9800 best mean reward: 8345.0800 current episode reward: 11946.0000 episodes: 2207 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29192.2 seconds (8.11 hours) Timestep: 6680000 mean reward (100 episodes): 8276.8000 best mean reward: 8345.0800 current episode reward: 5440.0000 episodes: 2209 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29237.4 seconds (8.12 hours) Timestep: 6690000 mean reward (100 episodes): 8249.1000 best mean reward: 8345.0800 current episode reward: 4606.0000 episodes: 2211 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29282.1 seconds (8.13 hours) Timestep: 6700000 mean reward (100 episodes): 8279.5400 best mean reward: 8345.0800 current episode reward: 7896.0000 episodes: 2213 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29327.0 seconds (8.15 hours) Timestep: 6710000 mean reward (100 episodes): 8297.0200 best mean reward: 8345.0800 current episode reward: 10968.0000 episodes: 2214 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29372.9 seconds (8.16 hours) Timestep: 6720000 mean reward (100 episodes): 8261.1000 best mean reward: 8345.0800 current episode reward: 7098.0000 episodes: 2216 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29417.9 seconds (8.17 hours) Timestep: 6730000 mean reward (100 episodes): 8249.8200 best mean reward: 8345.0800 current episode reward: 10590.0000 episodes: 2217 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29462.5 seconds (8.18 hours) Timestep: 6740000 mean reward (100 episodes): 8271.3400 best mean reward: 8345.0800 current episode reward: 8444.0000 episodes: 2219 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29507.8 seconds (8.20 hours) Timestep: 6750000 mean reward (100 episodes): 8325.6600 best mean reward: 8345.0800 current episode reward: 14000.0000 episodes: 2220 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29553.2 seconds (8.21 hours) Timestep: 6760000 mean reward (100 episodes): 8375.9600 best mean reward: 8375.9600 current episode reward: 8310.0000 episodes: 2222 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29598.2 seconds (8.22 hours) Timestep: 6770000 mean reward (100 episodes): 8346.7200 best mean reward: 8375.9600 current episode reward: 5880.0000 episodes: 2224 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29643.7 seconds (8.23 hours) Timestep: 6780000 mean reward (100 episodes): 8323.5400 best mean reward: 8375.9600 current episode reward: 4606.0000 episodes: 2225 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29689.1 seconds (8.25 hours) Timestep: 6790000 mean reward (100 episodes): 8287.7800 best mean reward: 8375.9600 current episode reward: 3990.0000 episodes: 2227 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29735.2 seconds (8.26 hours) Timestep: 6800000 mean reward (100 episodes): 8306.1000 best mean reward: 8375.9600 current episode reward: 6712.0000 episodes: 2229 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29780.9 seconds (8.27 hours) Timestep: 6810000 mean reward (100 episodes): 8254.7800 best mean reward: 8375.9600 current episode reward: 6864.0000 episodes: 2231 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29825.8 seconds (8.28 hours) Timestep: 6820000 mean reward (100 episodes): 8358.6200 best mean reward: 8375.9600 current episode reward: 13216.0000 episodes: 2232 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29871.2 seconds (8.30 hours) Timestep: 6830000 mean reward (100 episodes): 8365.1600 best mean reward: 8375.9600 current episode reward: 8226.0000 episodes: 2233 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29917.0 seconds (8.31 hours) Timestep: 6840000 mean reward (100 episodes): 8473.9200 best mean reward: 8473.9200 current episode reward: 10360.0000 episodes: 2235 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29962.1 seconds (8.32 hours) Timestep: 6850000 mean reward (100 episodes): 8448.7400 best mean reward: 8473.9200 current episode reward: 4028.0000 episodes: 2236 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30007.3 seconds (8.34 hours) Timestep: 6860000 mean reward (100 episodes): 8506.9600 best mean reward: 8506.9600 current episode reward: 12716.0000 episodes: 2237 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30052.5 seconds (8.35 hours) Timestep: 6870000 mean reward (100 episodes): 8652.3000 best mean reward: 8652.3000 current episode reward: 8346.0000 episodes: 2239 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30097.5 seconds (8.36 hours) Timestep: 6880000 mean reward (100 episodes): 8714.2800 best mean reward: 8714.2800 current episode reward: 14430.0000 episodes: 2240 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30142.4 seconds (8.37 hours) Timestep: 6890000 mean reward (100 episodes): 8660.4400 best mean reward: 8714.2800 current episode reward: 4230.0000 episodes: 2242 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30188.1 seconds (8.39 hours) Timestep: 6900000 mean reward (100 episodes): 8619.1800 best mean reward: 8714.2800 current episode reward: 5280.0000 episodes: 2244 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30233.6 seconds (8.40 hours) Timestep: 6910000 mean reward (100 episodes): 8643.8000 best mean reward: 8714.2800 current episode reward: 7038.0000 episodes: 2245 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30278.9 seconds (8.41 hours) Timestep: 6920000 mean reward (100 episodes): 8740.3000 best mean reward: 8740.3000 current episode reward: 9800.0000 episodes: 2247 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30324.1 seconds (8.42 hours) Timestep: 6930000 mean reward (100 episodes): 8639.9600 best mean reward: 8740.3000 current episode reward: 9268.0000 episodes: 2249 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30369.4 seconds (8.44 hours) Timestep: 6940000 mean reward (100 episodes): 8561.9800 best mean reward: 8740.3000 current episode reward: 4860.0000 episodes: 2250 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30414.5 seconds (8.45 hours) Timestep: 6950000 mean reward (100 episodes): 8466.1200 best mean reward: 8740.3000 current episode reward: 2108.0000 episodes: 2252 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30460.5 seconds (8.46 hours) Timestep: 6960000 mean reward (100 episodes): 8440.1800 best mean reward: 8740.3000 current episode reward: 8740.0000 episodes: 2253 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30505.6 seconds (8.47 hours) Timestep: 6970000 mean reward (100 episodes): 8541.7200 best mean reward: 8740.3000 current episode reward: 7530.0000 episodes: 2255 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30551.5 seconds (8.49 hours) Timestep: 6980000 mean reward (100 episodes): 8540.6200 best mean reward: 8740.3000 current episode reward: 8950.0000 episodes: 2256 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30596.5 seconds (8.50 hours) Timestep: 6990000 mean reward (100 episodes): 8553.7400 best mean reward: 8740.3000 current episode reward: 5760.0000 episodes: 2258 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30641.5 seconds (8.51 hours) Timestep: 7000000 mean reward (100 episodes): 8438.6400 best mean reward: 8740.3000 current episode reward: 5866.0000 episodes: 2261 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30686.7 seconds (8.52 hours) Timestep: 7010000 mean reward (100 episodes): 8480.5400 best mean reward: 8740.3000 current episode reward: 12450.0000 episodes: 2262 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30732.7 seconds (8.54 hours) Timestep: 7020000 mean reward (100 episodes): 8381.4400 best mean reward: 8740.3000 current episode reward: 7200.0000 episodes: 2263 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30777.0 seconds (8.55 hours) Timestep: 7030000 mean reward (100 episodes): 8470.2800 best mean reward: 8740.3000 current episode reward: 8580.0000 episodes: 2265 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30822.8 seconds (8.56 hours) Timestep: 7040000 mean reward (100 episodes): 8364.4400 best mean reward: 8740.3000 current episode reward: 6384.0000 episodes: 2266 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30868.2 seconds (8.57 hours) Timestep: 7050000 mean reward (100 episodes): 8430.8800 best mean reward: 8740.3000 current episode reward: 13352.0000 episodes: 2268 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30913.3 seconds (8.59 hours) Timestep: 7060000 mean reward (100 episodes): 8394.3800 best mean reward: 8740.3000 current episode reward: 7530.0000 episodes: 2269 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30958.9 seconds (8.60 hours) Timestep: 7070000 mean reward (100 episodes): 8355.3400 best mean reward: 8740.3000 current episode reward: 5216.0000 episodes: 2271 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31003.8 seconds (8.61 hours) Timestep: 7080000 mean reward (100 episodes): 8364.3400 best mean reward: 8740.3000 current episode reward: 6516.0000 episodes: 2273 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31048.8 seconds (8.62 hours) Timestep: 7090000 mean reward (100 episodes): 8447.9800 best mean reward: 8740.3000 current episode reward: 21676.0000 episodes: 2274 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31093.5 seconds (8.64 hours) Timestep: 7100000 mean reward (100 episodes): 8410.9000 best mean reward: 8740.3000 current episode reward: 6096.0000 episodes: 2275 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31138.0 seconds (8.65 hours) Timestep: 7110000 mean reward (100 episodes): 8491.8000 best mean reward: 8740.3000 current episode reward: 5280.0000 episodes: 2277 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31182.9 seconds (8.66 hours) Timestep: 7120000 mean reward (100 episodes): 8445.3200 best mean reward: 8740.3000 current episode reward: 7428.0000 episodes: 2279 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31227.3 seconds (8.67 hours) Timestep: 7130000 mean reward (100 episodes): 8480.0800 best mean reward: 8740.3000 current episode reward: 18920.0000 episodes: 2280 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31272.7 seconds (8.69 hours) Timestep: 7140000 mean reward (100 episodes): 8434.3400 best mean reward: 8740.3000 current episode reward: 8268.0000 episodes: 2281 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31318.6 seconds (8.70 hours) Timestep: 7150000 mean reward (100 episodes): 8544.0400 best mean reward: 8740.3000 current episode reward: 24716.0000 episodes: 2282 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31364.0 seconds (8.71 hours) Timestep: 7160000 mean reward (100 episodes): 8617.8400 best mean reward: 8740.3000 current episode reward: 12450.0000 episodes: 2283 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31408.7 seconds (8.72 hours) Timestep: 7170000 mean reward (100 episodes): 8756.4800 best mean reward: 8756.4800 current episode reward: 18756.0000 episodes: 2284 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31454.0 seconds (8.74 hours) Timestep: 7180000 mean reward (100 episodes): 8752.8400 best mean reward: 8756.4800 current episode reward: 7230.0000 episodes: 2286 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31499.1 seconds (8.75 hours) Timestep: 7190000 mean reward (100 episodes): 8739.8800 best mean reward: 8756.4800 current episode reward: 9810.0000 episodes: 2287 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31543.9 seconds (8.76 hours) Timestep: 7200000 mean reward (100 episodes): 8871.8600 best mean reward: 8871.8600 current episode reward: 20850.0000 episodes: 2288 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31589.0 seconds (8.77 hours) Timestep: 7210000 mean reward (100 episodes): 8888.0800 best mean reward: 8888.0800 current episode reward: 6804.0000 episodes: 2290 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31634.4 seconds (8.79 hours) Timestep: 7220000 mean reward (100 episodes): 8856.2400 best mean reward: 8888.0800 current episode reward: 6456.0000 episodes: 2291 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31679.0 seconds (8.80 hours) Timestep: 7230000 mean reward (100 episodes): 9018.7800 best mean reward: 9018.7800 current episode reward: 23310.0000 episodes: 2292 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31725.0 seconds (8.81 hours) Timestep: 7240000 mean reward (100 episodes): 9105.7200 best mean reward: 9105.7200 current episode reward: 14790.0000 episodes: 2293 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31770.1 seconds (8.83 hours) Timestep: 7250000 mean reward (100 episodes): 9221.8800 best mean reward: 9221.8800 current episode reward: 15936.0000 episodes: 2294 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31815.9 seconds (8.84 hours) Timestep: 7260000 mean reward (100 episodes): 9264.4800 best mean reward: 9264.4800 current episode reward: 12180.0000 episodes: 2295 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31861.2 seconds (8.85 hours) Timestep: 7270000 mean reward (100 episodes): 9365.6200 best mean reward: 9365.6200 current episode reward: 29044.0000 episodes: 2296 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31906.6 seconds (8.86 hours) Timestep: 7280000 mean reward (100 episodes): 9474.8600 best mean reward: 9474.8600 current episode reward: 16560.0000 episodes: 2297 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31952.1 seconds (8.88 hours) Timestep: 7290000 mean reward (100 episodes): 9529.0400 best mean reward: 9529.0400 current episode reward: 11820.0000 episodes: 2298 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31997.4 seconds (8.89 hours) Timestep: 7300000 mean reward (100 episodes): 9516.6800 best mean reward: 9529.0400 current episode reward: 6456.0000 episodes: 2299 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32043.3 seconds (8.90 hours) Timestep: 7310000 mean reward (100 episodes): 9578.0800 best mean reward: 9578.0800 current episode reward: 12132.0000 episodes: 2301 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32088.6 seconds (8.91 hours) Timestep: 7320000 mean reward (100 episodes): 9575.5200 best mean reward: 9578.0800 current episode reward: 4540.0000 episodes: 2302 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32133.6 seconds (8.93 hours) Timestep: 7330000 mean reward (100 episodes): 9602.1600 best mean reward: 9626.5800 current episode reward: 7758.0000 episodes: 2304 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32179.5 seconds (8.94 hours) Timestep: 7340000 mean reward (100 episodes): 9570.2200 best mean reward: 9626.5800 current episode reward: 3900.0000 episodes: 2305 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32224.7 seconds (8.95 hours) Timestep: 7350000 mean reward (100 episodes): 9619.4400 best mean reward: 9673.6200 current episode reward: 6528.0000 episodes: 2307 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32270.5 seconds (8.96 hours) Timestep: 7360000 mean reward (100 episodes): 9685.8000 best mean reward: 9685.8000 current episode reward: 13530.0000 episodes: 2308 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32315.7 seconds (8.98 hours) Timestep: 7370000 mean reward (100 episodes): 9767.8200 best mean reward: 9767.8200 current episode reward: 13642.0000 episodes: 2309 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32361.2 seconds (8.99 hours) Timestep: 7380000 mean reward (100 episodes): 9801.9600 best mean reward: 9801.9600 current episode reward: 7320.0000 episodes: 2311 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32406.5 seconds (9.00 hours) Timestep: 7390000 mean reward (100 episodes): 9766.4200 best mean reward: 9801.9600 current episode reward: 9750.0000 episodes: 2313 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32451.0 seconds (9.01 hours) Timestep: 7400000 mean reward (100 episodes): 9740.3600 best mean reward: 9801.9600 current episode reward: 8362.0000 episodes: 2314 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32497.2 seconds (9.03 hours) Timestep: 7410000 mean reward (100 episodes): 9776.3800 best mean reward: 9801.9600 current episode reward: 8740.0000 episodes: 2316 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32542.0 seconds (9.04 hours) Timestep: 7420000 mean reward (100 episodes): 9734.8000 best mean reward: 9801.9600 current episode reward: 6432.0000 episodes: 2317 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32587.1 seconds (9.05 hours) Timestep: 7430000 mean reward (100 episodes): 9758.7800 best mean reward: 9801.9600 current episode reward: 4606.0000 episodes: 2319 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32632.9 seconds (9.06 hours) Timestep: 7440000 mean reward (100 episodes): 9709.9800 best mean reward: 9801.9600 current episode reward: 5086.0000 episodes: 2321 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32678.3 seconds (9.08 hours) Timestep: 7450000 mean reward (100 episodes): 9705.7800 best mean reward: 9801.9600 current episode reward: 7890.0000 episodes: 2322 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32722.9 seconds (9.09 hours) Timestep: 7460000 mean reward (100 episodes): 9689.1000 best mean reward: 9801.9600 current episode reward: 8600.0000 episodes: 2324 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32768.6 seconds (9.10 hours) Timestep: 7470000 mean reward (100 episodes): 9722.8800 best mean reward: 9801.9600 current episode reward: 6648.0000 episodes: 2326 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32813.8 seconds (9.11 hours) Timestep: 7480000 mean reward (100 episodes): 9717.5200 best mean reward: 9801.9600 current episode reward: 5472.0000 episodes: 2328 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32858.9 seconds (9.13 hours) Timestep: 7490000 mean reward (100 episodes): 9764.7600 best mean reward: 9801.9600 current episode reward: 11436.0000 episodes: 2329 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32904.7 seconds (9.14 hours) Timestep: 7500000 mean reward (100 episodes): 9811.0200 best mean reward: 9811.0200 current episode reward: 9422.0000 episodes: 2330 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32950.3 seconds (9.15 hours) Timestep: 7510000 mean reward (100 episodes): 9773.4400 best mean reward: 9834.6800 current episode reward: 7092.0000 episodes: 2332 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32994.8 seconds (9.17 hours) Timestep: 7520000 mean reward (100 episodes): 9715.5200 best mean reward: 9834.6800 current episode reward: 7060.0000 episodes: 2334 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33040.2 seconds (9.18 hours) Timestep: 7530000 mean reward (100 episodes): 9682.1800 best mean reward: 9834.6800 current episode reward: 7026.0000 episodes: 2335 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33086.0 seconds (9.19 hours) Timestep: 7540000 mean reward (100 episodes): 9709.6800 best mean reward: 9834.6800 current episode reward: 4860.0000 episodes: 2337 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33131.9 seconds (9.20 hours) Timestep: 7550000 mean reward (100 episodes): 9618.0200 best mean reward: 9834.6800 current episode reward: 6254.0000 episodes: 2338 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33177.4 seconds (9.22 hours) Timestep: 7560000 mean reward (100 episodes): 9659.9400 best mean reward: 9834.6800 current episode reward: 12538.0000 episodes: 2339 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33222.3 seconds (9.23 hours) Timestep: 7570000 mean reward (100 episodes): 9671.1600 best mean reward: 9834.6800 current episode reward: 15552.0000 episodes: 2340 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33267.2 seconds (9.24 hours) Timestep: 7580000 mean reward (100 episodes): 9853.4200 best mean reward: 9853.4200 current episode reward: 22546.0000 episodes: 2341 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33312.9 seconds (9.25 hours) Timestep: 7590000 mean reward (100 episodes): 9885.3800 best mean reward: 9909.0200 current episode reward: 10590.0000 episodes: 2343 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33357.7 seconds (9.27 hours) Timestep: 7600000 mean reward (100 episodes): 9929.2800 best mean reward: 9929.2800 current episode reward: 9670.0000 episodes: 2344 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33402.9 seconds (9.28 hours) Timestep: 7610000 mean reward (100 episodes): 10001.4800 best mean reward: 10001.4800 current episode reward: 14258.0000 episodes: 2345 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33448.7 seconds (9.29 hours) Timestep: 7620000 mean reward (100 episodes): 9925.7600 best mean reward: 10001.4800 current episode reward: 5632.0000 episodes: 2347 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33493.9 seconds (9.30 hours) Timestep: 7630000 mean reward (100 episodes): 10016.1000 best mean reward: 10016.1000 current episode reward: 13126.0000 episodes: 2348 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33539.1 seconds (9.32 hours) Timestep: 7640000 mean reward (100 episodes): 10083.3200 best mean reward: 10083.3200 current episode reward: 15990.0000 episodes: 2349 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33583.9 seconds (9.33 hours) Timestep: 7650000 mean reward (100 episodes): 10097.2600 best mean reward: 10097.2600 current episode reward: 6254.0000 episodes: 2350 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33629.0 seconds (9.34 hours) Timestep: 7660000 mean reward (100 episodes): 10177.4000 best mean reward: 10177.4000 current episode reward: 17184.0000 episodes: 2351 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33673.6 seconds (9.35 hours) Timestep: 7670000 mean reward (100 episodes): 10211.2400 best mean reward: 10239.8400 current episode reward: 5880.0000 episodes: 2353 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33717.9 seconds (9.37 hours) Timestep: 7680000 mean reward (100 episodes): 10158.7400 best mean reward: 10239.8400 current episode reward: 9030.0000 episodes: 2354 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33763.0 seconds (9.38 hours) Timestep: 7690000 mean reward (100 episodes): 10224.2200 best mean reward: 10251.1000 current episode reward: 6262.0000 episodes: 2356 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33808.0 seconds (9.39 hours) Timestep: 7700000 mean reward (100 episodes): 10340.2800 best mean reward: 10340.2800 current episode reward: 21336.0000 episodes: 2357 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33853.5 seconds (9.40 hours) Timestep: 7710000 mean reward (100 episodes): 10340.2800 best mean reward: 10340.2800 current episode reward: 21336.0000 episodes: 2357 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33898.8 seconds (9.42 hours) Timestep: 7720000 mean reward (100 episodes): 10511.7000 best mean reward: 10511.7000 current episode reward: 4992.0000 episodes: 2360 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33943.5 seconds (9.43 hours) Timestep: 7730000 mean reward (100 episodes): 10511.7000 best mean reward: 10511.7000 current episode reward: 4992.0000 episodes: 2360 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33988.8 seconds (9.44 hours) Timestep: 7740000 mean reward (100 episodes): 10610.1600 best mean reward: 10680.5600 current episode reward: 5410.0000 episodes: 2362 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34034.5 seconds (9.45 hours) Timestep: 7750000 mean reward (100 episodes): 10550.5800 best mean reward: 10680.5600 current episode reward: 6996.0000 episodes: 2364 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34079.7 seconds (9.47 hours) Timestep: 7760000 mean reward (100 episodes): 10544.8800 best mean reward: 10680.5600 current episode reward: 8010.0000 episodes: 2365 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34124.0 seconds (9.48 hours) Timestep: 7770000 mean reward (100 episodes): 10633.2000 best mean reward: 10680.5600 current episode reward: 15216.0000 episodes: 2366 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34168.9 seconds (9.49 hours) Timestep: 7780000 mean reward (100 episodes): 10663.6000 best mean reward: 10708.6200 current episode reward: 8850.0000 episodes: 2368 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34214.2 seconds (9.50 hours) Timestep: 7790000 mean reward (100 episodes): 10663.7400 best mean reward: 10708.6200 current episode reward: 7544.0000 episodes: 2369 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34259.5 seconds (9.52 hours) Timestep: 7800000 mean reward (100 episodes): 10704.9400 best mean reward: 10708.6200 current episode reward: 8734.0000 episodes: 2371 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34304.7 seconds (9.53 hours) Timestep: 7810000 mean reward (100 episodes): 10693.8600 best mean reward: 10708.6200 current episode reward: 5676.0000 episodes: 2372 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34349.0 seconds (9.54 hours) Timestep: 7820000 mean reward (100 episodes): 10541.0600 best mean reward: 10708.6200 current episode reward: 6384.0000 episodes: 2374 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34394.1 seconds (9.55 hours) Timestep: 7830000 mean reward (100 episodes): 10510.5600 best mean reward: 10708.6200 current episode reward: 4384.0000 episodes: 2376 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34438.5 seconds (9.57 hours) Timestep: 7840000 mean reward (100 episodes): 10589.2400 best mean reward: 10708.6200 current episode reward: 5598.0000 episodes: 2378 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34483.7 seconds (9.58 hours) Timestep: 7850000 mean reward (100 episodes): 10430.4800 best mean reward: 10708.6200 current episode reward: 4796.0000 episodes: 2380 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34529.3 seconds (9.59 hours) Timestep: 7860000 mean reward (100 episodes): 10403.8800 best mean reward: 10708.6200 current episode reward: 5608.0000 episodes: 2381 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34574.8 seconds (9.60 hours) Timestep: 7870000 mean reward (100 episodes): 10213.2400 best mean reward: 10708.6200 current episode reward: 6300.0000 episodes: 2383 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34619.4 seconds (9.62 hours) Timestep: 7880000 mean reward (100 episodes): 10071.6200 best mean reward: 10708.6200 current episode reward: 6470.0000 episodes: 2385 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34665.2 seconds (9.63 hours) Timestep: 7890000 mean reward (100 episodes): 10102.2200 best mean reward: 10708.6200 current episode reward: 10290.0000 episodes: 2386 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34710.6 seconds (9.64 hours) Timestep: 7900000 mean reward (100 episodes): 9952.3600 best mean reward: 10708.6200 current episode reward: 6690.0000 episodes: 2388 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34756.4 seconds (9.65 hours) Timestep: 7910000 mean reward (100 episodes): 9900.8800 best mean reward: 10708.6200 current episode reward: 1284.0000 episodes: 2391 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34801.4 seconds (9.67 hours) Timestep: 7920000 mean reward (100 episodes): 9778.1800 best mean reward: 10708.6200 current episode reward: 11040.0000 episodes: 2392 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34846.1 seconds (9.68 hours) Timestep: 7930000 mean reward (100 episodes): 9647.1000 best mean reward: 10708.6200 current episode reward: 8298.0000 episodes: 2394 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34891.6 seconds (9.69 hours) Timestep: 7940000 mean reward (100 episodes): 9647.5400 best mean reward: 10708.6200 current episode reward: 12224.0000 episodes: 2395 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34936.7 seconds (9.70 hours) Timestep: 7950000 mean reward (100 episodes): 9322.7000 best mean reward: 10708.6200 current episode reward: 4092.0000 episodes: 2397 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34982.3 seconds (9.72 hours) Timestep: 7960000 mean reward (100 episodes): 9247.5400 best mean reward: 10708.6200 current episode reward: 4832.0000 episodes: 2399 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35027.7 seconds (9.73 hours) Timestep: 7970000 mean reward (100 episodes): 9228.8000 best mean reward: 10708.6200 current episode reward: 9310.0000 episodes: 2400 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35073.6 seconds (9.74 hours) Timestep: 7980000 mean reward (100 episodes): 9201.0800 best mean reward: 10708.6200 current episode reward: 7264.0000 episodes: 2402 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35118.1 seconds (9.76 hours) Timestep: 7990000 mean reward (100 episodes): 9120.8000 best mean reward: 10708.6200 current episode reward: 5964.0000 episodes: 2403 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35163.5 seconds (9.77 hours) Timestep: 8000000 mean reward (100 episodes): 9212.1200 best mean reward: 10708.6200 current episode reward: 8012.0000 episodes: 2405 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35208.5 seconds (9.78 hours) Timestep: 8010000 mean reward (100 episodes): 9165.5600 best mean reward: 10708.6200 current episode reward: 14694.0000 episodes: 2406 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35253.8 seconds (9.79 hours) Timestep: 8020000 mean reward (100 episodes): 9236.7400 best mean reward: 10708.6200 current episode reward: 13646.0000 episodes: 2407 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35299.4 seconds (9.81 hours) Timestep: 8030000 mean reward (100 episodes): 9105.1800 best mean reward: 10708.6200 current episode reward: 6864.0000 episodes: 2409 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35345.3 seconds (9.82 hours) Timestep: 8040000 mean reward (100 episodes): 9136.7800 best mean reward: 10708.6200 current episode reward: 9014.0000 episodes: 2410 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35390.2 seconds (9.83 hours) Timestep: 8050000 mean reward (100 episodes): 9236.9800 best mean reward: 10708.6200 current episode reward: 17340.0000 episodes: 2411 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35435.3 seconds (9.84 hours) Timestep: 8060000 mean reward (100 episodes): 9238.9800 best mean reward: 10708.6200 current episode reward: 5880.0000 episodes: 2413 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35481.1 seconds (9.86 hours) Timestep: 8070000 mean reward (100 episodes): 9202.9400 best mean reward: 10708.6200 current episode reward: 5924.0000 episodes: 2415 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35527.3 seconds (9.87 hours) Timestep: 8080000 mean reward (100 episodes): 9270.9000 best mean reward: 10708.6200 current episode reward: 15536.0000 episodes: 2416 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35572.6 seconds (9.88 hours) Timestep: 8090000 mean reward (100 episodes): 9358.9800 best mean reward: 10708.6200 current episode reward: 15240.0000 episodes: 2417 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35617.7 seconds (9.89 hours) Timestep: 8100000 mean reward (100 episodes): 9394.8400 best mean reward: 10708.6200 current episode reward: 16788.0000 episodes: 2418 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35664.0 seconds (9.91 hours) Timestep: 8110000 mean reward (100 episodes): 9494.9600 best mean reward: 10708.6200 current episode reward: 14618.0000 episodes: 2419 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35709.5 seconds (9.92 hours) Timestep: 8120000 mean reward (100 episodes): 9486.2200 best mean reward: 10708.6200 current episode reward: 8764.0000 episodes: 2421 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35755.2 seconds (9.93 hours) Timestep: 8130000 mean reward (100 episodes): 9492.1200 best mean reward: 10708.6200 current episode reward: 7446.0000 episodes: 2423 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35800.0 seconds (9.94 hours) Timestep: 8140000 mean reward (100 episodes): 9492.1200 best mean reward: 10708.6200 current episode reward: 7446.0000 episodes: 2423 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35844.9 seconds (9.96 hours) Timestep: 8150000 mean reward (100 episodes): 9540.8200 best mean reward: 10708.6200 current episode reward: 6762.0000 episodes: 2425 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35890.2 seconds (9.97 hours) Timestep: 8160000 mean reward (100 episodes): 9541.6200 best mean reward: 10708.6200 current episode reward: 8962.0000 episodes: 2427 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35934.8 seconds (9.98 hours) Timestep: 8170000 mean reward (100 episodes): 9470.5800 best mean reward: 10708.6200 current episode reward: 6804.0000 episodes: 2429 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35980.1 seconds (9.99 hours) Timestep: 8180000 mean reward (100 episodes): 9456.3800 best mean reward: 10708.6200 current episode reward: 8172.0000 episodes: 2431 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36025.3 seconds (10.01 hours) Timestep: 8190000 mean reward (100 episodes): 9449.9400 best mean reward: 10708.6200 current episode reward: 7576.0000 episodes: 2433 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36071.3 seconds (10.02 hours) Timestep: 8200000 mean reward (100 episodes): 9473.9400 best mean reward: 10708.6200 current episode reward: 9460.0000 episodes: 2434 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36117.1 seconds (10.03 hours) Timestep: 8210000 mean reward (100 episodes): 9413.0200 best mean reward: 10708.6200 current episode reward: 5730.0000 episodes: 2436 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36162.3 seconds (10.05 hours) Timestep: 8220000 mean reward (100 episodes): 9400.7200 best mean reward: 10708.6200 current episode reward: 4928.0000 episodes: 2438 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36207.5 seconds (10.06 hours) Timestep: 8230000 mean reward (100 episodes): 9347.6400 best mean reward: 10708.6200 current episode reward: 7230.0000 episodes: 2439 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36252.6 seconds (10.07 hours) Timestep: 8240000 mean reward (100 episodes): 9369.3600 best mean reward: 10708.6200 current episode reward: 17724.0000 episodes: 2440 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36297.3 seconds (10.08 hours) Timestep: 8250000 mean reward (100 episodes): 9249.0800 best mean reward: 10708.6200 current episode reward: 8584.0000 episodes: 2442 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36342.3 seconds (10.10 hours) Timestep: 8260000 mean reward (100 episodes): 9149.2000 best mean reward: 10708.6200 current episode reward: 4320.0000 episodes: 2444 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36387.0 seconds (10.11 hours) Timestep: 8270000 mean reward (100 episodes): 9157.5200 best mean reward: 10708.6200 current episode reward: 15090.0000 episodes: 2445 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36433.2 seconds (10.12 hours) Timestep: 8280000 mean reward (100 episodes): 9195.3600 best mean reward: 10708.6200 current episode reward: 6864.0000 episodes: 2447 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36478.9 seconds (10.13 hours) Timestep: 8290000 mean reward (100 episodes): 9030.7800 best mean reward: 10708.6200 current episode reward: 7380.0000 episodes: 2449 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36524.2 seconds (10.15 hours) Timestep: 8300000 mean reward (100 episodes): 9107.1400 best mean reward: 10708.6200 current episode reward: 13890.0000 episodes: 2450 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36569.9 seconds (10.16 hours) Timestep: 8310000 mean reward (100 episodes): 9032.4400 best mean reward: 10708.6200 current episode reward: 4604.0000 episodes: 2452 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36615.6 seconds (10.17 hours) Timestep: 8320000 mean reward (100 episodes): 9062.0200 best mean reward: 10708.6200 current episode reward: 8838.0000 episodes: 2453 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36660.5 seconds (10.18 hours) Timestep: 8330000 mean reward (100 episodes): 9115.7200 best mean reward: 10708.6200 current episode reward: 14400.0000 episodes: 2454 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36706.5 seconds (10.20 hours) Timestep: 8340000 mean reward (100 episodes): 9049.2600 best mean reward: 10708.6200 current episode reward: 7762.0000 episodes: 2456 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36751.5 seconds (10.21 hours) Timestep: 8350000 mean reward (100 episodes): 8773.2800 best mean reward: 10708.6200 current episode reward: 7622.0000 episodes: 2458 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36796.7 seconds (10.22 hours) Timestep: 8360000 mean reward (100 episodes): 8769.2600 best mean reward: 10708.6200 current episode reward: 6810.0000 episodes: 2459 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36841.4 seconds (10.23 hours) Timestep: 8370000 mean reward (100 episodes): 8639.3600 best mean reward: 10708.6200 current episode reward: 6414.0000 episodes: 2461 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36886.7 seconds (10.25 hours) Timestep: 8380000 mean reward (100 episodes): 8696.8600 best mean reward: 10708.6200 current episode reward: 9044.0000 episodes: 2463 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36932.1 seconds (10.26 hours) Timestep: 8390000 mean reward (100 episodes): 8683.8600 best mean reward: 10708.6200 current episode reward: 5696.0000 episodes: 2464 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36976.3 seconds (10.27 hours) Timestep: 8400000 mean reward (100 episodes): 8762.2600 best mean reward: 10708.6200 current episode reward: 15850.0000 episodes: 2465 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37022.2 seconds (10.28 hours) Timestep: 8410000 mean reward (100 episodes): 8679.7000 best mean reward: 10708.6200 current episode reward: 6960.0000 episodes: 2466 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37067.8 seconds (10.30 hours) Timestep: 8420000 mean reward (100 episodes): 8710.9200 best mean reward: 10708.6200 current episode reward: 7386.0000 episodes: 2468 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37112.4 seconds (10.31 hours) Timestep: 8430000 mean reward (100 episodes): 8700.8600 best mean reward: 10708.6200 current episode reward: 6538.0000 episodes: 2469 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37157.3 seconds (10.32 hours) Timestep: 8440000 mean reward (100 episodes): 8687.2600 best mean reward: 10708.6200 current episode reward: 6390.0000 episodes: 2471 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37203.2 seconds (10.33 hours) Timestep: 8450000 mean reward (100 episodes): 8832.2000 best mean reward: 10708.6200 current episode reward: 20170.0000 episodes: 2472 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37249.2 seconds (10.35 hours) Timestep: 8460000 mean reward (100 episodes): 8965.5600 best mean reward: 10708.6200 current episode reward: 19864.0000 episodes: 2473 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37294.8 seconds (10.36 hours) Timestep: 8470000 mean reward (100 episodes): 8916.4200 best mean reward: 10708.6200 current episode reward: 4860.0000 episodes: 2475 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37339.8 seconds (10.37 hours) Timestep: 8480000 mean reward (100 episodes): 9016.1800 best mean reward: 10708.6200 current episode reward: 14360.0000 episodes: 2476 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37385.1 seconds (10.38 hours) Timestep: 8490000 mean reward (100 episodes): 8975.9000 best mean reward: 10708.6200 current episode reward: 7486.0000 episodes: 2477 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37429.7 seconds (10.40 hours) Timestep: 8500000 mean reward (100 episodes): 9039.6400 best mean reward: 10708.6200 current episode reward: 11972.0000 episodes: 2478 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37475.0 seconds (10.41 hours) Timestep: 8510000 mean reward (100 episodes): 9163.1000 best mean reward: 10708.6200 current episode reward: 9098.0000 episodes: 2480 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37520.2 seconds (10.42 hours) Timestep: 8520000 mean reward (100 episodes): 9208.7200 best mean reward: 10708.6200 current episode reward: 10170.0000 episodes: 2481 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37565.5 seconds (10.43 hours) Timestep: 8530000 mean reward (100 episodes): 9233.4000 best mean reward: 10708.6200 current episode reward: 10680.0000 episodes: 2483 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37610.5 seconds (10.45 hours) Timestep: 8540000 mean reward (100 episodes): 9308.2000 best mean reward: 10708.6200 current episode reward: 14386.0000 episodes: 2484 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37655.3 seconds (10.46 hours) Timestep: 8550000 mean reward (100 episodes): 9360.2000 best mean reward: 10708.6200 current episode reward: 11670.0000 episodes: 2485 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37699.9 seconds (10.47 hours) Timestep: 8560000 mean reward (100 episodes): 9395.9000 best mean reward: 10708.6200 current episode reward: 13860.0000 episodes: 2486 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37745.0 seconds (10.48 hours) Timestep: 8570000 mean reward (100 episodes): 9432.6800 best mean reward: 10708.6200 current episode reward: 11112.0000 episodes: 2488 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37790.8 seconds (10.50 hours) Timestep: 8580000 mean reward (100 episodes): 9410.3800 best mean reward: 10708.6200 current episode reward: 5544.0000 episodes: 2489 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37836.7 seconds (10.51 hours) Timestep: 8590000 mean reward (100 episodes): 9479.9600 best mean reward: 10708.6200 current episode reward: 6402.0000 episodes: 2491 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37881.6 seconds (10.52 hours) Timestep: 8600000 mean reward (100 episodes): 9450.4800 best mean reward: 10708.6200 current episode reward: 6652.0000 episodes: 2493 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37926.8 seconds (10.54 hours) Timestep: 8610000 mean reward (100 episodes): 9428.4600 best mean reward: 10708.6200 current episode reward: 6096.0000 episodes: 2494 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37971.8 seconds (10.55 hours) Timestep: 8620000 mean reward (100 episodes): 9365.0600 best mean reward: 10708.6200 current episode reward: 7290.0000 episodes: 2496 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38016.3 seconds (10.56 hours) Timestep: 8630000 mean reward (100 episodes): 9516.1600 best mean reward: 10708.6200 current episode reward: 19202.0000 episodes: 2497 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38061.4 seconds (10.57 hours) Timestep: 8640000 mean reward (100 episodes): 9591.7600 best mean reward: 10708.6200 current episode reward: 4898.0000 episodes: 2499 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38106.9 seconds (10.59 hours) Timestep: 8650000 mean reward (100 episodes): 9547.9400 best mean reward: 10708.6200 current episode reward: 4928.0000 episodes: 2500 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38152.8 seconds (10.60 hours) Timestep: 8660000 mean reward (100 episodes): 9591.8200 best mean reward: 10708.6200 current episode reward: 7038.0000 episodes: 2502 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38198.7 seconds (10.61 hours) Timestep: 8670000 mean reward (100 episodes): 9534.6200 best mean reward: 10708.6200 current episode reward: 5666.0000 episodes: 2504 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38243.7 seconds (10.62 hours) Timestep: 8680000 mean reward (100 episodes): 9723.6000 best mean reward: 10708.6200 current episode reward: 26910.0000 episodes: 2505 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38290.0 seconds (10.64 hours) Timestep: 8690000 mean reward (100 episodes): 9622.7000 best mean reward: 10708.6200 current episode reward: 4604.0000 episodes: 2506 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38335.6 seconds (10.65 hours) Timestep: 8700000 mean reward (100 episodes): 9664.2600 best mean reward: 10708.6200 current episode reward: 7416.0000 episodes: 2508 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38381.0 seconds (10.66 hours) Timestep: 8710000 mean reward (100 episodes): 9713.6200 best mean reward: 10708.6200 current episode reward: 11800.0000 episodes: 2509 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38425.7 seconds (10.67 hours) Timestep: 8720000 mean reward (100 episodes): 9771.0400 best mean reward: 10708.6200 current episode reward: 14756.0000 episodes: 2510 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38471.4 seconds (10.69 hours) Timestep: 8730000 mean reward (100 episodes): 9682.4600 best mean reward: 10708.6200 current episode reward: 8482.0000 episodes: 2511 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38515.8 seconds (10.70 hours) Timestep: 8740000 mean reward (100 episodes): 9738.4400 best mean reward: 10708.6200 current episode reward: 6156.0000 episodes: 2513 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38561.9 seconds (10.71 hours) Timestep: 8750000 mean reward (100 episodes): 9747.5800 best mean reward: 10708.6200 current episode reward: 8420.0000 episodes: 2514 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38606.8 seconds (10.72 hours) Timestep: 8760000 mean reward (100 episodes): 9795.7400 best mean reward: 10708.6200 current episode reward: 10740.0000 episodes: 2515 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38651.8 seconds (10.74 hours) Timestep: 8770000 mean reward (100 episodes): 9778.6800 best mean reward: 10708.6200 current episode reward: 10490.0000 episodes: 2517 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38697.6 seconds (10.75 hours) Timestep: 8780000 mean reward (100 episodes): 9673.8000 best mean reward: 10708.6200 current episode reward: 6300.0000 episodes: 2518 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38742.5 seconds (10.76 hours) Timestep: 8790000 mean reward (100 episodes): 9630.7400 best mean reward: 10708.6200 current episode reward: 10312.0000 episodes: 2519 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38787.2 seconds (10.77 hours) Timestep: 8800000 mean reward (100 episodes): 9693.1200 best mean reward: 10708.6200 current episode reward: 9660.0000 episodes: 2521 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38833.3 seconds (10.79 hours) Timestep: 8810000 mean reward (100 episodes): 9710.0800 best mean reward: 10708.6200 current episode reward: 10566.0000 episodes: 2523 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38878.3 seconds (10.80 hours) Timestep: 8820000 mean reward (100 episodes): 9550.9800 best mean reward: 10708.6200 current episode reward: 4320.0000 episodes: 2525 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38923.4 seconds (10.81 hours) Timestep: 8830000 mean reward (100 episodes): 9541.7200 best mean reward: 10708.6200 current episode reward: 6690.0000 episodes: 2527 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38968.9 seconds (10.82 hours) Timestep: 8840000 mean reward (100 episodes): 9621.2800 best mean reward: 10708.6200 current episode reward: 12420.0000 episodes: 2529 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39015.2 seconds (10.84 hours) Timestep: 8850000 mean reward (100 episodes): 9610.7800 best mean reward: 10708.6200 current episode reward: 8010.0000 episodes: 2530 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39060.7 seconds (10.85 hours) Timestep: 8860000 mean reward (100 episodes): 9558.6200 best mean reward: 10708.6200 current episode reward: 6274.0000 episodes: 2533 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39105.7 seconds (10.86 hours) Timestep: 8870000 mean reward (100 episodes): 9537.2800 best mean reward: 10708.6200 current episode reward: 7326.0000 episodes: 2534 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39150.9 seconds (10.88 hours) Timestep: 8880000 mean reward (100 episodes): 9581.8400 best mean reward: 10708.6200 current episode reward: 13064.0000 episodes: 2536 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39196.2 seconds (10.89 hours) Timestep: 8890000 mean reward (100 episodes): 9610.7600 best mean reward: 10708.6200 current episode reward: 5348.0000 episodes: 2538 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39241.3 seconds (10.90 hours) Timestep: 8900000 mean reward (100 episodes): 9612.3200 best mean reward: 10708.6200 current episode reward: 7386.0000 episodes: 2539 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39286.4 seconds (10.91 hours) Timestep: 8910000 mean reward (100 episodes): 9534.2600 best mean reward: 10708.6200 current episode reward: 15312.0000 episodes: 2541 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39331.8 seconds (10.93 hours) Timestep: 8920000 mean reward (100 episodes): 9503.9400 best mean reward: 10708.6200 current episode reward: 5552.0000 episodes: 2542 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39377.7 seconds (10.94 hours) Timestep: 8930000 mean reward (100 episodes): 9597.7200 best mean reward: 10708.6200 current episode reward: 8690.0000 episodes: 2544 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39422.0 seconds (10.95 hours) Timestep: 8940000 mean reward (100 episodes): 9558.8200 best mean reward: 10708.6200 current episode reward: 11200.0000 episodes: 2545 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39467.9 seconds (10.96 hours) Timestep: 8950000 mean reward (100 episodes): 9552.1400 best mean reward: 10708.6200 current episode reward: 5212.0000 episodes: 2547 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39513.3 seconds (10.98 hours) Timestep: 8960000 mean reward (100 episodes): 9561.9200 best mean reward: 10708.6200 current episode reward: 5812.0000 episodes: 2549 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39558.7 seconds (10.99 hours) Timestep: 8970000 mean reward (100 episodes): 9579.6200 best mean reward: 10708.6200 current episode reward: 15660.0000 episodes: 2550 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39604.1 seconds (11.00 hours) Timestep: 8980000 mean reward (100 episodes): 9567.9400 best mean reward: 10708.6200 current episode reward: 12294.0000 episodes: 2551 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39649.2 seconds (11.01 hours) Timestep: 8990000 mean reward (100 episodes): 9710.4000 best mean reward: 10708.6200 current episode reward: 18850.0000 episodes: 2552 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39695.5 seconds (11.03 hours) Timestep: 9000000 mean reward (100 episodes): 9706.6000 best mean reward: 10708.6200 current episode reward: 8458.0000 episodes: 2553 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39741.4 seconds (11.04 hours) Timestep: 9010000 mean reward (100 episodes): 9737.0000 best mean reward: 10708.6200 current episode reward: 8750.0000 episodes: 2555 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39786.9 seconds (11.05 hours) Timestep: 9020000 mean reward (100 episodes): 9698.7600 best mean reward: 10708.6200 current episode reward: 4284.0000 episodes: 2557 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39831.6 seconds (11.06 hours) Timestep: 9030000 mean reward (100 episodes): 9607.9200 best mean reward: 10708.6200 current episode reward: 2552.0000 episodes: 2560 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39877.0 seconds (11.08 hours) Timestep: 9040000 mean reward (100 episodes): 9708.3800 best mean reward: 10708.6200 current episode reward: 16460.0000 episodes: 2561 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39922.3 seconds (11.09 hours) Timestep: 9050000 mean reward (100 episodes): 9708.3800 best mean reward: 10708.6200 current episode reward: 16460.0000 episodes: 2561 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39967.2 seconds (11.10 hours) Timestep: 9060000 mean reward (100 episodes): 9858.2000 best mean reward: 10708.6200 current episode reward: 14310.0000 episodes: 2563 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40012.6 seconds (11.11 hours) Timestep: 9070000 mean reward (100 episodes): 9904.7400 best mean reward: 10708.6200 current episode reward: 10350.0000 episodes: 2564 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40057.1 seconds (11.13 hours) Timestep: 9080000 mean reward (100 episodes): 9816.4400 best mean reward: 10708.6200 current episode reward: 8764.0000 episodes: 2566 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40102.6 seconds (11.14 hours) Timestep: 9090000 mean reward (100 episodes): 9718.1400 best mean reward: 10708.6200 current episode reward: 9480.0000 episodes: 2567 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40147.7 seconds (11.15 hours) Timestep: 9100000 mean reward (100 episodes): 9757.7600 best mean reward: 10708.6200 current episode reward: 7956.0000 episodes: 2569 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40192.9 seconds (11.16 hours) Timestep: 9110000 mean reward (100 episodes): 9740.8400 best mean reward: 10708.6200 current episode reward: 13094.0000 episodes: 2570 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40238.1 seconds (11.18 hours) Timestep: 9120000 mean reward (100 episodes): 9806.9400 best mean reward: 10708.6200 current episode reward: 13000.0000 episodes: 2571 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40283.5 seconds (11.19 hours) Timestep: 9130000 mean reward (100 episodes): 9558.5400 best mean reward: 10708.6200 current episode reward: 5858.0000 episodes: 2573 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40328.6 seconds (11.20 hours) Timestep: 9140000 mean reward (100 episodes): 9548.7800 best mean reward: 10708.6200 current episode reward: 5726.0000 episodes: 2575 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40373.8 seconds (11.21 hours) Timestep: 9150000 mean reward (100 episodes): 9654.8200 best mean reward: 10708.6200 current episode reward: 24964.0000 episodes: 2576 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40419.1 seconds (11.23 hours) Timestep: 9160000 mean reward (100 episodes): 9566.4600 best mean reward: 10708.6200 current episode reward: 6210.0000 episodes: 2578 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40464.6 seconds (11.24 hours) Timestep: 9170000 mean reward (100 episodes): 9510.5800 best mean reward: 10708.6200 current episode reward: 8132.0000 episodes: 2579 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40510.3 seconds (11.25 hours) Timestep: 9180000 mean reward (100 episodes): 9506.9000 best mean reward: 10708.6200 current episode reward: 4350.0000 episodes: 2581 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40555.4 seconds (11.27 hours) Timestep: 9190000 mean reward (100 episodes): 9600.8400 best mean reward: 10708.6200 current episode reward: 19284.0000 episodes: 2582 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40600.9 seconds (11.28 hours) Timestep: 9200000 mean reward (100 episodes): 9450.2400 best mean reward: 10708.6200 current episode reward: 8002.0000 episodes: 2584 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40646.5 seconds (11.29 hours) Timestep: 9210000 mean reward (100 episodes): 9343.2000 best mean reward: 10708.6200 current episode reward: 8386.0000 episodes: 2586 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40691.4 seconds (11.30 hours) Timestep: 9220000 mean reward (100 episodes): 9348.7400 best mean reward: 10708.6200 current episode reward: 8794.0000 episodes: 2587 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40737.4 seconds (11.32 hours) Timestep: 9230000 mean reward (100 episodes): 9376.5400 best mean reward: 10708.6200 current episode reward: 13892.0000 episodes: 2588 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40782.8 seconds (11.33 hours) Timestep: 9240000 mean reward (100 episodes): 9470.2800 best mean reward: 10708.6200 current episode reward: 6678.0000 episodes: 2590 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40827.2 seconds (11.34 hours) Timestep: 9250000 mean reward (100 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exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41054.6 seconds (11.40 hours) Timestep: 9300000 mean reward (100 episodes): 9552.8400 best mean reward: 10708.6200 current episode reward: 10920.0000 episodes: 2599 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41100.1 seconds (11.42 hours) Timestep: 9310000 mean reward (100 episodes): 9581.3800 best mean reward: 10708.6200 current episode reward: 7782.0000 episodes: 2600 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41145.5 seconds (11.43 hours) Timestep: 9320000 mean reward (100 episodes): 9655.3000 best mean reward: 10708.6200 current episode reward: 18642.0000 episodes: 2601 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41190.9 seconds (11.44 hours) Timestep: 9330000 mean reward (100 episodes): 9639.9000 best mean reward: 10708.6200 current episode reward: 8346.0000 episodes: 2604 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41235.5 seconds (11.45 hours) Timestep: 9340000 mean reward (100 episodes): 9455.3200 best mean reward: 10708.6200 current episode reward: 8452.0000 episodes: 2605 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41281.1 seconds (11.47 hours) Timestep: 9350000 mean reward (100 episodes): 9437.8200 best mean reward: 10708.6200 current episode reward: 8122.0000 episodes: 2607 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41326.5 seconds (11.48 hours) Timestep: 9360000 mean reward (100 episodes): 9449.5800 best mean reward: 10708.6200 current episode reward: 8592.0000 episodes: 2608 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41372.1 seconds (11.49 hours) Timestep: 9370000 mean reward (100 episodes): 9502.7600 best mean reward: 10708.6200 current episode reward: 17118.0000 episodes: 2609 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41417.8 seconds (11.50 hours) Timestep: 9380000 mean reward (100 episodes): 9425.2200 best mean reward: 10708.6200 current episode reward: 7264.0000 episodes: 2611 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41463.4 seconds (11.52 hours) Timestep: 9390000 mean reward (100 episodes): 9350.4800 best mean reward: 10708.6200 current episode reward: 6810.0000 episodes: 2612 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41508.7 seconds (11.53 hours) Timestep: 9400000 mean reward (100 episodes): 9344.4800 best mean reward: 10708.6200 current episode reward: 5952.0000 episodes: 2614 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41554.5 seconds (11.54 hours) Timestep: 9410000 mean reward (100 episodes): 9322.3600 best mean reward: 10708.6200 current episode reward: 8528.0000 episodes: 2615 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41599.5 seconds (11.56 hours) Timestep: 9420000 mean reward (100 episodes): 9241.8000 best mean reward: 10708.6200 current episode reward: 5854.0000 episodes: 2617 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41644.5 seconds (11.57 hours) Timestep: 9430000 mean reward (100 episodes): 9239.4000 best mean reward: 10708.6200 current episode reward: 7912.0000 episodes: 2619 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41690.7 seconds (11.58 hours) Timestep: 9440000 mean reward (100 episodes): 9157.0800 best mean reward: 10708.6200 current episode reward: 5440.0000 episodes: 2620 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41736.1 seconds (11.59 hours) Timestep: 9450000 mean reward (100 episodes): 9262.0000 best mean reward: 10708.6200 current episode reward: 7668.0000 episodes: 2622 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41781.5 seconds (11.61 hours) Timestep: 9460000 mean reward (100 episodes): 9267.5400 best mean reward: 10708.6200 current episode reward: 11120.0000 episodes: 2623 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41826.9 seconds (11.62 hours) Timestep: 9470000 mean reward (100 episodes): 9296.5400 best mean reward: 10708.6200 current episode reward: 5666.0000 episodes: 2626 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41871.9 seconds (11.63 hours) Timestep: 9480000 mean reward (100 episodes): 9323.9400 best mean reward: 10708.6200 current episode reward: 5500.0000 episodes: 2628 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41917.4 seconds (11.64 hours) Timestep: 9490000 mean reward (100 episodes): 9311.0400 best mean reward: 10708.6200 current episode reward: 11130.0000 episodes: 2629 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41963.2 seconds (11.66 hours) Timestep: 9500000 mean reward (100 episodes): 9295.9800 best mean reward: 10708.6200 current episode reward: 6504.0000 episodes: 2630 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42008.8 seconds (11.67 hours) Timestep: 9510000 mean reward (100 episodes): 9415.2200 best mean reward: 10708.6200 current episode reward: 7176.0000 episodes: 2632 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42054.3 seconds (11.68 hours) Timestep: 9520000 mean reward (100 episodes): 9544.8800 best mean reward: 10708.6200 current episode reward: 19240.0000 episodes: 2633 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42100.0 seconds (11.69 hours) Timestep: 9530000 mean reward (100 episodes): 9570.0200 best mean reward: 10708.6200 current episode reward: 9840.0000 episodes: 2634 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42145.7 seconds (11.71 hours) Timestep: 9540000 mean reward (100 episodes): 9626.6000 best mean reward: 10708.6200 current episode reward: 12618.0000 episodes: 2635 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42189.9 seconds (11.72 hours) Timestep: 9550000 mean reward (100 episodes): 9675.6600 best mean reward: 10708.6200 current episode reward: 17970.0000 episodes: 2636 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42235.2 seconds (11.73 hours) Timestep: 9560000 mean reward (100 episodes): 9764.7600 best mean reward: 10708.6200 current episode reward: 16338.0000 episodes: 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9610000 mean reward (100 episodes): 9952.4400 best mean reward: 10708.6200 current episode reward: 10830.0000 episodes: 2644 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42507.2 seconds (11.81 hours) Timestep: 9620000 mean reward (100 episodes): 9897.2400 best mean reward: 10708.6200 current episode reward: 8734.0000 episodes: 2646 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42552.3 seconds (11.82 hours) Timestep: 9630000 mean reward (100 episodes): 9919.1600 best mean reward: 10708.6200 current episode reward: 4928.0000 episodes: 2648 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42597.6 seconds (11.83 hours) Timestep: 9640000 mean reward (100 episodes): 9961.8400 best mean reward: 10708.6200 current episode reward: 10080.0000 episodes: 2649 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42643.2 seconds (11.85 hours) Timestep: 9650000 mean reward (100 episodes): 9884.3800 best mean reward: 10708.6200 current episode reward: 11118.0000 episodes: 2651 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42688.8 seconds (11.86 hours) Timestep: 9660000 mean reward (100 episodes): 9806.7800 best mean reward: 10708.6200 current episode reward: 11090.0000 episodes: 2652 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42735.2 seconds (11.87 hours) Timestep: 9670000 mean reward (100 episodes): 9817.1400 best mean reward: 10708.6200 current episode reward: 9494.0000 episodes: 2653 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42780.3 seconds (11.88 hours) Timestep: 9680000 mean reward (100 episodes): 9817.8200 best mean reward: 10708.6200 current episode reward: 17378.0000 episodes: 2654 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42825.6 seconds (11.90 hours) Timestep: 9690000 mean reward (100 episodes): 9851.2200 best mean reward: 10708.6200 current episode reward: 12090.0000 episodes: 2655 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42871.3 seconds (11.91 hours) Timestep: 9700000 mean reward (100 episodes): 9985.6000 best mean reward: 10708.6200 current episode reward: 10848.0000 episodes: 2657 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42916.6 seconds (11.92 hours) Timestep: 9710000 mean reward (100 episodes): 10028.8000 best mean reward: 10708.6200 current episode reward: 9888.0000 episodes: 2658 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42962.4 seconds (11.93 hours) Timestep: 9720000 mean reward (100 episodes): 10093.9800 best mean reward: 10708.6200 current episode reward: 8610.0000 episodes: 2660 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43007.1 seconds (11.95 hours) Timestep: 9730000 mean reward (100 episodes): 10008.6400 best mean reward: 10708.6200 current episode reward: 7926.0000 episodes: 2661 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43052.5 seconds (11.96 hours) Timestep: 9740000 mean reward (100 episodes): 9958.1800 best mean reward: 10708.6200 current episode reward: 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hours) Timestep: 9790000 mean reward (100 episodes): 10001.0200 best mean reward: 10708.6200 current episode reward: 6322.0000 episodes: 2670 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43323.8 seconds (12.03 hours) Timestep: 9800000 mean reward (100 episodes): 9969.9200 best mean reward: 10708.6200 current episode reward: 9890.0000 episodes: 2671 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43368.2 seconds (12.05 hours) Timestep: 9810000 mean reward (100 episodes): 9980.9200 best mean reward: 10708.6200 current episode reward: 5608.0000 episodes: 2673 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43413.2 seconds (12.06 hours) Timestep: 9820000 mean reward (100 episodes): 10015.9800 best mean reward: 10708.6200 current episode reward: 9896.0000 episodes: 2674 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43458.5 seconds (12.07 hours) Timestep: 9830000 mean reward (100 episodes): 9886.2600 best mean reward: 10708.6200 current episode reward: 6444.0000 episodes: 2676 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43504.0 seconds (12.08 hours) Timestep: 9840000 mean reward (100 episodes): 9977.9800 best mean reward: 10708.6200 current episode reward: 13584.0000 episodes: 2677 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43549.0 seconds (12.10 hours) Timestep: 9850000 mean reward (100 episodes): 10067.0400 best mean reward: 10708.6200 current episode reward: 15116.0000 episodes: 2678 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43594.0 seconds (12.11 hours) Timestep: 9860000 mean reward (100 episodes): 10112.9200 best mean reward: 10708.6200 current episode reward: 12720.0000 episodes: 2679 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43639.4 seconds (12.12 hours) Timestep: 9870000 mean reward (100 episodes): 10085.7200 best mean reward: 10708.6200 current episode reward: 9220.0000 episodes: 2681 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43684.8 seconds (12.13 hours) Timestep: 9880000 mean reward (100 episodes): 9995.8800 best mean reward: 10708.6200 current episode reward: 10300.0000 episodes: 2682 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43730.0 seconds (12.15 hours) Timestep: 9890000 mean reward (100 episodes): 10078.3800 best mean reward: 10708.6200 current episode reward: 10254.0000 episodes: 2683 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43775.0 seconds (12.16 hours) Timestep: 9900000 mean reward (100 episodes): 10107.6600 best mean reward: 10708.6200 current episode reward: 5730.0000 episodes: 2685 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43819.5 seconds (12.17 hours) Timestep: 9910000 mean reward (100 episodes): 10099.1000 best mean reward: 10708.6200 current episode reward: 8374.0000 episodes: 2687 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43865.3 seconds (12.18 hours) Timestep: 9920000 mean reward (100 episodes): 10034.0400 best mean reward: 10708.6200 current episode reward: 7386.0000 episodes: 2688 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43910.4 seconds (12.20 hours) Timestep: 9930000 mean reward (100 episodes): 10022.9800 best mean reward: 10708.6200 current episode reward: 6258.0000 episodes: 2690 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43955.4 seconds (12.21 hours) Timestep: 9940000 mean reward (100 episodes): 10018.4800 best mean reward: 10708.6200 current episode reward: 7470.0000 episodes: 2691 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44000.0 seconds (12.22 hours) Timestep: 9950000 mean reward (100 episodes): 10076.1200 best mean reward: 10708.6200 current episode reward: 18198.0000 episodes: 2692 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44046.0 seconds (12.23 hours) Timestep: 9953926 mean reward (100 episodes): 10053.9200 best mean reward: 10708.6200 current episode reward: 5280.0000 episodes: 2693 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44063.8 seconds (12.24 hours) ================================================ FILE: dqn/logs_text/Breakout_s001.text ================================================ ('AVAILABLE GPUS: ', [u'device: 0, name: TITAN X (Pascal), pci bus id: 0000:01:00.0']) task = Task Timestep: 60000 mean reward (100 episodes): 1.5200 best mean reward: 1.8200 current episode reward: 1.0000 episodes: 331 exploration: 0.94600 learning_rate: 0.00010 elapsed time: 102.5 seconds (0.03 hours) Timestep: 70000 mean reward (100 episodes): 1.5000 best mean reward: 1.8200 current episode reward: 1.0000 episodes: 385 exploration: 0.93700 learning_rate: 0.00010 elapsed time: 136.7 seconds (0.04 hours) Timestep: 80000 mean reward (100 episodes): 1.7200 best mean reward: 1.8200 current episode reward: 1.0000 episodes: 441 exploration: 0.92800 learning_rate: 0.00010 elapsed time: 170.3 seconds (0.05 hours) Timestep: 90000 mean reward (100 episodes): 1.4700 best mean reward: 1.8200 current episode reward: 5.0000 episodes: 499 exploration: 0.91900 learning_rate: 0.00010 elapsed time: 203.9 seconds (0.06 hours) Timestep: 100000 mean reward (100 episodes): 1.6900 best mean reward: 1.8200 current episode reward: 3.0000 episodes: 554 exploration: 0.91000 learning_rate: 0.00010 elapsed time: 238.1 seconds (0.07 hours) Timestep: 110000 mean reward (100 episodes): 1.5100 best mean reward: 1.8200 current episode reward: 2.0000 episodes: 614 exploration: 0.90100 learning_rate: 0.00010 elapsed time: 272.0 seconds (0.08 hours) Timestep: 120000 mean reward (100 episodes): 1.4300 best mean reward: 1.8200 current episode reward: 2.0000 episodes: 672 exploration: 0.89200 learning_rate: 0.00010 elapsed time: 306.0 seconds (0.08 hours) Timestep: 130000 mean reward (100 episodes): 1.7500 best mean reward: 1.8200 current episode reward: 7.0000 episodes: 722 exploration: 0.88300 learning_rate: 0.00010 elapsed time: 339.7 seconds (0.09 hours) Timestep: 140000 mean reward (100 episodes): 1.7400 best mean reward: 1.8200 current episode reward: 4.0000 episodes: 778 exploration: 0.87400 learning_rate: 0.00010 elapsed time: 374.2 seconds (0.10 hours) Timestep: 150000 mean reward (100 episodes): 1.5500 best mean reward: 1.8200 current episode reward: 1.0000 episodes: 836 exploration: 0.86500 learning_rate: 0.00010 elapsed time: 408.0 seconds (0.11 hours) Timestep: 160000 mean reward (100 episodes): 1.5300 best mean reward: 1.8200 current episode reward: 1.0000 episodes: 895 exploration: 0.85600 learning_rate: 0.00010 elapsed time: 441.9 seconds (0.12 hours) Timestep: 170000 mean reward (100 episodes): 1.4200 best mean reward: 1.8200 current episode reward: 0.0000 episodes: 952 exploration: 0.84700 learning_rate: 0.00010 elapsed time: 476.0 seconds (0.13 hours) Timestep: 180000 mean reward (100 episodes): 1.5200 best mean reward: 1.8200 current episode reward: 2.0000 episodes: 1006 exploration: 0.83800 learning_rate: 0.00010 elapsed time: 511.2 seconds (0.14 hours) Timestep: 190000 mean reward (100 episodes): 1.5900 best mean reward: 1.8200 current episode reward: 0.0000 episodes: 1063 exploration: 0.82900 learning_rate: 0.00010 elapsed time: 545.7 seconds (0.15 hours) Timestep: 200000 mean reward (100 episodes): 1.4000 best mean reward: 1.8200 current episode reward: 1.0000 episodes: 1121 exploration: 0.82000 learning_rate: 0.00010 elapsed time: 580.2 seconds (0.16 hours) Timestep: 210000 mean reward (100 episodes): 1.5200 best mean reward: 1.8200 current episode reward: 3.0000 episodes: 1175 exploration: 0.81100 learning_rate: 0.00010 elapsed time: 614.4 seconds (0.17 hours) Timestep: 220000 mean reward (100 episodes): 1.6700 best mean reward: 1.8200 current episode reward: 4.0000 episodes: 1229 exploration: 0.80200 learning_rate: 0.00010 elapsed time: 649.2 seconds (0.18 hours) Timestep: 230000 mean reward (100 episodes): 1.9000 best mean reward: 1.9000 current episode reward: 3.0000 episodes: 1279 exploration: 0.79300 learning_rate: 0.00010 elapsed time: 684.1 seconds (0.19 hours) Timestep: 240000 mean reward (100 episodes): 2.0300 best mean reward: 2.1100 current episode reward: 4.0000 episodes: 1330 exploration: 0.78400 learning_rate: 0.00010 elapsed time: 718.9 seconds (0.20 hours) Timestep: 250000 mean reward (100 episodes): 1.9100 best mean reward: 2.1300 current episode reward: 2.0000 episodes: 1383 exploration: 0.77500 learning_rate: 0.00010 elapsed time: 753.7 seconds (0.21 hours) Timestep: 260000 mean reward (100 episodes): 1.9000 best mean reward: 2.1300 current episode reward: 0.0000 episodes: 1436 exploration: 0.76600 learning_rate: 0.00010 elapsed time: 788.5 seconds (0.22 hours) Timestep: 270000 mean reward (100 episodes): 2.1400 best mean reward: 2.1400 current episode reward: 4.0000 episodes: 1483 exploration: 0.75700 learning_rate: 0.00010 elapsed time: 823.5 seconds (0.23 hours) Timestep: 280000 mean reward (100 episodes): 2.4000 best mean reward: 2.4100 current episode reward: 2.0000 episodes: 1530 exploration: 0.74800 learning_rate: 0.00010 elapsed time: 858.8 seconds (0.24 hours) Timestep: 290000 mean reward (100 episodes): 2.5000 best mean reward: 2.5900 current episode reward: 0.0000 episodes: 1575 exploration: 0.73900 learning_rate: 0.00010 elapsed time: 894.1 seconds (0.25 hours) Timestep: 300000 mean reward (100 episodes): 2.8400 best mean reward: 2.8400 current episode reward: 5.0000 episodes: 1619 exploration: 0.73000 learning_rate: 0.00010 elapsed time: 929.4 seconds (0.26 hours) Timestep: 310000 mean reward (100 episodes): 3.1500 best mean reward: 3.2400 current episode reward: 4.0000 episodes: 1657 exploration: 0.72100 learning_rate: 0.00010 elapsed time: 964.7 seconds (0.27 hours) Timestep: 320000 mean reward (100 episodes): 3.5100 best mean reward: 3.5700 current episode reward: 3.0000 episodes: 1696 exploration: 0.71200 learning_rate: 0.00010 elapsed time: 1000.2 seconds (0.28 hours) Timestep: 330000 mean reward (100 episodes): 4.0300 best mean reward: 4.0300 current episode reward: 5.0000 episodes: 1731 exploration: 0.70300 learning_rate: 0.00010 elapsed time: 1035.5 seconds (0.29 hours) Timestep: 340000 mean reward (100 episodes): 4.0800 best mean reward: 4.0800 current episode reward: 4.0000 episodes: 1768 exploration: 0.69400 learning_rate: 0.00010 elapsed time: 1071.3 seconds (0.30 hours) Timestep: 350000 mean reward (100 episodes): 4.5300 best mean reward: 4.6200 current episode reward: 3.0000 episodes: 1798 exploration: 0.68500 learning_rate: 0.00010 elapsed time: 1106.9 seconds (0.31 hours) Timestep: 360000 mean reward (100 episodes): 4.6200 best mean reward: 4.6900 current episode reward: 2.0000 episodes: 1833 exploration: 0.67600 learning_rate: 0.00010 elapsed time: 1142.8 seconds (0.32 hours) Timestep: 370000 mean reward (100 episodes): 5.0300 best mean reward: 5.0600 current episode reward: 6.0000 episodes: 1864 exploration: 0.66700 learning_rate: 0.00010 elapsed time: 1178.5 seconds (0.33 hours) Timestep: 380000 mean reward (100 episodes): 5.1200 best mean reward: 5.4000 current episode reward: 3.0000 episodes: 1895 exploration: 0.65800 learning_rate: 0.00010 elapsed time: 1214.1 seconds (0.34 hours) Timestep: 390000 mean reward (100 episodes): 5.5900 best mean reward: 5.5900 current episode reward: 6.0000 episodes: 1924 exploration: 0.64900 learning_rate: 0.00010 elapsed time: 1250.2 seconds (0.35 hours) Timestep: 400000 mean reward (100 episodes): 5.6200 best mean reward: 5.7500 current episode reward: 5.0000 episodes: 1956 exploration: 0.64000 learning_rate: 0.00010 elapsed time: 1286.5 seconds (0.36 hours) Timestep: 410000 mean reward (100 episodes): 5.7000 best mean reward: 5.7500 current episode reward: 2.0000 episodes: 1986 exploration: 0.63100 learning_rate: 0.00010 elapsed time: 1322.7 seconds (0.37 hours) Timestep: 420000 mean reward (100 episodes): 5.5800 best mean reward: 5.7500 current episode reward: 4.0000 episodes: 2014 exploration: 0.62200 learning_rate: 0.00010 elapsed time: 1360.6 seconds (0.38 hours) Timestep: 430000 mean reward (100 episodes): 5.5400 best mean reward: 5.7500 current episode reward: 9.0000 episodes: 2046 exploration: 0.61300 learning_rate: 0.00010 elapsed time: 1397.2 seconds (0.39 hours) Timestep: 440000 mean reward (100 episodes): 5.9200 best mean reward: 5.9400 current episode reward: 5.0000 episodes: 2073 exploration: 0.60400 learning_rate: 0.00010 elapsed time: 1434.1 seconds (0.40 hours) Timestep: 450000 mean reward (100 episodes): 6.0600 best mean reward: 6.1000 current episode reward: 5.0000 episodes: 2102 exploration: 0.59500 learning_rate: 0.00010 elapsed time: 1471.0 seconds (0.41 hours) Timestep: 460000 mean reward (100 episodes): 6.0700 best mean reward: 6.1300 current episode reward: 5.0000 episodes: 2131 exploration: 0.58600 learning_rate: 0.00010 elapsed time: 1508.5 seconds (0.42 hours) Timestep: 470000 mean reward (100 episodes): 6.3500 best mean reward: 6.4600 current episode reward: 7.0000 episodes: 2158 exploration: 0.57700 learning_rate: 0.00010 elapsed time: 1546.4 seconds (0.43 hours) Timestep: 480000 mean reward (100 episodes): 6.2200 best mean reward: 6.5200 current episode reward: 7.0000 episodes: 2185 exploration: 0.56800 learning_rate: 0.00010 elapsed time: 1583.4 seconds (0.44 hours) Timestep: 490000 mean reward (100 episodes): 6.5600 best mean reward: 6.6200 current episode reward: 9.0000 episodes: 2212 exploration: 0.55900 learning_rate: 0.00010 elapsed time: 1620.8 seconds (0.45 hours) Timestep: 500000 mean reward (100 episodes): 6.9100 best mean reward: 6.9400 current episode reward: 13.0000 episodes: 2238 exploration: 0.55000 learning_rate: 0.00010 elapsed time: 1658.2 seconds (0.46 hours) Timestep: 510000 mean reward (100 episodes): 7.2200 best mean reward: 7.3300 current episode reward: 4.0000 episodes: 2263 exploration: 0.54100 learning_rate: 0.00010 elapsed time: 1695.5 seconds (0.47 hours) Timestep: 520000 mean reward (100 episodes): 7.3300 best mean reward: 7.4300 current episode reward: 10.0000 episodes: 2289 exploration: 0.53200 learning_rate: 0.00010 elapsed time: 1733.5 seconds (0.48 hours) Timestep: 530000 mean reward (100 episodes): 7.7200 best mean reward: 7.8900 current episode reward: 4.0000 episodes: 2315 exploration: 0.52300 learning_rate: 0.00010 elapsed time: 1770.6 seconds (0.49 hours) Timestep: 540000 mean reward (100 episodes): 7.5900 best mean reward: 7.8900 current episode reward: 2.0000 episodes: 2343 exploration: 0.51400 learning_rate: 0.00010 elapsed time: 1808.0 seconds (0.50 hours) Timestep: 550000 mean reward (100 episodes): 7.5700 best mean reward: 7.8900 current episode reward: 8.0000 episodes: 2368 exploration: 0.50500 learning_rate: 0.00010 elapsed time: 1846.2 seconds (0.51 hours) Timestep: 560000 mean reward (100 episodes): 7.3200 best mean reward: 7.8900 current episode reward: 7.0000 episodes: 2396 exploration: 0.49600 learning_rate: 0.00010 elapsed time: 1884.0 seconds (0.52 hours) Timestep: 570000 mean reward (100 episodes): 7.5200 best mean reward: 7.8900 current episode reward: 6.0000 episodes: 2421 exploration: 0.48700 learning_rate: 0.00010 elapsed time: 1922.1 seconds (0.53 hours) Timestep: 580000 mean reward (100 episodes): 7.5500 best mean reward: 7.8900 current episode reward: 10.0000 episodes: 2447 exploration: 0.47800 learning_rate: 0.00010 elapsed time: 1960.0 seconds (0.54 hours) Timestep: 590000 mean reward (100 episodes): 7.8200 best mean reward: 7.8900 current episode reward: 8.0000 episodes: 2470 exploration: 0.46900 learning_rate: 0.00010 elapsed time: 1998.4 seconds (0.56 hours) Timestep: 600000 mean reward (100 episodes): 8.2500 best mean reward: 8.2500 current episode reward: 5.0000 episodes: 2493 exploration: 0.46000 learning_rate: 0.00010 elapsed time: 2037.0 seconds (0.57 hours) Timestep: 610000 mean reward (100 episodes): 8.6000 best mean reward: 8.7800 current episode reward: 8.0000 episodes: 2516 exploration: 0.45100 learning_rate: 0.00010 elapsed time: 2074.9 seconds (0.58 hours) Timestep: 620000 mean reward (100 episodes): 8.8400 best mean reward: 8.8600 current episode reward: 7.0000 episodes: 2540 exploration: 0.44200 learning_rate: 0.00010 elapsed time: 2114.5 seconds (0.59 hours) Timestep: 630000 mean reward (100 episodes): 8.7800 best mean reward: 8.9500 current episode reward: 15.0000 episodes: 2564 exploration: 0.43300 learning_rate: 0.00010 elapsed time: 2153.9 seconds (0.60 hours) Timestep: 640000 mean reward (100 episodes): 8.8100 best mean reward: 8.9500 current episode reward: 3.0000 episodes: 2586 exploration: 0.42400 learning_rate: 0.00010 elapsed time: 2193.2 seconds (0.61 hours) Timestep: 650000 mean reward (100 episodes): 8.6300 best mean reward: 9.0500 current episode reward: 5.0000 episodes: 2610 exploration: 0.41500 learning_rate: 0.00010 elapsed time: 2231.9 seconds (0.62 hours) Timestep: 660000 mean reward (100 episodes): 8.9600 best mean reward: 9.0500 current episode reward: 10.0000 episodes: 2631 exploration: 0.40600 learning_rate: 0.00010 elapsed time: 2270.2 seconds (0.63 hours) Timestep: 670000 mean reward (100 episodes): 9.4500 best mean reward: 9.4500 current episode reward: 19.0000 episodes: 2654 exploration: 0.39700 learning_rate: 0.00010 elapsed time: 2309.1 seconds (0.64 hours) Timestep: 680000 mean reward (100 episodes): 9.8700 best mean reward: 10.0200 current episode reward: 7.0000 episodes: 2675 exploration: 0.38800 learning_rate: 0.00010 elapsed time: 2348.1 seconds (0.65 hours) Timestep: 690000 mean reward (100 episodes): 9.9800 best mean reward: 10.0200 current episode reward: 9.0000 episodes: 2696 exploration: 0.37900 learning_rate: 0.00010 elapsed time: 2387.0 seconds (0.66 hours) Timestep: 700000 mean reward (100 episodes): 10.3600 best mean reward: 10.4100 current episode reward: 1.0000 episodes: 2718 exploration: 0.37000 learning_rate: 0.00010 elapsed time: 2426.2 seconds (0.67 hours) Timestep: 710000 mean reward (100 episodes): 10.2100 best mean reward: 10.4800 current episode reward: 15.0000 episodes: 2741 exploration: 0.36100 learning_rate: 0.00010 elapsed time: 2465.8 seconds (0.68 hours) Timestep: 720000 mean reward (100 episodes): 10.3000 best mean reward: 10.5200 current episode reward: 11.0000 episodes: 2760 exploration: 0.35200 learning_rate: 0.00010 elapsed time: 2505.5 seconds (0.70 hours) Timestep: 730000 mean reward (100 episodes): 10.4600 best mean reward: 10.6400 current episode reward: 9.0000 episodes: 2782 exploration: 0.34300 learning_rate: 0.00010 elapsed time: 2544.8 seconds (0.71 hours) Timestep: 740000 mean reward (100 episodes): 10.5100 best mean reward: 10.6400 current episode reward: 15.0000 episodes: 2804 exploration: 0.33400 learning_rate: 0.00010 elapsed time: 2584.7 seconds (0.72 hours) Timestep: 750000 mean reward (100 episodes): 11.2000 best mean reward: 11.2300 current episode reward: 11.0000 episodes: 2820 exploration: 0.32500 learning_rate: 0.00010 elapsed time: 2624.3 seconds (0.73 hours) Timestep: 760000 mean reward (100 episodes): 12.1700 best mean reward: 12.1700 current episode reward: 5.0000 episodes: 2838 exploration: 0.31600 learning_rate: 0.00010 elapsed time: 2665.3 seconds (0.74 hours) Timestep: 770000 mean reward (100 episodes): 13.0800 best mean reward: 13.1100 current episode reward: 16.0000 episodes: 2854 exploration: 0.30700 learning_rate: 0.00010 elapsed time: 2706.5 seconds (0.75 hours) Timestep: 780000 mean reward (100 episodes): 13.5100 best mean reward: 13.5100 current episode reward: 21.0000 episodes: 2873 exploration: 0.29800 learning_rate: 0.00010 elapsed time: 2747.8 seconds (0.76 hours) Timestep: 790000 mean reward (100 episodes): 14.2300 best mean reward: 14.3100 current episode reward: 10.0000 episodes: 2891 exploration: 0.28900 learning_rate: 0.00010 elapsed time: 2789.2 seconds (0.77 hours) Timestep: 800000 mean reward (100 episodes): 14.2600 best mean reward: 14.5000 current episode reward: 13.0000 episodes: 2910 exploration: 0.28000 learning_rate: 0.00010 elapsed time: 2830.1 seconds (0.79 hours) Timestep: 810000 mean reward (100 episodes): 14.3300 best mean reward: 14.6000 current episode reward: 13.0000 episodes: 2927 exploration: 0.27100 learning_rate: 0.00010 elapsed time: 2871.5 seconds (0.80 hours) Timestep: 820000 mean reward (100 episodes): 14.8300 best mean reward: 14.9500 current episode reward: 18.0000 episodes: 2943 exploration: 0.26200 learning_rate: 0.00010 elapsed time: 2912.6 seconds (0.81 hours) Timestep: 830000 mean reward (100 episodes): 15.4000 best mean reward: 15.5300 current episode reward: 15.0000 episodes: 2957 exploration: 0.25300 learning_rate: 0.00010 elapsed time: 2953.6 seconds (0.82 hours) Timestep: 840000 mean reward (100 episodes): 16.6500 best mean reward: 16.6500 current episode reward: 38.0000 episodes: 2970 exploration: 0.24400 learning_rate: 0.00010 elapsed time: 2995.5 seconds (0.83 hours) Timestep: 850000 mean reward (100 episodes): 17.1200 best mean reward: 17.1200 current episode reward: 28.0000 episodes: 2987 exploration: 0.23500 learning_rate: 0.00010 elapsed time: 3037.5 seconds (0.84 hours) Timestep: 860000 mean reward (100 episodes): 18.3200 best mean reward: 18.3200 current episode reward: 31.0000 episodes: 3001 exploration: 0.22600 learning_rate: 0.00010 elapsed time: 3082.1 seconds (0.86 hours) Timestep: 870000 mean reward (100 episodes): 19.0900 best mean reward: 19.0900 current episode reward: 39.0000 episodes: 3014 exploration: 0.21700 learning_rate: 0.00010 elapsed time: 3125.0 seconds (0.87 hours) Timestep: 880000 mean reward (100 episodes): 20.5900 best mean reward: 20.5900 current episode reward: 28.0000 episodes: 3027 exploration: 0.20800 learning_rate: 0.00010 elapsed time: 3167.8 seconds (0.88 hours) Timestep: 890000 mean reward (100 episodes): 21.4400 best mean reward: 21.4400 current episode reward: 33.0000 episodes: 3039 exploration: 0.19900 learning_rate: 0.00010 elapsed time: 3210.6 seconds (0.89 hours) Timestep: 900000 mean reward (100 episodes): 21.8600 best mean reward: 22.0300 current episode reward: 23.0000 episodes: 3051 exploration: 0.19000 learning_rate: 0.00010 elapsed time: 3253.4 seconds (0.90 hours) Timestep: 910000 mean reward (100 episodes): 22.5000 best mean reward: 22.5000 current episode reward: 33.0000 episodes: 3066 exploration: 0.18100 learning_rate: 0.00010 elapsed time: 3295.9 seconds (0.92 hours) Timestep: 920000 mean reward (100 episodes): 23.0100 best mean reward: 23.0100 current episode reward: 32.0000 episodes: 3077 exploration: 0.17200 learning_rate: 0.00010 elapsed time: 3338.8 seconds (0.93 hours) Timestep: 930000 mean reward (100 episodes): 24.0300 best mean reward: 24.0300 current episode reward: 30.0000 episodes: 3089 exploration: 0.16300 learning_rate: 0.00010 elapsed time: 3381.1 seconds (0.94 hours) Timestep: 940000 mean reward (100 episodes): 25.2400 best mean reward: 25.2400 current episode reward: 42.0000 episodes: 3101 exploration: 0.15400 learning_rate: 0.00010 elapsed time: 3424.0 seconds (0.95 hours) Timestep: 950000 mean reward (100 episodes): 26.3400 best mean reward: 26.3400 current episode reward: 34.0000 episodes: 3112 exploration: 0.14500 learning_rate: 0.00010 elapsed time: 3467.3 seconds (0.96 hours) Timestep: 960000 mean reward (100 episodes): 27.3600 best mean reward: 27.4200 current episode reward: 42.0000 episodes: 3122 exploration: 0.13600 learning_rate: 0.00010 elapsed time: 3511.3 seconds (0.98 hours) Timestep: 970000 mean reward (100 episodes): 28.3200 best mean reward: 28.3200 current episode reward: 37.0000 episodes: 3133 exploration: 0.12700 learning_rate: 0.00010 elapsed time: 3554.2 seconds (0.99 hours) Timestep: 980000 mean reward (100 episodes): 29.0700 best mean reward: 29.0700 current episode reward: 34.0000 episodes: 3143 exploration: 0.11800 learning_rate: 0.00010 elapsed time: 3598.1 seconds (1.00 hours) Timestep: 990000 mean reward (100 episodes): 30.9100 best mean reward: 30.9600 current episode reward: 11.0000 episodes: 3153 exploration: 0.10900 learning_rate: 0.00010 elapsed time: 3641.5 seconds (1.01 hours) Timestep: 1000000 mean reward (100 episodes): 32.5100 best mean reward: 32.5100 current episode reward: 25.0000 episodes: 3163 exploration: 0.10000 learning_rate: 0.00010 elapsed time: 3684.6 seconds (1.02 hours) Timestep: 1010000 mean reward (100 episodes): 34.2300 best mean reward: 34.2300 current episode reward: 19.0000 episodes: 3173 exploration: 0.09978 learning_rate: 0.00010 elapsed time: 3727.4 seconds (1.04 hours) Timestep: 1020000 mean reward (100 episodes): 35.4700 best mean reward: 35.4700 current episode reward: 42.0000 episodes: 3183 exploration: 0.09955 learning_rate: 0.00010 elapsed time: 3770.7 seconds (1.05 hours) Timestep: 1030000 mean reward (100 episodes): 37.0400 best mean reward: 37.0400 current episode reward: 78.0000 episodes: 3191 exploration: 0.09933 learning_rate: 0.00010 elapsed time: 3813.8 seconds (1.06 hours) Timestep: 1040000 mean reward (100 episodes): 37.8800 best mean reward: 37.8800 current episode reward: 58.0000 episodes: 3201 exploration: 0.09910 learning_rate: 0.00010 elapsed time: 3857.4 seconds (1.07 hours) Timestep: 1050000 mean reward (100 episodes): 39.5500 best mean reward: 39.5900 current episode reward: 46.0000 episodes: 3211 exploration: 0.09888 learning_rate: 0.00010 elapsed time: 3901.2 seconds (1.08 hours) Timestep: 1060000 mean reward (100 episodes): 40.2400 best mean reward: 40.3000 current episode reward: 38.0000 episodes: 3220 exploration: 0.09865 learning_rate: 0.00010 elapsed time: 3944.7 seconds (1.10 hours) Timestep: 1070000 mean reward (100 episodes): 40.7500 best mean reward: 40.7500 current episode reward: 74.0000 episodes: 3229 exploration: 0.09842 learning_rate: 0.00010 elapsed time: 3988.4 seconds (1.11 hours) Timestep: 1080000 mean reward (100 episodes): 41.4200 best mean reward: 41.4200 current episode reward: 24.0000 episodes: 3239 exploration: 0.09820 learning_rate: 0.00010 elapsed time: 4031.8 seconds (1.12 hours) Timestep: 1090000 mean reward (100 episodes): 42.0700 best mean reward: 42.0700 current episode reward: 86.0000 episodes: 3248 exploration: 0.09798 learning_rate: 0.00010 elapsed time: 4075.1 seconds (1.13 hours) Timestep: 1100000 mean reward (100 episodes): 43.2100 best mean reward: 43.2100 current episode reward: 77.0000 episodes: 3256 exploration: 0.09775 learning_rate: 0.00010 elapsed time: 4118.6 seconds (1.14 hours) Timestep: 1110000 mean reward (100 episodes): 43.8300 best mean reward: 43.8300 current episode reward: 54.0000 episodes: 3264 exploration: 0.09753 learning_rate: 0.00010 elapsed time: 4162.6 seconds (1.16 hours) Timestep: 1120000 mean reward (100 episodes): 44.6800 best mean reward: 44.6800 current episode reward: 46.0000 episodes: 3273 exploration: 0.09730 learning_rate: 0.00010 elapsed time: 4206.5 seconds (1.17 hours) Timestep: 1130000 mean reward (100 episodes): 44.9000 best mean reward: 44.9000 current episode reward: 61.0000 episodes: 3281 exploration: 0.09708 learning_rate: 0.00010 elapsed time: 4249.6 seconds (1.18 hours) Timestep: 1140000 mean reward (100 episodes): 45.4400 best mean reward: 45.7700 current episode reward: 45.0000 episodes: 3290 exploration: 0.09685 learning_rate: 0.00010 elapsed time: 4293.2 seconds (1.19 hours) Timestep: 1150000 mean reward (100 episodes): 45.6200 best mean reward: 45.7700 current episode reward: 40.0000 episodes: 3299 exploration: 0.09663 learning_rate: 0.00010 elapsed time: 4336.9 seconds (1.20 hours) Timestep: 1160000 mean reward (100 episodes): 45.4900 best mean reward: 46.5500 current episode reward: 33.0000 episodes: 3308 exploration: 0.09640 learning_rate: 0.00010 elapsed time: 4380.7 seconds (1.22 hours) Timestep: 1170000 mean reward (100 episodes): 46.2700 best mean reward: 47.1200 current episode reward: 48.0000 episodes: 3318 exploration: 0.09618 learning_rate: 0.00010 elapsed time: 4423.9 seconds (1.23 hours) Timestep: 1180000 mean reward (100 episodes): 46.8500 best mean reward: 47.1200 current episode reward: 64.0000 episodes: 3327 exploration: 0.09595 learning_rate: 0.00010 elapsed time: 4467.3 seconds (1.24 hours) Timestep: 1190000 mean reward (100 episodes): 47.9400 best mean reward: 47.9400 current episode reward: 75.0000 episodes: 3335 exploration: 0.09573 learning_rate: 0.00010 elapsed time: 4511.7 seconds (1.25 hours) Timestep: 1200000 mean reward (100 episodes): 48.0400 best mean reward: 48.1500 current episode reward: 33.0000 episodes: 3344 exploration: 0.09550 learning_rate: 0.00010 elapsed time: 4555.0 seconds (1.27 hours) Timestep: 1210000 mean reward (100 episodes): 48.3100 best mean reward: 48.4700 current episode reward: 82.0000 episodes: 3352 exploration: 0.09527 learning_rate: 0.00010 elapsed time: 4599.2 seconds (1.28 hours) Timestep: 1220000 mean reward (100 episodes): 48.7100 best mean reward: 48.7100 current episode reward: 50.0000 episodes: 3360 exploration: 0.09505 learning_rate: 0.00010 elapsed time: 4643.0 seconds (1.29 hours) Timestep: 1230000 mean reward (100 episodes): 48.7900 best mean reward: 49.4700 current episode reward: 0.0000 episodes: 3369 exploration: 0.09483 learning_rate: 0.00010 elapsed time: 4687.3 seconds (1.30 hours) Timestep: 1240000 mean reward (100 episodes): 48.3000 best mean reward: 49.4700 current episode reward: 19.0000 episodes: 3378 exploration: 0.09460 learning_rate: 0.00010 elapsed time: 4731.3 seconds (1.31 hours) Timestep: 1250000 mean reward (100 episodes): 48.3500 best mean reward: 49.4700 current episode reward: 76.0000 episodes: 3387 exploration: 0.09438 learning_rate: 0.00010 elapsed time: 4775.2 seconds (1.33 hours) Timestep: 1260000 mean reward (100 episodes): 49.2100 best mean reward: 49.6700 current episode reward: 56.0000 episodes: 3396 exploration: 0.09415 learning_rate: 0.00010 elapsed time: 4819.4 seconds (1.34 hours) Timestep: 1270000 mean reward (100 episodes): 49.7500 best mean reward: 49.7500 current episode reward: 59.0000 episodes: 3404 exploration: 0.09393 learning_rate: 0.00010 elapsed time: 4863.2 seconds (1.35 hours) Timestep: 1280000 mean reward (100 episodes): 50.6200 best mean reward: 50.6200 current episode reward: 69.0000 episodes: 3412 exploration: 0.09370 learning_rate: 0.00010 elapsed time: 4906.5 seconds (1.36 hours) Timestep: 1290000 mean reward (100 episodes): 51.0400 best mean reward: 51.2000 current episode reward: 74.0000 episodes: 3421 exploration: 0.09348 learning_rate: 0.00010 elapsed time: 4950.0 seconds (1.38 hours) Timestep: 1300000 mean reward (100 episodes): 53.0000 best mean reward: 53.0000 current episode reward: 70.0000 episodes: 3428 exploration: 0.09325 learning_rate: 0.00010 elapsed time: 4993.6 seconds (1.39 hours) Timestep: 1310000 mean reward (100 episodes): 52.4300 best mean reward: 53.0700 current episode reward: 64.0000 episodes: 3437 exploration: 0.09303 learning_rate: 0.00010 elapsed time: 5038.2 seconds (1.40 hours) Timestep: 1320000 mean reward (100 episodes): 53.0900 best mean reward: 53.2400 current episode reward: 61.0000 episodes: 3446 exploration: 0.09280 learning_rate: 0.00010 elapsed time: 5082.2 seconds (1.41 hours) Timestep: 1330000 mean reward (100 episodes): 53.1400 best mean reward: 53.2400 current episode reward: 61.0000 episodes: 3454 exploration: 0.09258 learning_rate: 0.00010 elapsed time: 5126.0 seconds (1.42 hours) Timestep: 1340000 mean reward (100 episodes): 52.3600 best mean reward: 53.2400 current episode reward: 57.0000 episodes: 3463 exploration: 0.09235 learning_rate: 0.00010 elapsed time: 5170.4 seconds (1.44 hours) Timestep: 1350000 mean reward (100 episodes): 51.9900 best mean reward: 53.2400 current episode reward: 95.0000 episodes: 3473 exploration: 0.09213 learning_rate: 0.00010 elapsed time: 5214.7 seconds (1.45 hours) Timestep: 1360000 mean reward (100 episodes): 52.4500 best mean reward: 53.2400 current episode reward: 59.0000 episodes: 3481 exploration: 0.09190 learning_rate: 0.00010 elapsed time: 5258.7 seconds (1.46 hours) Timestep: 1370000 mean reward (100 episodes): 53.4500 best mean reward: 53.5900 current episode reward: 41.0000 episodes: 3489 exploration: 0.09168 learning_rate: 0.00010 elapsed time: 5303.2 seconds (1.47 hours) Timestep: 1380000 mean reward (100 episodes): 53.9800 best mean reward: 54.0300 current episode reward: 57.0000 episodes: 3497 exploration: 0.09145 learning_rate: 0.00010 elapsed time: 5347.5 seconds (1.49 hours) Timestep: 1390000 mean reward (100 episodes): 54.7000 best mean reward: 54.7000 current episode reward: 60.0000 episodes: 3505 exploration: 0.09123 learning_rate: 0.00010 elapsed time: 5392.3 seconds (1.50 hours) Timestep: 1400000 mean reward (100 episodes): 54.5800 best mean reward: 54.7000 current episode reward: 70.0000 episodes: 3513 exploration: 0.09100 learning_rate: 0.00010 elapsed time: 5436.1 seconds (1.51 hours) Timestep: 1410000 mean reward (100 episodes): 54.0600 best mean reward: 55.0200 current episode reward: 76.0000 episodes: 3523 exploration: 0.09078 learning_rate: 0.00009 elapsed time: 5480.8 seconds (1.52 hours) Timestep: 1420000 mean reward (100 episodes): 52.0400 best mean reward: 55.0200 current episode reward: 70.0000 episodes: 3533 exploration: 0.09055 learning_rate: 0.00009 elapsed time: 5523.8 seconds (1.53 hours) Timestep: 1430000 mean reward (100 episodes): 52.6600 best mean reward: 55.0200 current episode reward: 51.0000 episodes: 3540 exploration: 0.09033 learning_rate: 0.00009 elapsed time: 5567.4 seconds (1.55 hours) Timestep: 1440000 mean reward (100 episodes): 53.1600 best mean reward: 55.0200 current episode reward: 51.0000 episodes: 3549 exploration: 0.09010 learning_rate: 0.00009 elapsed time: 5610.5 seconds (1.56 hours) Timestep: 1450000 mean reward (100 episodes): 53.9900 best mean reward: 55.0200 current episode reward: 85.0000 episodes: 3557 exploration: 0.08988 learning_rate: 0.00009 elapsed time: 5653.9 seconds (1.57 hours) Timestep: 1460000 mean reward (100 episodes): 54.6500 best mean reward: 55.0200 current episode reward: 86.0000 episodes: 3565 exploration: 0.08965 learning_rate: 0.00009 elapsed time: 5697.6 seconds (1.58 hours) Timestep: 1470000 mean reward (100 episodes): 57.1400 best mean reward: 57.1400 current episode reward: 83.0000 episodes: 3572 exploration: 0.08943 learning_rate: 0.00009 elapsed time: 5741.2 seconds (1.59 hours) Timestep: 1480000 mean reward (100 episodes): 58.3600 best mean reward: 58.6500 current episode reward: 37.0000 episodes: 3580 exploration: 0.08920 learning_rate: 0.00009 elapsed time: 5784.9 seconds (1.61 hours) Timestep: 1490000 mean reward (100 episodes): 59.0100 best mean reward: 59.0100 current episode reward: 74.0000 episodes: 3587 exploration: 0.08897 learning_rate: 0.00009 elapsed time: 5828.8 seconds (1.62 hours) Timestep: 1500000 mean reward (100 episodes): 60.0200 best mean reward: 60.0900 current episode reward: 63.0000 episodes: 3594 exploration: 0.08875 learning_rate: 0.00009 elapsed time: 5871.2 seconds (1.63 hours) Timestep: 1510000 mean reward (100 episodes): 62.6900 best mean reward: 62.6900 current episode reward: 76.0000 episodes: 3601 exploration: 0.08853 learning_rate: 0.00009 elapsed time: 5915.5 seconds (1.64 hours) Timestep: 1520000 mean reward (100 episodes): 53.7900 best mean reward: 62.6900 current episode reward: 8.0000 episodes: 3623 exploration: 0.08830 learning_rate: 0.00009 elapsed time: 5959.2 seconds (1.66 hours) Timestep: 1530000 mean reward (100 episodes): 48.2900 best mean reward: 62.6900 current episode reward: 65.0000 episodes: 3640 exploration: 0.08808 learning_rate: 0.00009 elapsed time: 6003.7 seconds (1.67 hours) Timestep: 1540000 mean reward (100 episodes): 48.2400 best mean reward: 62.6900 current episode reward: 63.0000 episodes: 3647 exploration: 0.08785 learning_rate: 0.00009 elapsed time: 6047.4 seconds (1.68 hours) Timestep: 1550000 mean reward (100 episodes): 49.2100 best mean reward: 62.6900 current episode reward: 66.0000 episodes: 3655 exploration: 0.08763 learning_rate: 0.00009 elapsed time: 6092.1 seconds (1.69 hours) Timestep: 1560000 mean reward (100 episodes): 48.5300 best mean reward: 62.6900 current episode reward: 4.0000 episodes: 3664 exploration: 0.08740 learning_rate: 0.00009 elapsed time: 6135.5 seconds (1.70 hours) Timestep: 1570000 mean reward (100 episodes): 47.8000 best mean reward: 62.6900 current episode reward: 86.0000 episodes: 3672 exploration: 0.08718 learning_rate: 0.00009 elapsed time: 6179.5 seconds (1.72 hours) Timestep: 1580000 mean reward (100 episodes): 47.7100 best mean reward: 62.6900 current episode reward: 92.0000 episodes: 3680 exploration: 0.08695 learning_rate: 0.00009 elapsed time: 6223.6 seconds (1.73 hours) Timestep: 1590000 mean reward (100 episodes): 37.2400 best mean reward: 62.6900 current episode reward: 67.0000 episodes: 3699 exploration: 0.08673 learning_rate: 0.00009 elapsed time: 6267.5 seconds (1.74 hours) Timestep: 1600000 mean reward (100 episodes): 39.0500 best mean reward: 62.6900 current episode reward: 50.0000 episodes: 3707 exploration: 0.08650 learning_rate: 0.00009 elapsed time: 6311.2 seconds (1.75 hours) Timestep: 1610000 mean reward (100 episodes): 43.0800 best mean reward: 62.6900 current episode reward: 67.0000 episodes: 3715 exploration: 0.08628 learning_rate: 0.00009 elapsed time: 6354.7 seconds (1.77 hours) Timestep: 1620000 mean reward (100 episodes): 47.3700 best mean reward: 62.6900 current episode reward: 48.0000 episodes: 3725 exploration: 0.08605 learning_rate: 0.00009 elapsed time: 6399.5 seconds (1.78 hours) Timestep: 1630000 mean reward (100 episodes): 50.8100 best mean reward: 62.6900 current episode reward: 5.0000 episodes: 3740 exploration: 0.08582 learning_rate: 0.00009 elapsed time: 6443.5 seconds (1.79 hours) Timestep: 1640000 mean reward (100 episodes): 45.9900 best mean reward: 62.6900 current episode reward: 44.0000 episodes: 3753 exploration: 0.08560 learning_rate: 0.00009 elapsed time: 6487.6 seconds (1.80 hours) Timestep: 1650000 mean reward (100 episodes): 48.6700 best mean reward: 62.6900 current episode reward: 214.0000 episodes: 3760 exploration: 0.08538 learning_rate: 0.00009 elapsed time: 6531.4 seconds (1.81 hours) Timestep: 1660000 mean reward (100 episodes): 47.0200 best mean reward: 62.6900 current episode reward: 80.0000 episodes: 3771 exploration: 0.08515 learning_rate: 0.00009 elapsed time: 6575.2 seconds (1.83 hours) Timestep: 1670000 mean reward (100 episodes): 45.2800 best mean reward: 62.6900 current episode reward: 13.0000 episodes: 3783 exploration: 0.08493 learning_rate: 0.00009 elapsed time: 6620.0 seconds (1.84 hours) Timestep: 1680000 mean reward (100 episodes): 50.6100 best mean reward: 62.6900 current episode reward: 35.0000 episodes: 3791 exploration: 0.08470 learning_rate: 0.00009 elapsed time: 6663.9 seconds (1.85 hours) Timestep: 1690000 mean reward (100 episodes): 51.5800 best mean reward: 62.6900 current episode reward: 69.0000 episodes: 3800 exploration: 0.08448 learning_rate: 0.00009 elapsed time: 6707.5 seconds (1.86 hours) Timestep: 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learning_rate: 0.00009 elapsed time: 6928.5 seconds (1.92 hours) Timestep: 1750000 mean reward (100 episodes): 47.2700 best mean reward: 62.6900 current episode reward: 60.0000 episodes: 3866 exploration: 0.08313 learning_rate: 0.00009 elapsed time: 6972.9 seconds (1.94 hours) Timestep: 1760000 mean reward (100 episodes): 47.7000 best mean reward: 62.6900 current episode reward: 51.0000 episodes: 3874 exploration: 0.08290 learning_rate: 0.00009 elapsed time: 7017.1 seconds (1.95 hours) Timestep: 1770000 mean reward (100 episodes): 50.1800 best mean reward: 62.6900 current episode reward: 74.0000 episodes: 3881 exploration: 0.08267 learning_rate: 0.00009 elapsed time: 7061.0 seconds (1.96 hours) Timestep: 1780000 mean reward (100 episodes): 50.3500 best mean reward: 62.6900 current episode reward: 83.0000 episodes: 3890 exploration: 0.08245 learning_rate: 0.00009 elapsed time: 7105.7 seconds (1.97 hours) Timestep: 1790000 mean reward (100 episodes): 51.6500 best mean reward: 62.6900 current episode reward: 55.0000 episodes: 3897 exploration: 0.08223 learning_rate: 0.00009 elapsed time: 7149.7 seconds (1.99 hours) Timestep: 1800000 mean reward (100 episodes): 52.7000 best mean reward: 62.6900 current episode reward: 62.0000 episodes: 3907 exploration: 0.08200 learning_rate: 0.00009 elapsed time: 7193.4 seconds (2.00 hours) Timestep: 1810000 mean reward (100 episodes): 54.0400 best mean reward: 62.6900 current episode reward: 77.0000 episodes: 3915 exploration: 0.08178 learning_rate: 0.00009 elapsed time: 7238.0 seconds (2.01 hours) Timestep: 1820000 mean reward (100 episodes): 53.2300 best mean reward: 62.6900 current episode reward: 67.0000 episodes: 3927 exploration: 0.08155 learning_rate: 0.00009 elapsed time: 7281.9 seconds (2.02 hours) Timestep: 1830000 mean reward (100 episodes): 57.4200 best mean reward: 62.6900 current episode reward: 25.0000 episodes: 3935 exploration: 0.08133 learning_rate: 0.00009 elapsed time: 7325.7 seconds (2.03 hours) Timestep: 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learning_rate: 0.00009 elapsed time: 7545.2 seconds (2.10 hours) Timestep: 1890000 mean reward (100 episodes): 62.9500 best mean reward: 65.3400 current episode reward: 42.0000 episodes: 3986 exploration: 0.07998 learning_rate: 0.00009 elapsed time: 7588.9 seconds (2.11 hours) Timestep: 1900000 mean reward (100 episodes): 65.0900 best mean reward: 65.4400 current episode reward: 27.0000 episodes: 3995 exploration: 0.07975 learning_rate: 0.00009 elapsed time: 7632.8 seconds (2.12 hours) Timestep: 1910000 mean reward (100 episodes): 65.4600 best mean reward: 66.2700 current episode reward: 52.0000 episodes: 4003 exploration: 0.07952 learning_rate: 0.00009 elapsed time: 7678.4 seconds (2.13 hours) Timestep: 1920000 mean reward (100 episodes): 66.4500 best mean reward: 66.4500 current episode reward: 77.0000 episodes: 4010 exploration: 0.07930 learning_rate: 0.00009 elapsed time: 7723.0 seconds (2.15 hours) Timestep: 1930000 mean reward (100 episodes): 68.1600 best mean reward: 68.5300 current episode reward: 23.0000 episodes: 4018 exploration: 0.07908 learning_rate: 0.00009 elapsed time: 7766.9 seconds (2.16 hours) Timestep: 1940000 mean reward (100 episodes): 67.4400 best mean reward: 69.2700 current episode reward: 64.0000 episodes: 4027 exploration: 0.07885 learning_rate: 0.00009 elapsed time: 7810.7 seconds (2.17 hours) Timestep: 1950000 mean reward (100 episodes): 67.7700 best mean reward: 69.2700 current episode reward: 100.0000 episodes: 4035 exploration: 0.07863 learning_rate: 0.00009 elapsed time: 7853.9 seconds (2.18 hours) Timestep: 1960000 mean reward (100 episodes): 66.9000 best mean reward: 69.5200 current episode reward: 31.0000 episodes: 4046 exploration: 0.07840 learning_rate: 0.00009 elapsed time: 7898.7 seconds (2.19 hours) Timestep: 1970000 mean reward (100 episodes): 66.5700 best mean reward: 69.5200 current episode reward: 42.0000 episodes: 4053 exploration: 0.07818 learning_rate: 0.00009 elapsed time: 7942.6 seconds (2.21 hours) Timestep: 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current episode reward: 62.0000 episodes: 4142 exploration: 0.07593 learning_rate: 0.00009 elapsed time: 8385.5 seconds (2.33 hours) Timestep: 2080000 mean reward (100 episodes): 72.3400 best mean reward: 72.8500 current episode reward: 99.0000 episodes: 4149 exploration: 0.07570 learning_rate: 0.00009 elapsed time: 8429.5 seconds (2.34 hours) Timestep: 2090000 mean reward (100 episodes): 74.6800 best mean reward: 74.6900 current episode reward: 59.0000 episodes: 4156 exploration: 0.07548 learning_rate: 0.00009 elapsed time: 8473.5 seconds (2.35 hours) Timestep: 2100000 mean reward (100 episodes): 76.6200 best mean reward: 76.6200 current episode reward: 88.0000 episodes: 4163 exploration: 0.07525 learning_rate: 0.00009 elapsed time: 8517.6 seconds (2.37 hours) Timestep: 2110000 mean reward (100 episodes): 83.8600 best mean reward: 83.8600 current episode reward: 74.0000 episodes: 4170 exploration: 0.07503 learning_rate: 0.00009 elapsed time: 8560.8 seconds (2.38 hours) Timestep: 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current episode reward: 61.0000 episodes: 4252 exploration: 0.07278 learning_rate: 0.00008 elapsed time: 9000.4 seconds (2.50 hours) Timestep: 2220000 mean reward (100 episodes): 76.0000 best mean reward: 90.1700 current episode reward: 50.0000 episodes: 4260 exploration: 0.07255 learning_rate: 0.00008 elapsed time: 9044.0 seconds (2.51 hours) Timestep: 2230000 mean reward (100 episodes): 78.6900 best mean reward: 90.1700 current episode reward: 102.0000 episodes: 4267 exploration: 0.07233 learning_rate: 0.00008 elapsed time: 9088.5 seconds (2.52 hours) Timestep: 2240000 mean reward (100 episodes): 78.7500 best mean reward: 90.1700 current episode reward: 109.0000 episodes: 4274 exploration: 0.07210 learning_rate: 0.00008 elapsed time: 9132.7 seconds (2.54 hours) Timestep: 2250000 mean reward (100 episodes): 80.1000 best mean reward: 90.1700 current episode reward: 122.0000 episodes: 4281 exploration: 0.07187 learning_rate: 0.00008 elapsed time: 9176.7 seconds (2.55 hours) Timestep: 2260000 mean reward (100 episodes): 82.6400 best mean reward: 90.1700 current episode reward: 69.0000 episodes: 4288 exploration: 0.07165 learning_rate: 0.00008 elapsed time: 9219.8 seconds (2.56 hours) Timestep: 2270000 mean reward (100 episodes): 82.2100 best mean reward: 90.1700 current episode reward: 134.0000 episodes: 4296 exploration: 0.07143 learning_rate: 0.00008 elapsed time: 9263.6 seconds (2.57 hours) Timestep: 2280000 mean reward (100 episodes): 83.6400 best mean reward: 90.1700 current episode reward: 45.0000 episodes: 4303 exploration: 0.07120 learning_rate: 0.00008 elapsed time: 9307.8 seconds (2.59 hours) Timestep: 2290000 mean reward (100 episodes): 87.3600 best mean reward: 90.1700 current episode reward: 239.0000 episodes: 4310 exploration: 0.07097 learning_rate: 0.00008 elapsed time: 9350.9 seconds (2.60 hours) Timestep: 2300000 mean reward (100 episodes): 88.5000 best mean reward: 90.1700 current episode reward: 73.0000 episodes: 4318 exploration: 0.07075 learning_rate: 0.00008 elapsed time: 9395.0 seconds (2.61 hours) Timestep: 2310000 mean reward (100 episodes): 87.2500 best mean reward: 90.1700 current episode reward: 155.0000 episodes: 4325 exploration: 0.07053 learning_rate: 0.00008 elapsed time: 9438.3 seconds (2.62 hours) Timestep: 2320000 mean reward (100 episodes): 86.5000 best mean reward: 90.1700 current episode reward: 76.0000 episodes: 4333 exploration: 0.07030 learning_rate: 0.00008 elapsed time: 9483.0 seconds (2.63 hours) Timestep: 2330000 mean reward (100 episodes): 91.4700 best mean reward: 91.4700 current episode reward: 174.0000 episodes: 4340 exploration: 0.07007 learning_rate: 0.00008 elapsed time: 9526.6 seconds (2.65 hours) Timestep: 2340000 mean reward (100 episodes): 94.8900 best mean reward: 94.8900 current episode reward: 77.0000 episodes: 4348 exploration: 0.06985 learning_rate: 0.00008 elapsed time: 9570.5 seconds (2.66 hours) Timestep: 2350000 mean reward (100 episodes): 96.1800 best mean reward: 97.4300 current episode reward: 131.0000 episodes: 4356 exploration: 0.06962 learning_rate: 0.00008 elapsed time: 9614.1 seconds (2.67 hours) Timestep: 2360000 mean reward (100 episodes): 98.1300 best mean reward: 99.7200 current episode reward: 11.0000 episodes: 4363 exploration: 0.06940 learning_rate: 0.00008 elapsed time: 9657.8 seconds (2.68 hours) Timestep: 2370000 mean reward (100 episodes): 97.8200 best mean reward: 99.7200 current episode reward: 132.0000 episodes: 4370 exploration: 0.06918 learning_rate: 0.00008 elapsed time: 9702.4 seconds (2.70 hours) Timestep: 2380000 mean reward (100 episodes): 99.8600 best mean reward: 99.8600 current episode reward: 106.0000 episodes: 4376 exploration: 0.06895 learning_rate: 0.00008 elapsed time: 9746.5 seconds (2.71 hours) Timestep: 2390000 mean reward (100 episodes): 101.2000 best mean reward: 102.7600 current episode reward: 131.0000 episodes: 4382 exploration: 0.06873 learning_rate: 0.00008 elapsed time: 9790.3 seconds (2.72 hours) Timestep: 2400000 mean reward (100 episodes): 103.5900 best mean reward: 104.3900 current episode reward: 97.0000 episodes: 4389 exploration: 0.06850 learning_rate: 0.00008 elapsed time: 9834.3 seconds (2.73 hours) Timestep: 2410000 mean reward (100 episodes): 104.8700 best mean reward: 105.9100 current episode reward: 23.0000 episodes: 4397 exploration: 0.06828 learning_rate: 0.00008 elapsed time: 9877.9 seconds (2.74 hours) Timestep: 2420000 mean reward (100 episodes): 104.0900 best mean reward: 105.9100 current episode reward: 40.0000 episodes: 4404 exploration: 0.06805 learning_rate: 0.00008 elapsed time: 9921.3 seconds (2.76 hours) Timestep: 2430000 mean reward (100 episodes): 101.6400 best mean reward: 105.9100 current episode reward: 58.0000 episodes: 4412 exploration: 0.06782 learning_rate: 0.00008 elapsed time: 9965.4 seconds (2.77 hours) Timestep: 2440000 mean reward (100 episodes): 102.3500 best mean reward: 105.9100 current episode reward: 31.0000 episodes: 4419 exploration: 0.06760 learning_rate: 0.00008 elapsed time: 10009.1 seconds (2.78 hours) Timestep: 2450000 mean reward (100 episodes): 103.7500 best mean reward: 105.9100 current episode reward: 121.0000 episodes: 4426 exploration: 0.06738 learning_rate: 0.00008 elapsed time: 10053.1 seconds (2.79 hours) Timestep: 2460000 mean reward (100 episodes): 104.3100 best mean reward: 105.9100 current episode reward: 186.0000 episodes: 4433 exploration: 0.06715 learning_rate: 0.00008 elapsed time: 10096.8 seconds (2.80 hours) Timestep: 2470000 mean reward (100 episodes): 104.2800 best mean reward: 105.9100 current episode reward: 75.0000 episodes: 4441 exploration: 0.06693 learning_rate: 0.00008 elapsed time: 10141.5 seconds (2.82 hours) Timestep: 2480000 mean reward (100 episodes): 109.9400 best mean reward: 109.9400 current episode reward: 108.0000 episodes: 4448 exploration: 0.06670 learning_rate: 0.00008 elapsed time: 10185.3 seconds (2.83 hours) Timestep: 2490000 mean reward (100 episodes): 110.7200 best mean reward: 110.7200 current episode reward: 146.0000 episodes: 4454 exploration: 0.06648 learning_rate: 0.00008 elapsed time: 10229.3 seconds (2.84 hours) Timestep: 2500000 mean reward (100 episodes): 110.0400 best mean reward: 111.1300 current episode reward: 115.0000 episodes: 4462 exploration: 0.06625 learning_rate: 0.00008 elapsed time: 10273.0 seconds (2.85 hours) Timestep: 2510000 mean reward (100 episodes): 110.3300 best mean reward: 111.5600 current episode reward: 105.0000 episodes: 4469 exploration: 0.06603 learning_rate: 0.00008 elapsed time: 10316.5 seconds (2.87 hours) Timestep: 2520000 mean reward (100 episodes): 109.4700 best mean reward: 111.5600 current episode reward: 139.0000 episodes: 4475 exploration: 0.06580 learning_rate: 0.00008 elapsed time: 10360.9 seconds (2.88 hours) Timestep: 2530000 mean reward (100 episodes): 108.9800 best mean reward: 111.5800 current episode reward: 115.0000 episodes: 4482 exploration: 0.06557 learning_rate: 0.00008 elapsed time: 10404.9 seconds (2.89 hours) Timestep: 2540000 mean reward (100 episodes): 111.2700 best mean reward: 111.5800 current episode reward: 136.0000 episodes: 4488 exploration: 0.06535 learning_rate: 0.00008 elapsed time: 10448.5 seconds (2.90 hours) Timestep: 2550000 mean reward (100 episodes): 112.0800 best mean reward: 113.7300 current episode reward: 101.0000 episodes: 4496 exploration: 0.06513 learning_rate: 0.00008 elapsed time: 10492.0 seconds (2.91 hours) Timestep: 2560000 mean reward (100 episodes): 113.1200 best mean reward: 114.1700 current episode reward: 78.0000 episodes: 4504 exploration: 0.06490 learning_rate: 0.00008 elapsed time: 10535.8 seconds (2.93 hours) Timestep: 2570000 mean reward (100 episodes): 115.5700 best mean reward: 115.5700 current episode reward: 227.0000 episodes: 4511 exploration: 0.06468 learning_rate: 0.00008 elapsed time: 10579.3 seconds (2.94 hours) Timestep: 2580000 mean reward (100 episodes): 117.3200 best mean reward: 118.4200 current episode reward: 89.0000 episodes: 4517 exploration: 0.06445 learning_rate: 0.00008 elapsed time: 10622.9 seconds (2.95 hours) Timestep: 2590000 mean reward (100 episodes): 119.5300 best mean reward: 120.0500 current episode reward: 95.0000 episodes: 4524 exploration: 0.06423 learning_rate: 0.00008 elapsed time: 10667.9 seconds (2.96 hours) Timestep: 2600000 mean reward (100 episodes): 121.2800 best mean reward: 121.2800 current episode reward: 165.0000 episodes: 4532 exploration: 0.06400 learning_rate: 0.00008 elapsed time: 10711.9 seconds (2.98 hours) Timestep: 2610000 mean reward (100 episodes): 117.6700 best mean reward: 121.5100 current episode reward: 73.0000 episodes: 4540 exploration: 0.06377 learning_rate: 0.00008 elapsed time: 10755.5 seconds (2.99 hours) Timestep: 2620000 mean reward (100 episodes): 116.8700 best mean reward: 121.5100 current episode reward: 170.0000 episodes: 4546 exploration: 0.06355 learning_rate: 0.00008 elapsed time: 10799.6 seconds (3.00 hours) Timestep: 2630000 mean reward (100 episodes): 118.4800 best mean reward: 121.5100 current episode reward: 91.0000 episodes: 4554 exploration: 0.06333 learning_rate: 0.00008 elapsed time: 10843.0 seconds (3.01 hours) Timestep: 2640000 mean reward (100 episodes): 123.2500 best mean reward: 123.8800 current episode reward: 170.0000 episodes: 4560 exploration: 0.06310 learning_rate: 0.00008 elapsed time: 10887.1 seconds (3.02 hours) Timestep: 2650000 mean reward (100 episodes): 124.2700 best mean reward: 125.5400 current episode reward: 106.0000 episodes: 4568 exploration: 0.06288 learning_rate: 0.00008 elapsed time: 10931.3 seconds (3.04 hours) Timestep: 2660000 mean reward (100 episodes): 123.0700 best mean reward: 125.5400 current episode reward: 49.0000 episodes: 4575 exploration: 0.06265 learning_rate: 0.00008 elapsed time: 10975.1 seconds (3.05 hours) Timestep: 2670000 mean reward (100 episodes): 123.4100 best mean reward: 125.5400 current episode reward: 180.0000 episodes: 4582 exploration: 0.06243 learning_rate: 0.00008 elapsed time: 11019.6 seconds (3.06 hours) Timestep: 2680000 mean reward (100 episodes): 118.2200 best mean reward: 125.5400 current episode reward: 53.0000 episodes: 4590 exploration: 0.06220 learning_rate: 0.00008 elapsed time: 11063.0 seconds (3.07 hours) Timestep: 2690000 mean reward (100 episodes): 116.7000 best mean reward: 125.5400 current episode reward: 151.0000 episodes: 4597 exploration: 0.06198 learning_rate: 0.00008 elapsed time: 11107.0 seconds (3.09 hours) Timestep: 2700000 mean reward (100 episodes): 120.3800 best mean reward: 125.5400 current episode reward: 135.0000 episodes: 4604 exploration: 0.06175 learning_rate: 0.00008 elapsed time: 11150.9 seconds (3.10 hours) Timestep: 2710000 mean reward (100 episodes): 118.2000 best mean reward: 125.5400 current episode reward: 54.0000 episodes: 4611 exploration: 0.06153 learning_rate: 0.00008 elapsed time: 11194.4 seconds (3.11 hours) Timestep: 2720000 mean reward (100 episodes): 116.2900 best mean reward: 125.5400 current episode reward: 57.0000 episodes: 4619 exploration: 0.06130 learning_rate: 0.00008 elapsed time: 11237.7 seconds (3.12 hours) Timestep: 2730000 mean reward (100 episodes): 117.0300 best mean reward: 125.5400 current episode reward: 276.0000 episodes: 4626 exploration: 0.06108 learning_rate: 0.00008 elapsed time: 11282.2 seconds (3.13 hours) Timestep: 2740000 mean reward (100 episodes): 118.7200 best mean reward: 125.5400 current episode reward: 126.0000 episodes: 4632 exploration: 0.06085 learning_rate: 0.00008 elapsed time: 11326.6 seconds (3.15 hours) Timestep: 2750000 mean reward (100 episodes): 125.0700 best mean reward: 125.5400 current episode reward: 270.0000 episodes: 4639 exploration: 0.06062 learning_rate: 0.00008 elapsed time: 11370.6 seconds (3.16 hours) Timestep: 2760000 mean reward (100 episodes): 128.3400 best mean reward: 128.3400 current episode reward: 185.0000 episodes: 4645 exploration: 0.06040 learning_rate: 0.00008 elapsed time: 11414.6 seconds (3.17 hours) Timestep: 2770000 mean reward (100 episodes): 126.5100 best mean reward: 128.3400 current episode reward: 230.0000 episodes: 4652 exploration: 0.06017 learning_rate: 0.00008 elapsed time: 11459.1 seconds (3.18 hours) Timestep: 2780000 mean reward (100 episodes): 123.3200 best mean reward: 128.3400 current episode reward: 154.0000 episodes: 4659 exploration: 0.05995 learning_rate: 0.00008 elapsed time: 11502.4 seconds (3.20 hours) Timestep: 2790000 mean reward (100 episodes): 120.5900 best mean reward: 128.3400 current episode reward: 87.0000 episodes: 4666 exploration: 0.05973 learning_rate: 0.00008 elapsed time: 11547.4 seconds (3.21 hours) Timestep: 2800000 mean reward (100 episodes): 119.2300 best mean reward: 128.3400 current episode reward: 82.0000 episodes: 4675 exploration: 0.05950 learning_rate: 0.00008 elapsed time: 11592.5 seconds (3.22 hours) Timestep: 2810000 mean reward (100 episodes): 116.6300 best mean reward: 128.3400 current episode reward: 106.0000 episodes: 4683 exploration: 0.05928 learning_rate: 0.00008 elapsed time: 11636.1 seconds (3.23 hours) Timestep: 2820000 mean reward (100 episodes): 116.3000 best mean reward: 128.3400 current episode reward: 276.0000 episodes: 4691 exploration: 0.05905 learning_rate: 0.00008 elapsed time: 11680.2 seconds (3.24 hours) Timestep: 2830000 mean reward (100 episodes): 113.0800 best mean reward: 128.3400 current episode reward: 130.0000 episodes: 4699 exploration: 0.05883 learning_rate: 0.00008 elapsed time: 11723.9 seconds (3.26 hours) Timestep: 2840000 mean reward (100 episodes): 107.1600 best mean reward: 128.3400 current episode reward: 39.0000 episodes: 4708 exploration: 0.05860 learning_rate: 0.00008 elapsed time: 11767.6 seconds (3.27 hours) Timestep: 2850000 mean reward (100 episodes): 100.8600 best mean reward: 128.3400 current episode reward: 32.0000 episodes: 4719 exploration: 0.05837 learning_rate: 0.00008 elapsed time: 11811.9 seconds (3.28 hours) Timestep: 2860000 mean reward (100 episodes): 88.0700 best mean reward: 128.3400 current episode reward: 21.0000 episodes: 4731 exploration: 0.05815 learning_rate: 0.00008 elapsed time: 11856.5 seconds (3.29 hours) Timestep: 2870000 mean reward (100 episodes): 66.7300 best mean reward: 128.3400 current episode reward: 15.0000 episodes: 4746 exploration: 0.05792 learning_rate: 0.00008 elapsed time: 11901.0 seconds (3.31 hours) Timestep: 2880000 mean reward (100 episodes): 45.2200 best mean reward: 128.3400 current episode reward: 4.0000 episodes: 4767 exploration: 0.05770 learning_rate: 0.00008 elapsed time: 11944.9 seconds (3.32 hours) Timestep: 2890000 mean reward (100 episodes): 23.2100 best mean reward: 128.3400 current episode reward: 13.0000 episodes: 4796 exploration: 0.05748 learning_rate: 0.00008 elapsed time: 11988.9 seconds (3.33 hours) Timestep: 2900000 mean reward (100 episodes): 8.2300 best mean reward: 128.3400 current episode reward: 3.0000 episodes: 4834 exploration: 0.05725 learning_rate: 0.00008 elapsed time: 12032.5 seconds (3.34 hours) Timestep: 2910000 mean reward (100 episodes): 3.7400 best mean reward: 128.3400 current episode reward: 1.0000 episodes: 4880 exploration: 0.05702 learning_rate: 0.00008 elapsed time: 12076.6 seconds (3.35 hours) Timestep: 2920000 mean reward (100 episodes): 2.2300 best mean reward: 128.3400 current episode reward: 6.0000 episodes: 4934 exploration: 0.05680 learning_rate: 0.00008 elapsed time: 12121.4 seconds (3.37 hours) Timestep: 2930000 mean reward (100 episodes): 2.0400 best mean reward: 128.3400 current episode reward: 4.0000 episodes: 4983 exploration: 0.05658 learning_rate: 0.00008 elapsed time: 12166.0 seconds (3.38 hours) Timestep: 2940000 mean reward (100 episodes): 2.0300 best mean reward: 128.3400 current episode reward: 1.0000 episodes: 5036 exploration: 0.05635 learning_rate: 0.00008 elapsed time: 12211.0 seconds (3.39 hours) Timestep: 2950000 mean reward (100 episodes): 2.4700 best mean reward: 128.3400 current episode reward: 3.0000 episodes: 5079 exploration: 0.05613 learning_rate: 0.00008 elapsed time: 12255.4 seconds (3.40 hours) Timestep: 2960000 mean reward (100 episodes): 2.9300 best mean reward: 128.3400 current episode reward: 4.0000 episodes: 5122 exploration: 0.05590 learning_rate: 0.00008 elapsed time: 12299.5 seconds (3.42 hours) Timestep: 2970000 mean reward (100 episodes): 3.0100 best mean reward: 128.3400 current episode reward: 6.0000 episodes: 5166 exploration: 0.05568 learning_rate: 0.00008 elapsed time: 12343.6 seconds (3.43 hours) Timestep: 2980000 mean reward (100 episodes): 2.8900 best mean reward: 128.3400 current episode reward: 5.0000 episodes: 5211 exploration: 0.05545 learning_rate: 0.00008 elapsed time: 12388.3 seconds (3.44 hours) Timestep: 2990000 mean reward (100 episodes): 2.4000 best mean reward: 128.3400 current episode reward: 0.0000 episodes: 5264 exploration: 0.05523 learning_rate: 0.00008 elapsed time: 12432.5 seconds (3.45 hours) Timestep: 3000000 mean reward (100 episodes): 2.2300 best mean reward: 128.3400 current episode reward: 3.0000 episodes: 5312 exploration: 0.05500 learning_rate: 0.00008 elapsed time: 12476.8 seconds (3.47 hours) Timestep: 3010000 mean reward (100 episodes): 2.3100 best mean reward: 128.3400 current episode reward: 2.0000 episodes: 5361 exploration: 0.05478 learning_rate: 0.00007 elapsed time: 12520.4 seconds (3.48 hours) Timestep: 3020000 mean reward (100 episodes): 2.4500 best mean reward: 128.3400 current episode reward: 2.0000 episodes: 5407 exploration: 0.05455 learning_rate: 0.00007 elapsed time: 12564.6 seconds (3.49 hours) Timestep: 3030000 mean reward (100 episodes): 2.4200 best mean reward: 128.3400 current episode reward: 2.0000 episodes: 5454 exploration: 0.05433 learning_rate: 0.00007 elapsed time: 12609.0 seconds (3.50 hours) Timestep: 3040000 mean reward (100 episodes): 2.6000 best mean reward: 128.3400 current episode reward: 1.0000 episodes: 5499 exploration: 0.05410 learning_rate: 0.00007 elapsed time: 12653.8 seconds (3.51 hours) Timestep: 3050000 mean reward (100 episodes): 2.3200 best mean reward: 128.3400 current episode reward: 1.0000 episodes: 5550 exploration: 0.05388 learning_rate: 0.00007 elapsed time: 12698.1 seconds (3.53 hours) Timestep: 3060000 mean reward (100 episodes): 2.0000 best mean reward: 128.3400 current episode reward: 3.0000 episodes: 5601 exploration: 0.05365 learning_rate: 0.00007 elapsed time: 12742.3 seconds (3.54 hours) Timestep: 3070000 mean reward (100 episodes): 2.4400 best mean reward: 128.3400 current episode reward: 7.0000 episodes: 5645 exploration: 0.05343 learning_rate: 0.00007 elapsed time: 12786.8 seconds (3.55 hours) Timestep: 3080000 mean reward (100 episodes): 3.4400 best mean reward: 128.3400 current episode reward: 4.0000 episodes: 5678 exploration: 0.05320 learning_rate: 0.00007 elapsed time: 12831.7 seconds (3.56 hours) Timestep: 3090000 mean reward (100 episodes): 3.9200 best mean reward: 128.3400 current episode reward: 5.0000 episodes: 5717 exploration: 0.05298 learning_rate: 0.00007 elapsed time: 12876.3 seconds (3.58 hours) Timestep: 3100000 mean reward (100 episodes): 4.6700 best mean reward: 128.3400 current episode reward: 2.0000 episodes: 5749 exploration: 0.05275 learning_rate: 0.00007 elapsed time: 12920.0 seconds (3.59 hours) Timestep: 3110000 mean reward (100 episodes): 4.7300 best mean reward: 128.3400 current episode reward: 6.0000 episodes: 5782 exploration: 0.05253 learning_rate: 0.00007 elapsed time: 12963.7 seconds (3.60 hours) Timestep: 3120000 mean reward (100 episodes): 4.7300 best mean reward: 128.3400 current episode reward: 2.0000 episodes: 5819 exploration: 0.05230 learning_rate: 0.00007 elapsed time: 13007.8 seconds (3.61 hours) Timestep: 3130000 mean reward (100 episodes): 4.3400 best mean reward: 128.3400 current episode reward: 3.0000 episodes: 5856 exploration: 0.05208 learning_rate: 0.00007 elapsed time: 13052.3 seconds (3.63 hours) Timestep: 3140000 mean reward (100 episodes): 4.2200 best mean reward: 128.3400 current episode reward: 1.0000 episodes: 5895 exploration: 0.05185 learning_rate: 0.00007 elapsed time: 13096.2 seconds (3.64 hours) Timestep: 3150000 mean reward (100 episodes): 4.1300 best mean reward: 128.3400 current episode reward: 1.0000 episodes: 5931 exploration: 0.05163 learning_rate: 0.00007 elapsed time: 13140.4 seconds (3.65 hours) Timestep: 3160000 mean reward (100 episodes): 4.2400 best mean reward: 128.3400 current episode reward: 4.0000 episodes: 5968 exploration: 0.05140 learning_rate: 0.00007 elapsed time: 13184.6 seconds (3.66 hours) Timestep: 3170000 mean reward (100 episodes): 4.6400 best mean reward: 128.3400 current episode reward: 17.0000 episodes: 6002 exploration: 0.05117 learning_rate: 0.00007 elapsed time: 13229.2 seconds (3.67 hours) Timestep: 3180000 mean reward (100 episodes): 4.7800 best mean reward: 128.3400 current episode reward: 3.0000 episodes: 6035 exploration: 0.05095 learning_rate: 0.00007 elapsed time: 13273.2 seconds (3.69 hours) Timestep: 3190000 mean reward (100 episodes): 4.7600 best mean reward: 128.3400 current episode reward: 0.0000 episodes: 6071 exploration: 0.05072 learning_rate: 0.00007 elapsed time: 13317.4 seconds (3.70 hours) Timestep: 3200000 mean reward (100 episodes): 4.3400 best mean reward: 128.3400 current episode reward: 4.0000 episodes: 6108 exploration: 0.05050 learning_rate: 0.00007 elapsed time: 13361.6 seconds (3.71 hours) Timestep: 3210000 mean reward (100 episodes): 4.0000 best mean reward: 128.3400 current episode reward: 7.0000 episodes: 6144 exploration: 0.05028 learning_rate: 0.00007 elapsed time: 13407.1 seconds (3.72 hours) Timestep: 3220000 mean reward (100 episodes): 4.6200 best mean reward: 128.3400 current episode reward: 6.0000 episodes: 6175 exploration: 0.05005 learning_rate: 0.00007 elapsed time: 13451.1 seconds (3.74 hours) Timestep: 3230000 mean reward (100 episodes): 5.1100 best mean reward: 128.3400 current episode reward: 8.0000 episodes: 6206 exploration: 0.04983 learning_rate: 0.00007 elapsed time: 13495.0 seconds (3.75 hours) Timestep: 3240000 mean reward (100 episodes): 5.4900 best mean reward: 128.3400 current episode reward: 5.0000 episodes: 6236 exploration: 0.04960 learning_rate: 0.00007 elapsed time: 13538.7 seconds (3.76 hours) Timestep: 3250000 mean reward (100 episodes): 6.0200 best mean reward: 128.3400 current episode reward: 7.0000 episodes: 6264 exploration: 0.04938 learning_rate: 0.00007 elapsed time: 13582.8 seconds (3.77 hours) Timestep: 3260000 mean reward (100 episodes): 5.6000 best mean reward: 128.3400 current episode reward: 8.0000 episodes: 6298 exploration: 0.04915 learning_rate: 0.00007 elapsed time: 13627.2 seconds (3.79 hours) Timestep: 3270000 mean reward (100 episodes): 5.5800 best mean reward: 128.3400 current episode reward: 1.0000 episodes: 6328 exploration: 0.04892 learning_rate: 0.00007 elapsed time: 13671.8 seconds (3.80 hours) Timestep: 3280000 mean reward (100 episodes): 5.5100 best mean reward: 128.3400 current episode reward: 5.0000 episodes: 6356 exploration: 0.04870 learning_rate: 0.00007 elapsed time: 13716.0 seconds (3.81 hours) Timestep: 3290000 mean reward (100 episodes): 5.8700 best mean reward: 128.3400 current episode reward: 5.0000 episodes: 6387 exploration: 0.04847 learning_rate: 0.00007 elapsed time: 13759.9 seconds (3.82 hours) Timestep: 3300000 mean reward (100 episodes): 5.7400 best mean reward: 128.3400 current episode reward: 5.0000 episodes: 6419 exploration: 0.04825 learning_rate: 0.00007 elapsed time: 13803.9 seconds (3.83 hours) Timestep: 3310000 mean reward (100 episodes): 5.3400 best mean reward: 128.3400 current episode reward: 2.0000 episodes: 6452 exploration: 0.04802 learning_rate: 0.00007 elapsed time: 13847.8 seconds (3.85 hours) Timestep: 3320000 mean reward (100 episodes): 5.6800 best mean reward: 128.3400 current episode reward: 6.0000 episodes: 6478 exploration: 0.04780 learning_rate: 0.00007 elapsed time: 13891.8 seconds (3.86 hours) Timestep: 3330000 mean reward (100 episodes): 6.2600 best mean reward: 128.3400 current episode reward: 2.0000 episodes: 6505 exploration: 0.04757 learning_rate: 0.00007 elapsed time: 13935.7 seconds (3.87 hours) Timestep: 3340000 mean reward (100 episodes): 6.9100 best mean reward: 128.3400 current episode reward: 6.0000 episodes: 6530 exploration: 0.04735 learning_rate: 0.00007 elapsed time: 13979.8 seconds (3.88 hours) Timestep: 3350000 mean reward (100 episodes): 7.4800 best mean reward: 128.3400 current episode reward: 11.0000 episodes: 6556 exploration: 0.04713 learning_rate: 0.00007 elapsed time: 14024.1 seconds (3.90 hours) Timestep: 3360000 mean reward (100 episodes): 7.7900 best mean reward: 128.3400 current episode reward: 12.0000 episodes: 6580 exploration: 0.04690 learning_rate: 0.00007 elapsed time: 14068.3 seconds (3.91 hours) Timestep: 3370000 mean reward (100 episodes): 8.3600 best mean reward: 128.3400 current episode reward: 7.0000 episodes: 6601 exploration: 0.04667 learning_rate: 0.00007 elapsed time: 14110.7 seconds (3.92 hours) Timestep: 3380000 mean reward (100 episodes): 8.6900 best mean reward: 128.3400 current episode reward: 5.0000 episodes: 6623 exploration: 0.04645 learning_rate: 0.00007 elapsed time: 14154.5 seconds (3.93 hours) Timestep: 3390000 mean reward (100 episodes): 9.6500 best mean reward: 128.3400 current episode reward: 5.0000 episodes: 6641 exploration: 0.04622 learning_rate: 0.00007 elapsed time: 14197.8 seconds (3.94 hours) Timestep: 3400000 mean reward (100 episodes): 10.5000 best mean reward: 128.3400 current episode reward: 8.0000 episodes: 6660 exploration: 0.04600 learning_rate: 0.00007 elapsed time: 14241.7 seconds (3.96 hours) Timestep: 3410000 mean reward (100 episodes): 10.3900 best mean reward: 128.3400 current episode reward: 8.0000 episodes: 6684 exploration: 0.04577 learning_rate: 0.00007 elapsed time: 14285.8 seconds (3.97 hours) Timestep: 3420000 mean reward (100 episodes): 10.4900 best mean reward: 128.3400 current episode reward: 13.0000 episodes: 6704 exploration: 0.04555 learning_rate: 0.00007 elapsed time: 14330.2 seconds (3.98 hours) Timestep: 3430000 mean reward (100 episodes): 10.9900 best mean reward: 128.3400 current episode reward: 18.0000 episodes: 6723 exploration: 0.04532 learning_rate: 0.00007 elapsed time: 14374.4 seconds (3.99 hours) Timestep: 3440000 mean reward (100 episodes): 11.2900 best mean reward: 128.3400 current episode reward: 15.0000 episodes: 6741 exploration: 0.04510 learning_rate: 0.00007 elapsed time: 14418.3 seconds (4.01 hours) Timestep: 3450000 mean reward (100 episodes): 10.8800 best mean reward: 128.3400 current episode reward: 9.0000 episodes: 6762 exploration: 0.04487 learning_rate: 0.00007 elapsed time: 14462.5 seconds (4.02 hours) Timestep: 3460000 mean reward (100 episodes): 11.6100 best mean reward: 128.3400 current episode reward: 9.0000 episodes: 6780 exploration: 0.04465 learning_rate: 0.00007 elapsed time: 14506.7 seconds (4.03 hours) Timestep: 3470000 mean reward (100 episodes): 12.4500 best mean reward: 128.3400 current episode reward: 13.0000 episodes: 6797 exploration: 0.04442 learning_rate: 0.00007 elapsed time: 14551.6 seconds (4.04 hours) Timestep: 3480000 mean reward (100 episodes): 13.5100 best mean reward: 128.3400 current episode reward: 11.0000 episodes: 6810 exploration: 0.04420 learning_rate: 0.00007 elapsed time: 14596.9 seconds (4.05 hours) Timestep: 3490000 mean reward (100 episodes): 13.0400 best mean reward: 128.3400 current episode reward: 5.0000 episodes: 6832 exploration: 0.04397 learning_rate: 0.00007 elapsed time: 14640.7 seconds (4.07 hours) Timestep: 3500000 mean reward (100 episodes): 13.3300 best mean reward: 128.3400 current episode reward: 19.0000 episodes: 6851 exploration: 0.04375 learning_rate: 0.00007 elapsed time: 14685.2 seconds (4.08 hours) Timestep: 3510000 mean reward (100 episodes): 14.0400 best mean reward: 128.3400 current episode reward: 17.0000 episodes: 6867 exploration: 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reward: 128.3400 current episode reward: 8.0000 episodes: 6948 exploration: 0.04240 learning_rate: 0.00007 elapsed time: 14949.9 seconds (4.15 hours) Timestep: 3570000 mean reward (100 episodes): 15.7100 best mean reward: 128.3400 current episode reward: 18.0000 episodes: 6963 exploration: 0.04218 learning_rate: 0.00007 elapsed time: 14994.2 seconds (4.17 hours) Timestep: 3580000 mean reward (100 episodes): 15.3800 best mean reward: 128.3400 current episode reward: 26.0000 episodes: 6980 exploration: 0.04195 learning_rate: 0.00007 elapsed time: 15038.0 seconds (4.18 hours) Timestep: 3590000 mean reward (100 episodes): 16.3800 best mean reward: 128.3400 current episode reward: 28.0000 episodes: 6994 exploration: 0.04173 learning_rate: 0.00007 elapsed time: 15082.0 seconds (4.19 hours) Timestep: 3600000 mean reward (100 episodes): 17.2300 best mean reward: 128.3400 current episode reward: 22.0000 episodes: 7007 exploration: 0.04150 learning_rate: 0.00007 elapsed time: 15128.1 seconds (4.20 hours) Timestep: 3610000 mean reward (100 episodes): 17.4000 best mean reward: 128.3400 current episode reward: 17.0000 episodes: 7022 exploration: 0.04127 learning_rate: 0.00007 elapsed time: 15172.2 seconds (4.21 hours) Timestep: 3620000 mean reward (100 episodes): 17.9800 best mean reward: 128.3400 current episode reward: 18.0000 episodes: 7035 exploration: 0.04105 learning_rate: 0.00007 elapsed time: 15216.2 seconds (4.23 hours) Timestep: 3630000 mean reward (100 episodes): 18.9600 best mean reward: 128.3400 current episode reward: 12.0000 episodes: 7051 exploration: 0.04083 learning_rate: 0.00007 elapsed time: 15260.2 seconds (4.24 hours) Timestep: 3640000 mean reward (100 episodes): 19.7300 best mean reward: 128.3400 current episode reward: 30.0000 episodes: 7063 exploration: 0.04060 learning_rate: 0.00007 elapsed time: 15304.6 seconds (4.25 hours) Timestep: 3650000 mean reward (100 episodes): 20.6900 best mean reward: 128.3400 current episode reward: 34.0000 episodes: 7078 exploration: 0.04038 learning_rate: 0.00007 elapsed time: 15348.3 seconds (4.26 hours) Timestep: 3660000 mean reward (100 episodes): 20.8900 best mean reward: 128.3400 current episode reward: 20.0000 episodes: 7090 exploration: 0.04015 learning_rate: 0.00007 elapsed time: 15392.4 seconds (4.28 hours) Timestep: 3670000 mean reward (100 episodes): 20.4300 best mean reward: 128.3400 current episode reward: 19.0000 episodes: 7106 exploration: 0.03993 learning_rate: 0.00007 elapsed time: 15436.0 seconds (4.29 hours) Timestep: 3680000 mean reward (100 episodes): 20.7100 best mean reward: 128.3400 current episode reward: 17.0000 episodes: 7119 exploration: 0.03970 learning_rate: 0.00007 elapsed time: 15479.9 seconds (4.30 hours) Timestep: 3690000 mean reward (100 episodes): 21.2200 best mean reward: 128.3400 current episode reward: 30.0000 episodes: 7131 exploration: 0.03947 learning_rate: 0.00007 elapsed time: 15524.4 seconds (4.31 hours) Timestep: 3700000 mean reward (100 episodes): 21.2300 best mean reward: 128.3400 current episode reward: 27.0000 episodes: 7144 exploration: 0.03925 learning_rate: 0.00007 elapsed time: 15568.2 seconds (4.32 hours) Timestep: 3710000 mean reward (100 episodes): 21.2400 best mean reward: 128.3400 current episode reward: 10.0000 episodes: 7159 exploration: 0.03902 learning_rate: 0.00007 elapsed time: 15612.4 seconds (4.34 hours) Timestep: 3720000 mean reward (100 episodes): 21.5800 best mean reward: 128.3400 current episode reward: 19.0000 episodes: 7172 exploration: 0.03880 learning_rate: 0.00007 elapsed time: 15656.7 seconds (4.35 hours) Timestep: 3730000 mean reward (100 episodes): 21.6700 best mean reward: 128.3400 current episode reward: 17.0000 episodes: 7184 exploration: 0.03857 learning_rate: 0.00007 elapsed time: 15701.0 seconds (4.36 hours) Timestep: 3740000 mean reward (100 episodes): 21.9500 best mean reward: 128.3400 current episode reward: 40.0000 episodes: 7196 exploration: 0.03835 learning_rate: 0.00007 elapsed time: 15745.5 seconds (4.37 hours) Timestep: 3750000 mean reward (100 episodes): 23.4100 best mean reward: 128.3400 current episode reward: 36.0000 episodes: 7207 exploration: 0.03812 learning_rate: 0.00007 elapsed time: 15789.7 seconds (4.39 hours) Timestep: 3760000 mean reward (100 episodes): 24.5100 best mean reward: 128.3400 current episode reward: 23.0000 episodes: 7218 exploration: 0.03790 learning_rate: 0.00007 elapsed time: 15834.2 seconds (4.40 hours) Timestep: 3770000 mean reward (100 episodes): 24.7500 best mean reward: 128.3400 current episode reward: 31.0000 episodes: 7230 exploration: 0.03768 learning_rate: 0.00007 elapsed time: 15877.8 seconds (4.41 hours) Timestep: 3780000 mean reward (100 episodes): 25.5800 best mean reward: 128.3400 current episode reward: 22.0000 episodes: 7241 exploration: 0.03745 learning_rate: 0.00007 elapsed time: 15922.3 seconds (4.42 hours) Timestep: 3790000 mean reward (100 episodes): 26.0200 best mean reward: 128.3400 current episode reward: 14.0000 episodes: 7253 exploration: 0.03722 learning_rate: 0.00007 elapsed time: 15967.1 seconds (4.44 hours) Timestep: 3800000 mean reward (100 episodes): 26.1900 best mean reward: 128.3400 current episode reward: 17.0000 episodes: 7266 exploration: 0.03700 learning_rate: 0.00007 elapsed time: 16011.4 seconds (4.45 hours) Timestep: 3810000 mean reward (100 episodes): 26.4500 best mean reward: 128.3400 current episode reward: 19.0000 episodes: 7278 exploration: 0.03678 learning_rate: 0.00006 elapsed time: 16054.8 seconds (4.46 hours) Timestep: 3820000 mean reward (100 episodes): 27.3500 best mean reward: 128.3400 current episode reward: 34.0000 episodes: 7289 exploration: 0.03655 learning_rate: 0.00006 elapsed time: 16099.5 seconds (4.47 hours) Timestep: 3830000 mean reward (100 episodes): 27.4100 best mean reward: 128.3400 current episode reward: 50.0000 episodes: 7301 exploration: 0.03632 learning_rate: 0.00006 elapsed time: 16143.7 seconds (4.48 hours) Timestep: 3840000 mean reward (100 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elapsed time: 16365.2 seconds (4.55 hours) Timestep: 3890000 mean reward (100 episodes): 33.6500 best mean reward: 128.3400 current episode reward: 73.0000 episodes: 7363 exploration: 0.03497 learning_rate: 0.00006 elapsed time: 16409.5 seconds (4.56 hours) Timestep: 3900000 mean reward (100 episodes): 35.2900 best mean reward: 128.3400 current episode reward: 45.0000 episodes: 7372 exploration: 0.03475 learning_rate: 0.00006 elapsed time: 16453.4 seconds (4.57 hours) Timestep: 3910000 mean reward (100 episodes): 36.0800 best mean reward: 128.3400 current episode reward: 42.0000 episodes: 7383 exploration: 0.03453 learning_rate: 0.00006 elapsed time: 16497.7 seconds (4.58 hours) Timestep: 3920000 mean reward (100 episodes): 37.0600 best mean reward: 128.3400 current episode reward: 56.0000 episodes: 7392 exploration: 0.03430 learning_rate: 0.00006 elapsed time: 16541.6 seconds (4.59 hours) Timestep: 3930000 mean reward (100 episodes): 38.4700 best mean reward: 128.3400 current episode reward: 65.0000 episodes: 7401 exploration: 0.03407 learning_rate: 0.00006 elapsed time: 16586.5 seconds (4.61 hours) Timestep: 3940000 mean reward (100 episodes): 39.3900 best mean reward: 128.3400 current episode reward: 21.0000 episodes: 7410 exploration: 0.03385 learning_rate: 0.00006 elapsed time: 16630.4 seconds (4.62 hours) Timestep: 3950000 mean reward (100 episodes): 40.0100 best mean reward: 128.3400 current episode reward: 57.0000 episodes: 7418 exploration: 0.03362 learning_rate: 0.00006 elapsed time: 16674.3 seconds (4.63 hours) Timestep: 3960000 mean reward (100 episodes): 38.3500 best mean reward: 128.3400 current episode reward: 22.0000 episodes: 7429 exploration: 0.03340 learning_rate: 0.00006 elapsed time: 16719.2 seconds (4.64 hours) Timestep: 3970000 mean reward (100 episodes): 38.1800 best mean reward: 128.3400 current episode reward: 31.0000 episodes: 7438 exploration: 0.03317 learning_rate: 0.00006 elapsed time: 16762.9 seconds (4.66 hours) Timestep: 3980000 mean reward (100 episodes): 39.4400 best mean reward: 128.3400 current episode reward: 60.0000 episodes: 7448 exploration: 0.03295 learning_rate: 0.00006 elapsed time: 16806.8 seconds (4.67 hours) Timestep: 3990000 mean reward (100 episodes): 40.6100 best mean reward: 128.3400 current episode reward: 42.0000 episodes: 7457 exploration: 0.03272 learning_rate: 0.00006 elapsed time: 16850.6 seconds (4.68 hours) Timestep: 4000000 mean reward (100 episodes): 40.5600 best mean reward: 128.3400 current episode reward: 40.0000 episodes: 7467 exploration: 0.03250 learning_rate: 0.00006 elapsed time: 16894.9 seconds (4.69 hours) Timestep: 4010000 mean reward (100 episodes): 41.7000 best mean reward: 128.3400 current episode reward: 54.0000 episodes: 7475 exploration: 0.03227 learning_rate: 0.00006 elapsed time: 16938.4 seconds (4.71 hours) Timestep: 4020000 mean reward (100 episodes): 41.9300 best mean reward: 128.3400 current episode reward: 44.0000 episodes: 7485 exploration: 0.03205 learning_rate: 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episode reward: 66.0000 episodes: 7525 exploration: 0.03092 learning_rate: 0.00006 elapsed time: 17201.7 seconds (4.78 hours) Timestep: 4080000 mean reward (100 episodes): 47.2300 best mean reward: 128.3400 current episode reward: 52.0000 episodes: 7534 exploration: 0.03070 learning_rate: 0.00006 elapsed time: 17245.5 seconds (4.79 hours) Timestep: 4090000 mean reward (100 episodes): 47.5300 best mean reward: 128.3400 current episode reward: 22.0000 episodes: 7543 exploration: 0.03048 learning_rate: 0.00006 elapsed time: 17289.6 seconds (4.80 hours) Timestep: 4100000 mean reward (100 episodes): 47.7700 best mean reward: 128.3400 current episode reward: 39.0000 episodes: 7552 exploration: 0.03025 learning_rate: 0.00006 elapsed time: 17334.1 seconds (4.82 hours) Timestep: 4110000 mean reward (100 episodes): 47.6300 best mean reward: 128.3400 current episode reward: 37.0000 episodes: 7561 exploration: 0.03002 learning_rate: 0.00006 elapsed time: 17378.6 seconds (4.83 hours) Timestep: 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hours) Timestep: 4260000 mean reward (100 episodes): 54.9400 best mean reward: 128.3400 current episode reward: 32.0000 episodes: 7682 exploration: 0.02665 learning_rate: 0.00006 elapsed time: 18043.3 seconds (5.01 hours) Timestep: 4270000 mean reward (100 episodes): 56.1600 best mean reward: 128.3400 current episode reward: 53.0000 episodes: 7690 exploration: 0.02642 learning_rate: 0.00006 elapsed time: 18087.2 seconds (5.02 hours) Timestep: 4280000 mean reward (100 episodes): 56.8900 best mean reward: 128.3400 current episode reward: 78.0000 episodes: 7697 exploration: 0.02620 learning_rate: 0.00006 elapsed time: 18131.5 seconds (5.04 hours) Timestep: 4290000 mean reward (100 episodes): 56.8500 best mean reward: 128.3400 current episode reward: 70.0000 episodes: 7704 exploration: 0.02597 learning_rate: 0.00006 elapsed time: 18176.1 seconds (5.05 hours) Timestep: 4300000 mean reward (100 episodes): 57.9300 best mean reward: 128.3400 current episode reward: 76.0000 episodes: 7712 exploration: 0.02575 learning_rate: 0.00006 elapsed time: 18220.0 seconds (5.06 hours) Timestep: 4310000 mean reward (100 episodes): 57.0500 best mean reward: 128.3400 current episode reward: 64.0000 episodes: 7720 exploration: 0.02552 learning_rate: 0.00006 elapsed time: 18263.4 seconds (5.07 hours) Timestep: 4320000 mean reward (100 episodes): 57.9700 best mean reward: 128.3400 current episode reward: 84.0000 episodes: 7727 exploration: 0.02530 learning_rate: 0.00006 elapsed time: 18307.8 seconds (5.09 hours) Timestep: 4330000 mean reward (100 episodes): 58.9000 best mean reward: 128.3400 current episode reward: 54.0000 episodes: 7735 exploration: 0.02508 learning_rate: 0.00006 elapsed time: 18352.3 seconds (5.10 hours) Timestep: 4340000 mean reward (100 episodes): 59.6900 best mean reward: 128.3400 current episode reward: 66.0000 episodes: 7742 exploration: 0.02485 learning_rate: 0.00006 elapsed time: 18396.1 seconds (5.11 hours) Timestep: 4350000 mean reward (100 episodes): 60.2600 best mean reward: 128.3400 current episode reward: 65.0000 episodes: 7750 exploration: 0.02462 learning_rate: 0.00006 elapsed time: 18440.5 seconds (5.12 hours) Timestep: 4360000 mean reward (100 episodes): 60.9700 best mean reward: 128.3400 current episode reward: 81.0000 episodes: 7757 exploration: 0.02440 learning_rate: 0.00006 elapsed time: 18485.1 seconds (5.13 hours) Timestep: 4370000 mean reward (100 episodes): 61.8800 best mean reward: 128.3400 current episode reward: 89.0000 episodes: 7764 exploration: 0.02417 learning_rate: 0.00006 elapsed time: 18529.2 seconds (5.15 hours) Timestep: 4380000 mean reward (100 episodes): 62.2800 best mean reward: 128.3400 current episode reward: 39.0000 episodes: 7772 exploration: 0.02395 learning_rate: 0.00006 elapsed time: 18573.5 seconds (5.16 hours) Timestep: 4390000 mean reward (100 episodes): 63.7500 best mean reward: 128.3400 current episode reward: 45.0000 episodes: 7780 exploration: 0.02372 learning_rate: 0.00006 elapsed time: 18618.4 seconds (5.17 hours) Timestep: 4400000 mean reward (100 episodes): 63.5300 best mean reward: 128.3400 current episode reward: 61.0000 episodes: 7788 exploration: 0.02350 learning_rate: 0.00006 elapsed time: 18662.0 seconds (5.18 hours) Timestep: 4410000 mean reward (100 episodes): 63.6100 best mean reward: 128.3400 current episode reward: 72.0000 episodes: 7796 exploration: 0.02327 learning_rate: 0.00006 elapsed time: 18706.2 seconds (5.20 hours) Timestep: 4420000 mean reward (100 episodes): 63.3800 best mean reward: 128.3400 current episode reward: 94.0000 episodes: 7804 exploration: 0.02305 learning_rate: 0.00006 elapsed time: 18751.7 seconds (5.21 hours) Timestep: 4430000 mean reward (100 episodes): 64.2900 best mean reward: 128.3400 current episode reward: 101.0000 episodes: 7811 exploration: 0.02282 learning_rate: 0.00006 elapsed time: 18797.0 seconds (5.22 hours) Timestep: 4440000 mean reward (100 episodes): 64.6100 best mean reward: 128.3400 current episode reward: 72.0000 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elapsed time: 19243.4 seconds (5.35 hours) Timestep: 4540000 mean reward (100 episodes): 69.7000 best mean reward: 128.3400 current episode reward: 81.0000 episodes: 7891 exploration: 0.02035 learning_rate: 0.00006 elapsed time: 19287.3 seconds (5.36 hours) Timestep: 4550000 mean reward (100 episodes): 70.4400 best mean reward: 128.3400 current episode reward: 56.0000 episodes: 7898 exploration: 0.02013 learning_rate: 0.00006 elapsed time: 19331.9 seconds (5.37 hours) Timestep: 4560000 mean reward (100 episodes): 71.5800 best mean reward: 128.3400 current episode reward: 69.0000 episodes: 7905 exploration: 0.01990 learning_rate: 0.00006 elapsed time: 19376.3 seconds (5.38 hours) Timestep: 4570000 mean reward (100 episodes): 72.5900 best mean reward: 128.3400 current episode reward: 109.0000 episodes: 7912 exploration: 0.01967 learning_rate: 0.00006 elapsed time: 19420.7 seconds (5.39 hours) Timestep: 4580000 mean reward (100 episodes): 73.6500 best mean reward: 128.3400 current episode reward: 101.0000 episodes: 7919 exploration: 0.01945 learning_rate: 0.00006 elapsed time: 19465.3 seconds (5.41 hours) Timestep: 4590000 mean reward (100 episodes): 51.8300 best mean reward: 128.3400 current episode reward: 2.0000 episodes: 7954 exploration: 0.01923 learning_rate: 0.00006 elapsed time: 19509.4 seconds (5.42 hours) Timestep: 4600000 mean reward (100 episodes): 33.9200 best mean reward: 128.3400 current episode reward: 10.0000 episodes: 7982 exploration: 0.01900 learning_rate: 0.00006 elapsed time: 19553.6 seconds (5.43 hours) Timestep: 4610000 mean reward (100 episodes): 24.4800 best mean reward: 128.3400 current episode reward: 32.0000 episodes: 7999 exploration: 0.01878 learning_rate: 0.00005 elapsed time: 19598.2 seconds (5.44 hours) Timestep: 4620000 mean reward (100 episodes): 22.7200 best mean reward: 128.3400 current episode reward: 43.0000 episodes: 8007 exploration: 0.01855 learning_rate: 0.00005 elapsed time: 19645.6 seconds (5.46 hours) Timestep: 4630000 mean reward (100 episodes): 21.0900 best mean reward: 128.3400 current episode reward: 91.0000 episodes: 8014 exploration: 0.01832 learning_rate: 0.00005 elapsed time: 19690.5 seconds (5.47 hours) Timestep: 4640000 mean reward (100 episodes): 21.6200 best mean reward: 128.3400 current episode reward: 76.0000 episodes: 8021 exploration: 0.01810 learning_rate: 0.00005 elapsed time: 19735.6 seconds (5.48 hours) Timestep: 4650000 mean reward (100 episodes): 26.5100 best mean reward: 128.3400 current episode reward: 89.0000 episodes: 8028 exploration: 0.01788 learning_rate: 0.00005 elapsed time: 19780.7 seconds (5.49 hours) Timestep: 4660000 mean reward (100 episodes): 31.8200 best mean reward: 128.3400 current episode reward: 81.0000 episodes: 8035 exploration: 0.01765 learning_rate: 0.00005 elapsed time: 19825.3 seconds (5.51 hours) Timestep: 4670000 mean reward (100 episodes): 38.1100 best mean reward: 128.3400 current episode reward: 106.0000 episodes: 8042 exploration: 0.01742 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hours) Timestep: 4770000 mean reward (100 episodes): 90.2300 best mean reward: 128.3400 current episode reward: 114.0000 episodes: 8111 exploration: 0.01517 learning_rate: 0.00005 elapsed time: 20312.9 seconds (5.64 hours) Timestep: 4780000 mean reward (100 episodes): 90.9000 best mean reward: 128.3400 current episode reward: 104.0000 episodes: 8118 exploration: 0.01495 learning_rate: 0.00005 elapsed time: 20358.0 seconds (5.66 hours) Timestep: 4790000 mean reward (100 episodes): 93.3900 best mean reward: 128.3400 current episode reward: 79.0000 episodes: 8124 exploration: 0.01472 learning_rate: 0.00005 elapsed time: 20402.6 seconds (5.67 hours) Timestep: 4800000 mean reward (100 episodes): 95.7100 best mean reward: 128.3400 current episode reward: 71.0000 episodes: 8131 exploration: 0.01450 learning_rate: 0.00005 elapsed time: 20447.1 seconds (5.68 hours) Timestep: 4810000 mean reward (100 episodes): 91.8300 best mean reward: 128.3400 current episode reward: 78.0000 episodes: 8141 exploration: 0.01427 learning_rate: 0.00005 elapsed time: 20491.7 seconds (5.69 hours) Timestep: 4820000 mean reward (100 episodes): 93.2400 best mean reward: 128.3400 current episode reward: 45.0000 episodes: 8148 exploration: 0.01405 learning_rate: 0.00005 elapsed time: 20536.9 seconds (5.70 hours) Timestep: 4830000 mean reward (100 episodes): 92.2000 best mean reward: 128.3400 current episode reward: 69.0000 episodes: 8156 exploration: 0.01382 learning_rate: 0.00005 elapsed time: 20581.3 seconds (5.72 hours) Timestep: 4840000 mean reward (100 episodes): 95.3700 best mean reward: 128.3400 current episode reward: 115.0000 episodes: 8163 exploration: 0.01360 learning_rate: 0.00005 elapsed time: 20625.8 seconds (5.73 hours) Timestep: 4850000 mean reward (100 episodes): 95.2800 best mean reward: 128.3400 current episode reward: 84.0000 episodes: 8170 exploration: 0.01337 learning_rate: 0.00005 elapsed time: 20670.3 seconds (5.74 hours) Timestep: 4860000 mean reward (100 episodes): 96.2000 best mean reward: 128.3400 current episode reward: 116.0000 episodes: 8177 exploration: 0.01315 learning_rate: 0.00005 elapsed time: 20715.0 seconds (5.75 hours) Timestep: 4870000 mean reward (100 episodes): 98.0600 best mean reward: 128.3400 current episode reward: 166.0000 episodes: 8183 exploration: 0.01292 learning_rate: 0.00005 elapsed time: 20758.9 seconds (5.77 hours) Timestep: 4880000 mean reward (100 episodes): 99.1300 best mean reward: 128.3400 current episode reward: 116.0000 episodes: 8190 exploration: 0.01270 learning_rate: 0.00005 elapsed time: 20803.5 seconds (5.78 hours) Timestep: 4890000 mean reward (100 episodes): 99.6300 best mean reward: 128.3400 current episode reward: 145.0000 episodes: 8196 exploration: 0.01247 learning_rate: 0.00005 elapsed time: 20847.5 seconds (5.79 hours) Timestep: 4900000 mean reward (100 episodes): 98.7500 best mean reward: 128.3400 current episode reward: 89.0000 episodes: 8204 exploration: 0.01225 learning_rate: 0.00005 elapsed 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reward: 81.0000 episodes: 8237 exploration: 0.01112 learning_rate: 0.00005 elapsed time: 21115.9 seconds (5.87 hours) Timestep: 4960000 mean reward (100 episodes): 110.1700 best mean reward: 128.3400 current episode reward: 110.0000 episodes: 8243 exploration: 0.01090 learning_rate: 0.00005 elapsed time: 21160.2 seconds (5.88 hours) Timestep: 4970000 mean reward (100 episodes): 112.2100 best mean reward: 128.3400 current episode reward: 90.0000 episodes: 8250 exploration: 0.01067 learning_rate: 0.00005 elapsed time: 21204.5 seconds (5.89 hours) Timestep: 4980000 mean reward (100 episodes): 114.9100 best mean reward: 128.3400 current episode reward: 231.0000 episodes: 8257 exploration: 0.01045 learning_rate: 0.00005 elapsed time: 21248.6 seconds (5.90 hours) Timestep: 4990000 mean reward (100 episodes): 114.3800 best mean reward: 128.3400 current episode reward: 155.0000 episodes: 8264 exploration: 0.01022 learning_rate: 0.00005 elapsed time: 21292.9 seconds (5.91 hours) Timestep: 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reward: 288.0000 episodes: 8806 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25208.9 seconds (7.00 hours) Timestep: 5880000 mean reward (100 episodes): 276.4100 best mean reward: 278.1900 current episode reward: 400.0000 episodes: 8811 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25253.4 seconds (7.01 hours) Timestep: 5890000 mean reward (100 episodes): 279.2900 best mean reward: 279.2900 current episode reward: 292.0000 episodes: 8817 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25297.6 seconds (7.03 hours) Timestep: 5900000 mean reward (100 episodes): 281.7200 best mean reward: 282.5600 current episode reward: 226.0000 episodes: 8823 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25341.7 seconds (7.04 hours) Timestep: 5910000 mean reward (100 episodes): 282.2600 best mean reward: 282.5600 current episode reward: 375.0000 episodes: 8828 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25385.8 seconds (7.05 hours) Timestep: 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best mean reward: 299.7300 current episode reward: 211.0000 episodes: 8881 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25830.7 seconds (7.18 hours) Timestep: 6020000 mean reward (100 episodes): 297.3700 best mean reward: 299.7600 current episode reward: 284.0000 episodes: 8887 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25875.6 seconds (7.19 hours) Timestep: 6030000 mean reward (100 episodes): 297.6800 best mean reward: 299.7600 current episode reward: 365.0000 episodes: 8893 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25920.1 seconds (7.20 hours) Timestep: 6040000 mean reward (100 episodes): 293.4200 best mean reward: 299.7600 current episode reward: 232.0000 episodes: 8898 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25963.3 seconds (7.21 hours) Timestep: 6050000 mean reward (100 episodes): 291.6800 best mean reward: 299.7600 current episode reward: 313.0000 episodes: 8904 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26007.3 seconds (7.22 hours) Timestep: 6060000 mean reward (100 episodes): 293.8500 best mean reward: 299.7600 current episode reward: 387.0000 episodes: 8909 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26052.0 seconds (7.24 hours) Timestep: 6070000 mean reward (100 episodes): 294.4900 best mean reward: 299.7600 current episode reward: 379.0000 episodes: 8914 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26096.4 seconds (7.25 hours) Timestep: 6080000 mean reward (100 episodes): 296.5500 best mean reward: 299.7600 current episode reward: 407.0000 episodes: 8919 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26140.8 seconds (7.26 hours) Timestep: 6090000 mean reward (100 episodes): 297.4900 best mean reward: 299.7600 current episode reward: 229.0000 episodes: 8924 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26184.7 seconds (7.27 hours) Timestep: 6100000 mean reward (100 episodes): 298.1900 best mean reward: 299.7600 current episode reward: 410.0000 episodes: 8929 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26228.9 seconds (7.29 hours) Timestep: 6110000 mean reward (100 episodes): 295.7200 best mean reward: 299.7600 current episode reward: 305.0000 episodes: 8934 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26272.6 seconds (7.30 hours) Timestep: 6120000 mean reward (100 episodes): 295.1500 best mean reward: 299.7600 current episode reward: 375.0000 episodes: 8940 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26317.3 seconds (7.31 hours) Timestep: 6130000 mean reward (100 episodes): 295.7300 best mean reward: 299.7600 current episode reward: 399.0000 episodes: 8944 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26361.6 seconds (7.32 hours) Timestep: 6140000 mean reward (100 episodes): 299.0200 best mean reward: 300.1700 current episode reward: 323.0000 episodes: 8949 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26406.0 seconds (7.34 hours) Timestep: 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best mean reward: 319.5300 current episode reward: 350.0000 episodes: 8999 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26846.4 seconds (7.46 hours) Timestep: 6250000 mean reward (100 episodes): 321.5400 best mean reward: 321.5400 current episode reward: 358.0000 episodes: 9004 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26895.4 seconds (7.47 hours) Timestep: 6260000 mean reward (100 episodes): 318.5100 best mean reward: 321.9400 current episode reward: 350.0000 episodes: 9009 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26939.7 seconds (7.48 hours) Timestep: 6270000 mean reward (100 episodes): 317.4600 best mean reward: 321.9400 current episode reward: 338.0000 episodes: 9013 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26984.8 seconds (7.50 hours) Timestep: 6280000 mean reward (100 episodes): 315.8400 best mean reward: 321.9400 current episode reward: 329.0000 episodes: 9017 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 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30095.6 seconds (8.36 hours) Timestep: 6980000 mean reward (100 episodes): 364.5400 best mean reward: 366.8500 current episode reward: 346.0000 episodes: 9315 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30140.3 seconds (8.37 hours) Timestep: 6990000 mean reward (100 episodes): 360.7900 best mean reward: 366.8500 current episode reward: 65.0000 episodes: 9319 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30184.7 seconds (8.38 hours) Timestep: 7000000 mean reward (100 episodes): 363.1700 best mean reward: 366.8500 current episode reward: 374.0000 episodes: 9324 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30228.5 seconds (8.40 hours) Timestep: 7010000 mean reward (100 episodes): 362.7300 best mean reward: 366.8500 current episode reward: 283.0000 episodes: 9328 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30273.9 seconds (8.41 hours) Timestep: 7020000 mean reward (100 episodes): 362.4900 best mean reward: 366.8500 current episode reward: 386.0000 episodes: 9332 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30318.6 seconds (8.42 hours) Timestep: 7030000 mean reward (100 episodes): 363.6200 best mean reward: 366.8500 current episode reward: 407.0000 episodes: 9336 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30363.7 seconds (8.43 hours) Timestep: 7040000 mean reward (100 episodes): 364.9200 best mean reward: 366.8500 current episode reward: 343.0000 episodes: 9339 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30408.5 seconds (8.45 hours) Timestep: 7050000 mean reward (100 episodes): 364.9400 best mean reward: 366.8500 current episode reward: 388.0000 episodes: 9341 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30453.6 seconds (8.46 hours) Timestep: 7060000 mean reward (100 episodes): 366.6400 best mean reward: 367.3200 current episode reward: 346.0000 episodes: 9346 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30498.0 seconds (8.47 hours) Timestep: 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0.01000 learning_rate: 0.00005 elapsed time: 30720.1 seconds (8.53 hours) Timestep: 7120000 mean reward (100 episodes): 368.4200 best mean reward: 368.4200 current episode reward: 383.0000 episodes: 9369 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30764.8 seconds (8.55 hours) Timestep: 7130000 mean reward (100 episodes): 367.0900 best mean reward: 368.5300 current episode reward: 424.0000 episodes: 9373 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30809.2 seconds (8.56 hours) Timestep: 7140000 mean reward (100 episodes): 367.5800 best mean reward: 368.5300 current episode reward: 412.0000 episodes: 9377 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30853.7 seconds (8.57 hours) Timestep: 7150000 mean reward (100 episodes): 370.0500 best mean reward: 370.0500 current episode reward: 442.0000 episodes: 9381 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30898.7 seconds (8.58 hours) Timestep: 7160000 mean reward (100 episodes): 371.5300 best mean reward: 371.5300 current episode reward: 417.0000 episodes: 9386 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30943.7 seconds (8.60 hours) Timestep: 7170000 mean reward (100 episodes): 367.8700 best mean reward: 372.0000 current episode reward: 399.0000 episodes: 9390 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30987.4 seconds (8.61 hours) Timestep: 7180000 mean reward (100 episodes): 366.9500 best mean reward: 372.0000 current episode reward: 277.0000 episodes: 9393 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31031.2 seconds (8.62 hours) Timestep: 7190000 mean reward (100 episodes): 370.8700 best mean reward: 372.0000 current episode reward: 458.0000 episodes: 9397 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31075.5 seconds (8.63 hours) Timestep: 7200000 mean reward (100 episodes): 370.9700 best mean reward: 372.0000 current episode reward: 385.0000 episodes: 9400 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31120.0 seconds (8.64 hours) Timestep: 7210000 mean reward (100 episodes): 370.7300 best mean reward: 372.0000 current episode reward: 416.0000 episodes: 9403 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31164.4 seconds (8.66 hours) Timestep: 7220000 mean reward (100 episodes): 370.9300 best mean reward: 372.0000 current episode reward: 360.0000 episodes: 9407 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31209.0 seconds (8.67 hours) Timestep: 7230000 mean reward (100 episodes): 368.7000 best mean reward: 372.0000 current episode reward: 360.0000 episodes: 9412 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31254.3 seconds (8.68 hours) Timestep: 7240000 mean reward (100 episodes): 370.4100 best mean reward: 372.0000 current episode reward: 398.0000 episodes: 9415 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31298.3 seconds (8.69 hours) Timestep: 7250000 mean reward (100 episodes): 376.8900 best mean reward: 376.8900 current episode reward: 330.0000 episodes: 9418 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31343.2 seconds (8.71 hours) Timestep: 7260000 mean reward (100 episodes): 380.6200 best mean reward: 380.6200 current episode reward: 423.0000 episodes: 9420 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31388.2 seconds (8.72 hours) Timestep: 7270000 mean reward (100 episodes): 380.2700 best mean reward: 380.6200 current episode reward: 359.0000 episodes: 9424 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31433.1 seconds (8.73 hours) Timestep: 7280000 mean reward (100 episodes): 381.6100 best mean reward: 381.6100 current episode reward: 340.0000 episodes: 9428 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31477.4 seconds (8.74 hours) Timestep: 7290000 mean reward (100 episodes): 382.1700 best mean reward: 382.1700 current episode reward: 425.0000 episodes: 9429 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31522.8 seconds (8.76 hours) Timestep: 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best mean reward: 382.5300 current episode reward: 361.0000 episodes: 9469 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31967.5 seconds (8.88 hours) Timestep: 7400000 mean reward (100 episodes): 376.5300 best mean reward: 382.5300 current episode reward: 278.0000 episodes: 9474 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32011.8 seconds (8.89 hours) Timestep: 7410000 mean reward (100 episodes): 377.3700 best mean reward: 382.5300 current episode reward: 424.0000 episodes: 9477 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32056.6 seconds (8.90 hours) Timestep: 7420000 mean reward (100 episodes): 379.1400 best mean reward: 382.5300 current episode reward: 407.0000 episodes: 9481 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32101.6 seconds (8.92 hours) Timestep: 7430000 mean reward (100 episodes): 379.2500 best mean reward: 382.5300 current episode reward: 421.0000 episodes: 9483 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 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best mean reward: 383.6900 current episode reward: 396.0000 episodes: 9549 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32990.0 seconds (9.16 hours) Timestep: 7630000 mean reward (100 episodes): 373.7300 best mean reward: 383.6900 current episode reward: 396.0000 episodes: 9553 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33034.7 seconds (9.18 hours) Timestep: 7640000 mean reward (100 episodes): 375.0600 best mean reward: 383.6900 current episode reward: 424.0000 episodes: 9555 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33079.6 seconds (9.19 hours) Timestep: 7650000 mean reward (100 episodes): 374.9200 best mean reward: 383.6900 current episode reward: 381.0000 episodes: 9558 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 33124.1 seconds (9.20 hours) Timestep: 7660000 mean reward (100 episodes): 378.1400 best mean reward: 383.6900 current episode reward: 461.0000 episodes: 9561 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 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best mean reward: 388.0500 current episode reward: 392.0000 episodes: 9622 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34013.7 seconds (9.45 hours) Timestep: 7860000 mean reward (100 episodes): 387.2500 best mean reward: 388.0500 current episode reward: 408.0000 episodes: 9626 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34058.4 seconds (9.46 hours) Timestep: 7870000 mean reward (100 episodes): 389.0000 best mean reward: 389.0900 current episode reward: 396.0000 episodes: 9630 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34102.8 seconds (9.47 hours) Timestep: 7880000 mean reward (100 episodes): 388.1700 best mean reward: 390.4400 current episode reward: 297.0000 episodes: 9634 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34147.0 seconds (9.49 hours) Timestep: 7890000 mean reward (100 episodes): 384.5700 best mean reward: 390.4400 current episode reward: 117.0000 episodes: 9637 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 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best mean reward: 390.4400 current episode reward: 416.0000 episodes: 9706 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35037.4 seconds (9.73 hours) Timestep: 8090000 mean reward (100 episodes): 371.9000 best mean reward: 390.4400 current episode reward: 416.0000 episodes: 9710 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35082.2 seconds (9.75 hours) Timestep: 8100000 mean reward (100 episodes): 377.5000 best mean reward: 390.4400 current episode reward: 830.0000 episodes: 9711 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35127.0 seconds (9.76 hours) Timestep: 8110000 mean reward (100 episodes): 376.7000 best mean reward: 390.4400 current episode reward: 424.0000 episodes: 9715 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35170.9 seconds (9.77 hours) Timestep: 8120000 mean reward (100 episodes): 377.1400 best mean reward: 390.4400 current episode reward: 425.0000 episodes: 9717 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35215.2 seconds (9.78 hours) Timestep: 8130000 mean reward (100 episodes): 377.6400 best mean reward: 390.4400 current episode reward: 411.0000 episodes: 9719 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35260.1 seconds (9.79 hours) Timestep: 8140000 mean reward (100 episodes): 378.1200 best mean reward: 390.4400 current episode reward: 414.0000 episodes: 9720 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35305.3 seconds (9.81 hours) Timestep: 8150000 mean reward (100 episodes): 378.9800 best mean reward: 390.4400 current episode reward: 408.0000 episodes: 9722 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35349.4 seconds (9.82 hours) Timestep: 8160000 mean reward (100 episodes): 378.3400 best mean reward: 390.4400 current episode reward: 413.0000 episodes: 9725 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35393.6 seconds (9.83 hours) Timestep: 8170000 mean reward (100 episodes): 377.0700 best mean reward: 390.4400 current episode reward: 428.0000 episodes: 9730 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35437.7 seconds (9.84 hours) Timestep: 8180000 mean reward (100 episodes): 378.5800 best mean reward: 390.4400 current episode reward: 435.0000 episodes: 9733 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35482.1 seconds (9.86 hours) Timestep: 8190000 mean reward (100 episodes): 379.5300 best mean reward: 390.4400 current episode reward: 359.0000 episodes: 9736 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35526.8 seconds (9.87 hours) Timestep: 8200000 mean reward (100 episodes): 383.4800 best mean reward: 390.4400 current episode reward: 424.0000 episodes: 9740 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35571.2 seconds (9.88 hours) Timestep: 8210000 mean reward (100 episodes): 382.9200 best mean reward: 390.4400 current episode reward: 310.0000 episodes: 9743 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35615.8 seconds (9.89 hours) Timestep: 8220000 mean reward (100 episodes): 380.0200 best mean reward: 390.4400 current episode reward: 90.0000 episodes: 9748 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35660.4 seconds (9.91 hours) Timestep: 8230000 mean reward (100 episodes): 375.4800 best mean reward: 390.4400 current episode reward: 124.0000 episodes: 9753 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35705.1 seconds (9.92 hours) Timestep: 8240000 mean reward (100 episodes): 373.8900 best mean reward: 390.4400 current episode reward: 275.0000 episodes: 9755 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35750.3 seconds (9.93 hours) Timestep: 8250000 mean reward (100 episodes): 374.5100 best mean reward: 390.4400 current episode reward: 428.0000 episodes: 9758 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35794.4 seconds (9.94 hours) Timestep: 8260000 mean reward (100 episodes): 378.2300 best mean reward: 390.4400 current episode reward: 220.0000 episodes: 9762 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35838.6 seconds (9.96 hours) Timestep: 8270000 mean reward (100 episodes): 376.1300 best mean reward: 390.4400 current episode reward: 381.0000 episodes: 9767 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35883.4 seconds (9.97 hours) Timestep: 8280000 mean reward (100 episodes): 374.3900 best mean reward: 390.4400 current episode reward: 227.0000 episodes: 9771 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35927.8 seconds (9.98 hours) Timestep: 8290000 mean reward (100 episodes): 376.7800 best mean reward: 390.4400 current episode reward: 435.0000 episodes: 9774 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35972.0 seconds (9.99 hours) Timestep: 8300000 mean reward (100 episodes): 377.0900 best mean reward: 390.4400 current episode reward: 332.0000 episodes: 9779 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36016.2 seconds (10.00 hours) Timestep: 8310000 mean reward (100 episodes): 377.5200 best mean reward: 390.4400 current episode reward: 334.0000 episodes: 9782 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36060.6 seconds (10.02 hours) Timestep: 8320000 mean reward (100 episodes): 378.4900 best mean reward: 390.4400 current episode reward: 426.0000 episodes: 9785 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36104.3 seconds (10.03 hours) Timestep: 8330000 mean reward (100 episodes): 377.7600 best mean reward: 390.4400 current episode reward: 382.0000 episodes: 9788 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36147.1 seconds (10.04 hours) Timestep: 8340000 mean reward (100 episodes): 379.5400 best mean reward: 390.4400 current episode reward: 388.0000 episodes: 9791 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36190.9 seconds (10.05 hours) Timestep: 8350000 mean reward (100 episodes): 378.2000 best mean reward: 390.4400 current episode reward: 313.0000 episodes: 9795 exploration: 0.01000 learning_rate: 0.00005 elapsed 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episode reward: 360.0000 episodes: 9811 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36457.8 seconds (10.13 hours) Timestep: 8410000 mean reward (100 episodes): 371.6700 best mean reward: 390.4400 current episode reward: 321.0000 episodes: 9815 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36502.4 seconds (10.14 hours) Timestep: 8420000 mean reward (100 episodes): 361.9900 best mean reward: 390.4400 current episode reward: 107.0000 episodes: 9820 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36546.8 seconds (10.15 hours) Timestep: 8430000 mean reward (100 episodes): 362.3400 best mean reward: 390.4400 current episode reward: 422.0000 episodes: 9824 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36591.9 seconds (10.16 hours) Timestep: 8440000 mean reward (100 episodes): 360.4400 best mean reward: 390.4400 current episode reward: 425.0000 episodes: 9828 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36635.9 seconds (10.18 hours) Timestep: 8450000 mean reward (100 episodes): 359.6500 best mean reward: 390.4400 current episode reward: 409.0000 episodes: 9832 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36681.6 seconds (10.19 hours) Timestep: 8460000 mean reward (100 episodes): 359.8200 best mean reward: 390.4400 current episode reward: 418.0000 episodes: 9836 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36726.6 seconds (10.20 hours) Timestep: 8470000 mean reward (100 episodes): 360.3700 best mean reward: 390.4400 current episode reward: 425.0000 episodes: 9839 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36771.5 seconds (10.21 hours) Timestep: 8480000 mean reward (100 episodes): 359.8900 best mean reward: 390.4400 current episode reward: 391.0000 episodes: 9842 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36815.6 seconds (10.23 hours) Timestep: 8490000 mean reward (100 episodes): 363.9000 best mean reward: 390.4400 current episode reward: 395.0000 episodes: 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learning_rate: 0.00005 elapsed time: 37260.0 seconds (10.35 hours) Timestep: 8590000 mean reward (100 episodes): 374.6300 best mean reward: 390.4400 current episode reward: 411.0000 episodes: 9876 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37304.9 seconds (10.36 hours) Timestep: 8600000 mean reward (100 episodes): 373.2000 best mean reward: 390.4400 current episode reward: 347.0000 episodes: 9880 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37350.5 seconds (10.38 hours) Timestep: 8610000 mean reward (100 episodes): 373.8400 best mean reward: 390.4400 current episode reward: 327.0000 episodes: 9884 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37394.8 seconds (10.39 hours) Timestep: 8620000 mean reward (100 episodes): 373.7400 best mean reward: 390.4400 current episode reward: 363.0000 episodes: 9888 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37440.3 seconds (10.40 hours) Timestep: 8630000 mean reward (100 episodes): 372.7000 best mean reward: 390.4400 current episode reward: 324.0000 episodes: 9892 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37485.3 seconds (10.41 hours) Timestep: 8640000 mean reward (100 episodes): 373.8700 best mean reward: 390.4400 current episode reward: 398.0000 episodes: 9895 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37530.1 seconds (10.43 hours) Timestep: 8650000 mean reward (100 episodes): 370.9900 best mean reward: 390.4400 current episode reward: 347.0000 episodes: 9898 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37574.3 seconds (10.44 hours) Timestep: 8660000 mean reward (100 episodes): 371.1200 best mean reward: 390.4400 current episode reward: 427.0000 episodes: 9901 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37619.3 seconds (10.45 hours) Timestep: 8670000 mean reward (100 episodes): 370.4800 best mean reward: 390.4400 current episode reward: 436.0000 episodes: 9905 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 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reward: 419.0000 episodes: 9922 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37886.7 seconds (10.52 hours) Timestep: 8730000 mean reward (100 episodes): 376.6100 best mean reward: 390.4400 current episode reward: 258.0000 episodes: 9925 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37931.1 seconds (10.54 hours) Timestep: 8740000 mean reward (100 episodes): 377.8000 best mean reward: 390.4400 current episode reward: 344.0000 episodes: 9927 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37975.5 seconds (10.55 hours) Timestep: 8750000 mean reward (100 episodes): 377.5700 best mean reward: 390.4400 current episode reward: 379.0000 episodes: 9929 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38019.2 seconds (10.56 hours) Timestep: 8760000 mean reward (100 episodes): 375.1600 best mean reward: 390.4400 current episode reward: 434.0000 episodes: 9933 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38063.8 seconds (10.57 hours) 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mean reward: 390.4400 current episode reward: 415.0000 episodes: 10002 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38914.5 seconds (10.81 hours) Timestep: 8960000 mean reward (100 episodes): 370.0100 best mean reward: 390.4400 current episode reward: 418.0000 episodes: 10006 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38959.4 seconds (10.82 hours) Timestep: 8970000 mean reward (100 episodes): 369.0200 best mean reward: 390.4400 current episode reward: 200.0000 episodes: 10009 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39003.5 seconds (10.83 hours) Timestep: 8980000 mean reward (100 episodes): 368.3000 best mean reward: 390.4400 current episode reward: 428.0000 episodes: 10013 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39047.9 seconds (10.85 hours) Timestep: 8990000 mean reward (100 episodes): 370.2100 best mean reward: 390.4400 current episode reward: 431.0000 episodes: 10016 exploration: 0.01000 learning_rate: 0.00005 elapsed 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current episode reward: 399.0000 episodes: 10035 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39314.0 seconds (10.92 hours) Timestep: 9050000 mean reward (100 episodes): 377.6800 best mean reward: 390.4400 current episode reward: 344.0000 episodes: 10038 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39357.7 seconds (10.93 hours) Timestep: 9060000 mean reward (100 episodes): 377.6600 best mean reward: 390.4400 current episode reward: 422.0000 episodes: 10040 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39402.2 seconds (10.95 hours) Timestep: 9070000 mean reward (100 episodes): 378.3600 best mean reward: 390.4400 current episode reward: 424.0000 episodes: 10044 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39446.2 seconds (10.96 hours) Timestep: 9080000 mean reward (100 episodes): 378.7600 best mean reward: 390.4400 current episode reward: 398.0000 episodes: 10048 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39490.9 seconds (10.97 hours) Timestep: 9090000 mean reward (100 episodes): 377.4600 best mean reward: 390.4400 current episode reward: 306.0000 episodes: 10052 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39535.7 seconds (10.98 hours) Timestep: 9100000 mean reward (100 episodes): 380.0800 best mean reward: 390.4400 current episode reward: 791.0000 episodes: 10055 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39580.6 seconds (10.99 hours) Timestep: 9110000 mean reward (100 episodes): 377.6900 best mean reward: 390.4400 current episode reward: 424.0000 episodes: 10059 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39625.4 seconds (11.01 hours) Timestep: 9120000 mean reward (100 episodes): 376.9200 best mean reward: 390.4400 current episode reward: 459.0000 episodes: 10063 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39670.2 seconds (11.02 hours) Timestep: 9130000 mean reward (100 episodes): 379.0200 best mean reward: 390.4400 current episode reward: 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9180000 mean reward (100 episodes): 385.0900 best mean reward: 390.4400 current episode reward: 412.0000 episodes: 10082 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39937.5 seconds (11.09 hours) Timestep: 9190000 mean reward (100 episodes): 388.0000 best mean reward: 390.4400 current episode reward: 667.0000 episodes: 10085 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39981.5 seconds (11.11 hours) Timestep: 9200000 mean reward (100 episodes): 382.6500 best mean reward: 390.4400 current episode reward: 92.0000 episodes: 10090 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40025.8 seconds (11.12 hours) Timestep: 9210000 mean reward (100 episodes): 383.5000 best mean reward: 390.4400 current episode reward: 419.0000 episodes: 10093 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40070.5 seconds (11.13 hours) Timestep: 9220000 mean reward (100 episodes): 388.0300 best mean reward: 390.4400 current episode reward: 342.0000 episodes: 10096 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40114.8 seconds (11.14 hours) Timestep: 9230000 mean reward (100 episodes): 390.0100 best mean reward: 390.4400 current episode reward: 379.0000 episodes: 10099 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40158.3 seconds (11.16 hours) Timestep: 9240000 mean reward (100 episodes): 391.1600 best mean reward: 391.1600 current episode reward: 434.0000 episodes: 10103 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40203.3 seconds (11.17 hours) Timestep: 9250000 mean reward (100 episodes): 391.4500 best mean reward: 391.6900 current episode reward: 387.0000 episodes: 10105 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40248.1 seconds (11.18 hours) Timestep: 9260000 mean reward (100 episodes): 394.7600 best mean reward: 394.7600 current episode reward: 328.0000 episodes: 10110 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40292.7 seconds (11.19 hours) Timestep: 9270000 mean reward (100 episodes): 394.4500 best mean reward: 394.9400 current episode reward: 393.0000 episodes: 10113 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40337.2 seconds (11.20 hours) Timestep: 9280000 mean reward (100 episodes): 396.7000 best mean reward: 397.5100 current episode reward: 402.0000 episodes: 10117 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40381.7 seconds (11.22 hours) Timestep: 9290000 mean reward (100 episodes): 396.6500 best mean reward: 397.5100 current episode reward: 418.0000 episodes: 10119 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40426.2 seconds (11.23 hours) Timestep: 9300000 mean reward (100 episodes): 395.8000 best mean reward: 397.5100 current episode reward: 380.0000 episodes: 10122 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40470.4 seconds (11.24 hours) Timestep: 9310000 mean reward (100 episodes): 390.8900 best mean reward: 397.5100 current episode reward: 381.0000 episodes: 10127 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40515.2 seconds (11.25 hours) Timestep: 9320000 mean reward (100 episodes): 393.2000 best mean reward: 397.5100 current episode reward: 411.0000 episodes: 10130 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40559.1 seconds (11.27 hours) Timestep: 9330000 mean reward (100 episodes): 393.1800 best mean reward: 397.5100 current episode reward: 369.0000 episodes: 10133 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40602.6 seconds (11.28 hours) Timestep: 9340000 mean reward (100 episodes): 393.2500 best mean reward: 397.5100 current episode reward: 425.0000 episodes: 10138 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40646.8 seconds (11.29 hours) Timestep: 9350000 mean reward (100 episodes): 392.7300 best mean reward: 397.5100 current episode reward: 398.0000 episodes: 10140 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40690.7 seconds (11.30 hours) Timestep: 9360000 mean reward (100 episodes): 392.8300 best mean reward: 397.5100 current episode reward: 125.0000 episodes: 10144 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40735.9 seconds (11.32 hours) Timestep: 9370000 mean reward (100 episodes): 391.9300 best mean reward: 397.5100 current episode reward: 405.0000 episodes: 10149 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40780.4 seconds (11.33 hours) Timestep: 9380000 mean reward (100 episodes): 392.6500 best mean reward: 397.5100 current episode reward: 410.0000 episodes: 10153 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40825.4 seconds (11.34 hours) Timestep: 9390000 mean reward (100 episodes): 390.0800 best mean reward: 397.5100 current episode reward: 421.0000 episodes: 10156 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40869.9 seconds (11.35 hours) Timestep: 9400000 mean reward (100 episodes): 390.3300 best mean reward: 397.5100 current episode reward: 428.0000 episodes: 10159 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40914.4 seconds (11.37 hours) Timestep: 9410000 mean reward (100 episodes): 391.8900 best mean reward: 397.5100 current episode reward: 300.0000 episodes: 10164 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40959.3 seconds (11.38 hours) Timestep: 9420000 mean reward (100 episodes): 392.0700 best mean reward: 397.5100 current episode reward: 420.0000 episodes: 10167 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41003.5 seconds (11.39 hours) Timestep: 9430000 mean reward (100 episodes): 388.5700 best mean reward: 397.5100 current episode reward: 390.0000 episodes: 10171 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41049.4 seconds (11.40 hours) Timestep: 9440000 mean reward (100 episodes): 387.6600 best mean reward: 397.5100 current episode reward: 369.0000 episodes: 10174 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41094.8 seconds (11.42 hours) Timestep: 9450000 mean reward (100 episodes): 387.1900 best mean reward: 397.5100 current episode reward: 428.0000 episodes: 10177 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41138.8 seconds (11.43 hours) Timestep: 9460000 mean reward (100 episodes): 384.8600 best mean reward: 397.5100 current episode reward: 417.0000 episodes: 10181 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41183.1 seconds (11.44 hours) Timestep: 9470000 mean reward (100 episodes): 386.6700 best mean reward: 397.5100 current episode reward: 424.0000 episodes: 10184 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41228.0 seconds (11.45 hours) Timestep: 9480000 mean reward (100 episodes): 386.8900 best mean reward: 397.5100 current episode reward: 437.0000 episodes: 10188 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41272.5 seconds (11.46 hours) Timestep: 9490000 mean reward (100 episodes): 394.2400 best mean reward: 397.5100 current episode reward: 420.0000 episodes: 10191 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41317.0 seconds (11.48 hours) Timestep: 9500000 mean reward (100 episodes): 395.2000 best mean reward: 397.5100 current episode reward: 366.0000 episodes: 10194 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41361.6 seconds (11.49 hours) Timestep: 9510000 mean reward (100 episodes): 389.2800 best mean reward: 397.5100 current episode reward: 131.0000 episodes: 10197 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41406.2 seconds (11.50 hours) Timestep: 9520000 mean reward (100 episodes): 389.8800 best mean reward: 397.5100 current episode reward: 421.0000 episodes: 10201 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41451.3 seconds (11.51 hours) Timestep: 9530000 mean reward (100 episodes): 386.7600 best mean reward: 397.5100 current episode reward: 415.0000 episodes: 10205 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41495.1 seconds (11.53 hours) Timestep: 9540000 mean reward (100 episodes): 387.7400 best mean reward: 397.5100 current episode reward: 412.0000 episodes: 10209 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41539.4 seconds (11.54 hours) Timestep: 9550000 mean reward (100 episodes): 387.4400 best mean reward: 397.5100 current episode reward: 412.0000 episodes: 10213 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41584.0 seconds (11.55 hours) Timestep: 9560000 mean reward (100 episodes): 385.8800 best mean reward: 397.5100 current episode reward: 428.0000 episodes: 10215 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41629.0 seconds (11.56 hours) Timestep: 9570000 mean reward (100 episodes): 386.6200 best mean reward: 397.5100 current episode reward: 297.0000 episodes: 10219 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41674.3 seconds (11.58 hours) Timestep: 9580000 mean reward (100 episodes): 390.4500 best mean reward: 397.5100 current episode reward: 810.0000 episodes: 10222 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41718.7 seconds (11.59 hours) Timestep: 9590000 mean reward (100 episodes): 396.2800 best mean reward: 397.5100 current episode reward: 427.0000 episodes: 10226 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41763.1 seconds (11.60 hours) Timestep: 9600000 mean reward (100 episodes): 396.5200 best mean reward: 397.5100 current episode reward: 421.0000 episodes: 10228 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41807.9 seconds (11.61 hours) Timestep: 9610000 mean reward (100 episodes): 394.2100 best mean reward: 397.5100 current episode reward: 194.0000 episodes: 10231 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41852.9 seconds (11.63 hours) Timestep: 9620000 mean reward (100 episodes): 394.3000 best mean reward: 397.5100 current episode reward: 283.0000 episodes: 10235 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41897.9 seconds (11.64 hours) Timestep: 9630000 mean reward (100 episodes): 394.2700 best mean reward: 397.5100 current episode reward: 406.0000 episodes: 10238 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41942.4 seconds (11.65 hours) Timestep: 9640000 mean reward (100 episodes): 395.5100 best mean reward: 397.5100 current episode reward: 424.0000 episodes: 10242 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41986.6 seconds (11.66 hours) Timestep: 9650000 mean reward (100 episodes): 395.7000 best mean reward: 398.0700 current episode reward: 375.0000 episodes: 10247 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42031.8 seconds (11.68 hours) Timestep: 9660000 mean reward (100 episodes): 395.3000 best mean reward: 398.0700 current episode reward: 331.0000 episodes: 10251 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42076.2 seconds (11.69 hours) Timestep: 9670000 mean reward (100 episodes): 396.6700 best mean reward: 398.0700 current episode reward: 412.0000 episodes: 10254 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42120.8 seconds (11.70 hours) Timestep: 9680000 mean reward (100 episodes): 397.4900 best mean reward: 398.0700 current episode reward: 416.0000 episodes: 10257 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42165.5 seconds (11.71 hours) Timestep: 9690000 mean reward (100 episodes): 396.8500 best mean reward: 398.0700 current episode reward: 334.0000 episodes: 10260 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42209.8 seconds (11.72 hours) Timestep: 9700000 mean reward (100 episodes): 394.9400 best mean reward: 398.0700 current episode reward: 401.0000 episodes: 10265 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42254.6 seconds (11.74 hours) Timestep: 9710000 mean reward (100 episodes): 396.9900 best mean reward: 398.0700 current episode reward: 626.0000 episodes: 10269 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42298.9 seconds (11.75 hours) Timestep: 9713708 mean reward (100 episodes): 395.5300 best mean reward: 398.0700 current episode reward: 253.0000 episodes: 10270 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42315.7 seconds (11.75 hours) ================================================ FILE: dqn/logs_text/Breakout_s002.text ================================================ ('AVAILABLE GPUS: ', [u'device: 0, name: TITAN X (Pascal), pci bus id: 0000:01:00.0']) task = Task Timestep: 60000 mean reward (100 episodes): 1.5100 best mean reward: 1.6200 current episode reward: 4.0000 episodes: 339 exploration: 0.94600 learning_rate: 0.00010 elapsed time: 100.0 seconds (0.03 hours) Timestep: 70000 mean reward (100 episodes): 1.6100 best mean reward: 1.6200 current episode reward: 2.0000 episodes: 397 exploration: 0.93700 learning_rate: 0.00010 elapsed time: 133.9 seconds (0.04 hours) Timestep: 80000 mean reward (100 episodes): 1.4200 best mean reward: 1.6200 current episode reward: 0.0000 episodes: 455 exploration: 0.92800 learning_rate: 0.00010 elapsed time: 167.1 seconds (0.05 hours) Timestep: 90000 mean reward (100 episodes): 1.6300 best mean reward: 1.7400 current episode reward: 3.0000 episodes: 508 exploration: 0.91900 learning_rate: 0.00010 elapsed time: 200.3 seconds (0.06 hours) Timestep: 100000 mean reward (100 episodes): 1.7200 best mean reward: 1.7600 current episode reward: 4.0000 episodes: 564 exploration: 0.91000 learning_rate: 0.00010 elapsed time: 234.3 seconds (0.07 hours) Timestep: 110000 mean reward (100 episodes): 1.6800 best mean reward: 1.8100 current episode reward: 0.0000 episodes: 619 exploration: 0.90100 learning_rate: 0.00010 elapsed time: 268.2 seconds (0.07 hours) Timestep: 120000 mean reward (100 episodes): 1.6500 best mean reward: 1.8100 current episode reward: 0.0000 episodes: 672 exploration: 0.89200 learning_rate: 0.00010 elapsed time: 301.6 seconds (0.08 hours) Timestep: 130000 mean reward (100 episodes): 1.8200 best mean reward: 1.9000 current episode reward: 0.0000 episodes: 726 exploration: 0.88300 learning_rate: 0.00010 elapsed time: 335.2 seconds (0.09 hours) Timestep: 140000 mean reward (100 episodes): 1.6900 best mean reward: 1.9500 current episode reward: 1.0000 episodes: 782 exploration: 0.87400 learning_rate: 0.00010 elapsed time: 369.6 seconds (0.10 hours) Timestep: 150000 mean reward (100 episodes): 1.4600 best mean reward: 1.9500 current episode reward: 0.0000 episodes: 839 exploration: 0.86500 learning_rate: 0.00010 elapsed time: 403.5 seconds (0.11 hours) Timestep: 160000 mean reward (100 episodes): 1.4100 best mean reward: 1.9500 current episode reward: 4.0000 episodes: 899 exploration: 0.85600 learning_rate: 0.00010 elapsed time: 437.4 seconds (0.12 hours) Timestep: 170000 mean reward (100 episodes): 1.4100 best mean reward: 1.9500 current episode reward: 2.0000 episodes: 958 exploration: 0.84700 learning_rate: 0.00010 elapsed time: 471.8 seconds (0.13 hours) Timestep: 180000 mean reward (100 episodes): 1.5900 best mean reward: 1.9500 current episode reward: 2.0000 episodes: 1010 exploration: 0.83800 learning_rate: 0.00010 elapsed time: 506.6 seconds (0.14 hours) Timestep: 190000 mean reward (100 episodes): 1.7500 best mean reward: 1.9500 current episode reward: 1.0000 episodes: 1064 exploration: 0.82900 learning_rate: 0.00010 elapsed time: 540.8 seconds (0.15 hours) Timestep: 200000 mean reward (100 episodes): 1.8000 best mean reward: 1.9500 current episode reward: 6.0000 episodes: 1116 exploration: 0.82000 learning_rate: 0.00010 elapsed time: 575.1 seconds (0.16 hours) Timestep: 210000 mean reward (100 episodes): 2.3800 best mean reward: 2.3800 current episode reward: 8.0000 episodes: 1159 exploration: 0.81100 learning_rate: 0.00010 elapsed time: 609.4 seconds (0.17 hours) Timestep: 220000 mean reward (100 episodes): 2.5500 best mean reward: 2.5700 current episode reward: 1.0000 episodes: 1207 exploration: 0.80200 learning_rate: 0.00010 elapsed time: 643.8 seconds (0.18 hours) Timestep: 230000 mean reward (100 episodes): 2.5400 best mean reward: 2.6600 current episode reward: 3.0000 episodes: 1252 exploration: 0.79300 learning_rate: 0.00010 elapsed time: 678.4 seconds (0.19 hours) Timestep: 240000 mean reward (100 episodes): 2.6000 best mean reward: 2.6600 current episode reward: 1.0000 episodes: 1300 exploration: 0.78400 learning_rate: 0.00010 elapsed time: 713.1 seconds (0.20 hours) Timestep: 250000 mean reward (100 episodes): 2.9000 best mean reward: 2.9300 current episode reward: 0.0000 episodes: 1340 exploration: 0.77500 learning_rate: 0.00010 elapsed time: 747.5 seconds (0.21 hours) Timestep: 260000 mean reward (100 episodes): 3.2200 best mean reward: 3.2400 current episode reward: 4.0000 episodes: 1380 exploration: 0.76600 learning_rate: 0.00010 elapsed time: 782.4 seconds (0.22 hours) Timestep: 270000 mean reward (100 episodes): 3.4600 best mean reward: 3.5400 current episode reward: 0.0000 episodes: 1419 exploration: 0.75700 learning_rate: 0.00010 elapsed time: 817.0 seconds (0.23 hours) Timestep: 280000 mean reward (100 episodes): 3.1700 best mean reward: 3.5400 current episode reward: 1.0000 episodes: 1462 exploration: 0.74800 learning_rate: 0.00010 elapsed time: 851.9 seconds (0.24 hours) Timestep: 290000 mean reward (100 episodes): 3.7700 best mean reward: 3.8500 current episode reward: 2.0000 episodes: 1495 exploration: 0.73900 learning_rate: 0.00010 elapsed time: 887.0 seconds (0.25 hours) Timestep: 300000 mean reward (100 episodes): 4.1400 best mean reward: 4.1400 current episode reward: 2.0000 episodes: 1530 exploration: 0.73000 learning_rate: 0.00010 elapsed time: 921.6 seconds (0.26 hours) Timestep: 310000 mean reward (100 episodes): 4.6800 best mean reward: 4.6800 current episode reward: 7.0000 episodes: 1564 exploration: 0.72100 learning_rate: 0.00010 elapsed time: 956.6 seconds (0.27 hours) Timestep: 320000 mean reward (100 episodes): 4.3800 best mean reward: 4.7600 current episode reward: 2.0000 episodes: 1600 exploration: 0.71200 learning_rate: 0.00010 elapsed time: 991.8 seconds (0.28 hours) Timestep: 330000 mean reward (100 episodes): 4.8400 best mean reward: 4.8400 current episode reward: 4.0000 episodes: 1630 exploration: 0.70300 learning_rate: 0.00010 elapsed time: 1027.2 seconds (0.29 hours) Timestep: 340000 mean reward (100 episodes): 4.9600 best mean reward: 5.0900 current episode reward: 2.0000 episodes: 1664 exploration: 0.69400 learning_rate: 0.00010 elapsed time: 1062.6 seconds (0.30 hours) Timestep: 350000 mean reward (100 episodes): 4.9100 best mean reward: 5.0900 current episode reward: 9.0000 episodes: 1699 exploration: 0.68500 learning_rate: 0.00010 elapsed time: 1098.3 seconds (0.31 hours) Timestep: 360000 mean reward (100 episodes): 4.5900 best mean reward: 5.0900 current episode reward: 8.0000 episodes: 1733 exploration: 0.67600 learning_rate: 0.00010 elapsed time: 1133.9 seconds (0.31 hours) Timestep: 370000 mean reward (100 episodes): 4.3700 best mean reward: 5.0900 current episode reward: 6.0000 episodes: 1770 exploration: 0.66700 learning_rate: 0.00010 elapsed time: 1169.9 seconds (0.32 hours) Timestep: 380000 mean reward (100 episodes): 4.4200 best mean reward: 5.0900 current episode reward: 6.0000 episodes: 1805 exploration: 0.65800 learning_rate: 0.00010 elapsed time: 1205.7 seconds (0.33 hours) Timestep: 390000 mean reward (100 episodes): 4.2800 best mean reward: 5.0900 current episode reward: 5.0000 episodes: 1839 exploration: 0.64900 learning_rate: 0.00010 elapsed time: 1242.1 seconds (0.35 hours) Timestep: 400000 mean reward (100 episodes): 4.8100 best mean reward: 5.0900 current episode reward: 2.0000 episodes: 1871 exploration: 0.64000 learning_rate: 0.00010 elapsed time: 1278.1 seconds (0.36 hours) Timestep: 410000 mean reward (100 episodes): 5.0400 best mean reward: 5.2300 current episode reward: 3.0000 episodes: 1904 exploration: 0.63100 learning_rate: 0.00010 elapsed time: 1314.3 seconds (0.37 hours) Timestep: 420000 mean reward (100 episodes): 5.4300 best mean reward: 5.4900 current episode reward: 8.0000 episodes: 1933 exploration: 0.62200 learning_rate: 0.00010 elapsed time: 1350.8 seconds (0.38 hours) Timestep: 430000 mean reward (100 episodes): 5.4600 best mean reward: 5.6000 current episode reward: 3.0000 episodes: 1963 exploration: 0.61300 learning_rate: 0.00010 elapsed time: 1387.1 seconds (0.39 hours) Timestep: 440000 mean reward (100 episodes): 5.9000 best mean reward: 5.9400 current episode reward: 5.0000 episodes: 1991 exploration: 0.60400 learning_rate: 0.00010 elapsed time: 1423.4 seconds (0.40 hours) Timestep: 450000 mean reward (100 episodes): 6.3000 best mean reward: 6.4500 current episode reward: 4.0000 episodes: 2020 exploration: 0.59500 learning_rate: 0.00010 elapsed time: 1462.4 seconds (0.41 hours) Timestep: 460000 mean reward (100 episodes): 6.4500 best mean reward: 6.5800 current episode reward: 3.0000 episodes: 2047 exploration: 0.58600 learning_rate: 0.00010 elapsed time: 1500.2 seconds (0.42 hours) Timestep: 470000 mean reward (100 episodes): 6.7700 best mean reward: 6.9400 current episode reward: 6.0000 episodes: 2075 exploration: 0.57700 learning_rate: 0.00010 elapsed time: 1537.7 seconds (0.43 hours) Timestep: 480000 mean reward (100 episodes): 6.6000 best mean reward: 6.9400 current episode reward: 9.0000 episodes: 2103 exploration: 0.56800 learning_rate: 0.00010 elapsed time: 1575.3 seconds (0.44 hours) Timestep: 490000 mean reward (100 episodes): 6.7900 best mean reward: 6.9400 current episode reward: 7.0000 episodes: 2130 exploration: 0.55900 learning_rate: 0.00010 elapsed time: 1612.7 seconds (0.45 hours) Timestep: 500000 mean reward (100 episodes): 6.7800 best mean reward: 6.9400 current episode reward: 7.0000 episodes: 2159 exploration: 0.55000 learning_rate: 0.00010 elapsed time: 1651.1 seconds (0.46 hours) Timestep: 510000 mean reward (100 episodes): 6.6800 best mean reward: 6.9400 current episode reward: 8.0000 episodes: 2187 exploration: 0.54100 learning_rate: 0.00010 elapsed time: 1689.1 seconds (0.47 hours) Timestep: 520000 mean reward (100 episodes): 6.4600 best mean reward: 6.9400 current episode reward: 11.0000 episodes: 2217 exploration: 0.53200 learning_rate: 0.00010 elapsed time: 1727.1 seconds (0.48 hours) Timestep: 530000 mean reward (100 episodes): 6.6100 best mean reward: 6.9400 current episode reward: 6.0000 episodes: 2243 exploration: 0.52300 learning_rate: 0.00010 elapsed time: 1765.8 seconds (0.49 hours) Timestep: 540000 mean reward (100 episodes): 6.8300 best mean reward: 6.9400 current episode reward: 7.0000 episodes: 2269 exploration: 0.51400 learning_rate: 0.00010 elapsed time: 1803.9 seconds (0.50 hours) Timestep: 550000 mean reward (100 episodes): 7.0800 best mean reward: 7.0900 current episode reward: 4.0000 episodes: 2296 exploration: 0.50500 learning_rate: 0.00010 elapsed time: 1842.5 seconds (0.51 hours) Timestep: 560000 mean reward (100 episodes): 7.7100 best mean reward: 7.7400 current episode reward: 3.0000 episodes: 2321 exploration: 0.49600 learning_rate: 0.00010 elapsed time: 1880.8 seconds (0.52 hours) Timestep: 570000 mean reward (100 episodes): 7.8200 best mean reward: 7.8300 current episode reward: 10.0000 episodes: 2346 exploration: 0.48700 learning_rate: 0.00010 elapsed time: 1919.5 seconds (0.53 hours) Timestep: 580000 mean reward (100 episodes): 7.9300 best mean reward: 8.2800 current episode reward: 10.0000 episodes: 2370 exploration: 0.47800 learning_rate: 0.00010 elapsed time: 1958.3 seconds (0.54 hours) Timestep: 590000 mean reward (100 episodes): 8.1400 best mean reward: 8.3000 current episode reward: 6.0000 episodes: 2393 exploration: 0.46900 learning_rate: 0.00010 elapsed time: 1997.5 seconds (0.55 hours) Timestep: 600000 mean reward (100 episodes): 8.4800 best mean reward: 8.6400 current episode reward: 19.0000 episodes: 2414 exploration: 0.46000 learning_rate: 0.00010 elapsed time: 2035.7 seconds (0.57 hours) Timestep: 610000 mean reward (100 episodes): 8.7400 best mean reward: 8.7400 current episode reward: 8.0000 episodes: 2439 exploration: 0.45100 learning_rate: 0.00010 elapsed time: 2075.0 seconds (0.58 hours) Timestep: 620000 mean reward (100 episodes): 8.5400 best mean reward: 8.8000 current episode reward: 17.0000 episodes: 2463 exploration: 0.44200 learning_rate: 0.00010 elapsed time: 2114.2 seconds (0.59 hours) Timestep: 630000 mean reward (100 episodes): 9.2300 best mean reward: 9.2300 current episode reward: 9.0000 episodes: 2485 exploration: 0.43300 learning_rate: 0.00010 elapsed time: 2153.0 seconds (0.60 hours) Timestep: 640000 mean reward (100 episodes): 9.0800 best mean reward: 9.3300 current episode reward: 5.0000 episodes: 2508 exploration: 0.42400 learning_rate: 0.00010 elapsed time: 2192.3 seconds (0.61 hours) Timestep: 650000 mean reward (100 episodes): 9.0000 best mean reward: 9.3300 current episode reward: 10.0000 episodes: 2532 exploration: 0.41500 learning_rate: 0.00010 elapsed time: 2231.9 seconds (0.62 hours) Timestep: 660000 mean reward (100 episodes): 9.0200 best mean reward: 9.3300 current episode reward: 7.0000 episodes: 2554 exploration: 0.40600 learning_rate: 0.00010 elapsed time: 2271.1 seconds (0.63 hours) Timestep: 670000 mean reward (100 episodes): 8.9300 best mean reward: 9.3300 current episode reward: 5.0000 episodes: 2577 exploration: 0.39700 learning_rate: 0.00010 elapsed time: 2310.2 seconds (0.64 hours) Timestep: 680000 mean reward (100 episodes): 9.1500 best mean reward: 9.3300 current episode reward: 17.0000 episodes: 2598 exploration: 0.38800 learning_rate: 0.00010 elapsed time: 2349.9 seconds (0.65 hours) Timestep: 690000 mean reward (100 episodes): 9.5400 best mean reward: 9.5600 current episode reward: 9.0000 episodes: 2618 exploration: 0.37900 learning_rate: 0.00010 elapsed time: 2389.8 seconds (0.66 hours) Timestep: 700000 mean reward (100 episodes): 10.0900 best mean reward: 10.1100 current episode reward: 8.0000 episodes: 2639 exploration: 0.37000 learning_rate: 0.00010 elapsed time: 2429.4 seconds (0.67 hours) Timestep: 710000 mean reward (100 episodes): 10.5800 best mean reward: 10.5800 current episode reward: 20.0000 episodes: 2659 exploration: 0.36100 learning_rate: 0.00010 elapsed time: 2469.7 seconds (0.69 hours) Timestep: 720000 mean reward (100 episodes): 11.2900 best mean reward: 11.2900 current episode reward: 15.0000 episodes: 2679 exploration: 0.35200 learning_rate: 0.00010 elapsed time: 2509.2 seconds (0.70 hours) Timestep: 730000 mean reward (100 episodes): 11.8200 best mean reward: 11.8200 current episode reward: 12.0000 episodes: 2697 exploration: 0.34300 learning_rate: 0.00010 elapsed time: 2549.2 seconds (0.71 hours) Timestep: 740000 mean reward (100 episodes): 12.5700 best mean reward: 12.5700 current episode reward: 9.0000 episodes: 2713 exploration: 0.33400 learning_rate: 0.00010 elapsed time: 2589.8 seconds (0.72 hours) Timestep: 750000 mean reward (100 episodes): 12.9200 best mean reward: 12.9600 current episode reward: 7.0000 episodes: 2732 exploration: 0.32500 learning_rate: 0.00010 elapsed time: 2630.1 seconds (0.73 hours) Timestep: 760000 mean reward (100 episodes): 13.6100 best mean reward: 13.6100 current episode reward: 17.0000 episodes: 2749 exploration: 0.31600 learning_rate: 0.00010 elapsed time: 2670.9 seconds (0.74 hours) Timestep: 770000 mean reward (100 episodes): 14.1000 best mean reward: 14.1000 current episode reward: 13.0000 episodes: 2767 exploration: 0.30700 learning_rate: 0.00010 elapsed time: 2711.3 seconds (0.75 hours) Timestep: 780000 mean reward (100 episodes): 14.5100 best mean reward: 14.5100 current episode reward: 17.0000 episodes: 2783 exploration: 0.29800 learning_rate: 0.00010 elapsed time: 2753.5 seconds (0.76 hours) Timestep: 790000 mean reward (100 episodes): 14.7100 best mean reward: 14.7500 current episode reward: 12.0000 episodes: 2801 exploration: 0.28900 learning_rate: 0.00010 elapsed time: 2795.1 seconds (0.78 hours) Timestep: 800000 mean reward (100 episodes): 14.8500 best mean reward: 14.8500 current episode reward: 16.0000 episodes: 2816 exploration: 0.28000 learning_rate: 0.00010 elapsed time: 2836.6 seconds (0.79 hours) Timestep: 810000 mean reward (100 episodes): 15.4600 best mean reward: 15.4600 current episode reward: 21.0000 episodes: 2831 exploration: 0.27100 learning_rate: 0.00010 elapsed time: 2878.6 seconds (0.80 hours) Timestep: 820000 mean reward (100 episodes): 16.0600 best mean reward: 16.0600 current episode reward: 16.0000 episodes: 2847 exploration: 0.26200 learning_rate: 0.00010 elapsed time: 2921.0 seconds (0.81 hours) Timestep: 830000 mean reward (100 episodes): 16.2000 best mean reward: 16.2900 current episode reward: 16.0000 episodes: 2864 exploration: 0.25300 learning_rate: 0.00010 elapsed time: 2962.7 seconds (0.82 hours) Timestep: 840000 mean reward (100 episodes): 17.0000 best mean reward: 17.0000 current episode reward: 24.0000 episodes: 2878 exploration: 0.24400 learning_rate: 0.00010 elapsed time: 3004.4 seconds (0.83 hours) Timestep: 850000 mean reward (100 episodes): 17.9300 best mean reward: 17.9300 current episode reward: 32.0000 episodes: 2891 exploration: 0.23500 learning_rate: 0.00010 elapsed time: 3046.0 seconds (0.85 hours) Timestep: 860000 mean reward (100 episodes): 18.2800 best mean reward: 18.4500 current episode reward: 19.0000 episodes: 2906 exploration: 0.22600 learning_rate: 0.00010 elapsed time: 3087.1 seconds (0.86 hours) Timestep: 870000 mean reward (100 episodes): 18.5600 best mean reward: 18.5600 current episode reward: 30.0000 episodes: 2922 exploration: 0.21700 learning_rate: 0.00010 elapsed time: 3128.7 seconds (0.87 hours) Timestep: 880000 mean reward (100 episodes): 18.8600 best mean reward: 18.8600 current episode reward: 29.0000 episodes: 2936 exploration: 0.20800 learning_rate: 0.00010 elapsed time: 3170.4 seconds (0.88 hours) Timestep: 890000 mean reward (100 episodes): 19.5600 best mean reward: 19.5600 current episode reward: 28.0000 episodes: 2949 exploration: 0.19900 learning_rate: 0.00010 elapsed time: 3212.4 seconds (0.89 hours) Timestep: 900000 mean reward (100 episodes): 20.1700 best mean reward: 20.2700 current episode reward: 6.0000 episodes: 2963 exploration: 0.19000 learning_rate: 0.00010 elapsed time: 3255.4 seconds (0.90 hours) Timestep: 910000 mean reward (100 episodes): 21.0100 best mean reward: 21.0100 current episode reward: 33.0000 episodes: 2975 exploration: 0.18100 learning_rate: 0.00010 elapsed time: 3297.6 seconds (0.92 hours) Timestep: 920000 mean reward (100 episodes): 21.5900 best mean reward: 21.5900 current episode reward: 18.0000 episodes: 2987 exploration: 0.17200 learning_rate: 0.00010 elapsed time: 3340.8 seconds (0.93 hours) Timestep: 930000 mean reward (100 episodes): 21.9800 best mean reward: 22.0900 current episode reward: 17.0000 episodes: 2998 exploration: 0.16300 learning_rate: 0.00010 elapsed time: 3384.0 seconds (0.94 hours) Timestep: 940000 mean reward (100 episodes): 23.6200 best mean reward: 23.6200 current episode reward: 26.0000 episodes: 3010 exploration: 0.15400 learning_rate: 0.00010 elapsed time: 3429.7 seconds (0.95 hours) Timestep: 950000 mean reward (100 episodes): 25.6700 best mean reward: 25.6700 current episode reward: 24.0000 episodes: 3021 exploration: 0.14500 learning_rate: 0.00010 elapsed time: 3472.7 seconds (0.96 hours) Timestep: 960000 mean reward (100 episodes): 27.1600 best mean reward: 27.1600 current episode reward: 77.0000 episodes: 3031 exploration: 0.13600 learning_rate: 0.00010 elapsed time: 3516.2 seconds (0.98 hours) Timestep: 970000 mean reward (100 episodes): 27.8800 best mean reward: 27.8800 current episode reward: 27.0000 episodes: 3043 exploration: 0.12700 learning_rate: 0.00010 elapsed time: 3559.8 seconds (0.99 hours) Timestep: 980000 mean reward (100 episodes): 28.5900 best mean reward: 28.6200 current episode reward: 23.0000 episodes: 3054 exploration: 0.11800 learning_rate: 0.00010 elapsed time: 3603.1 seconds (1.00 hours) Timestep: 990000 mean reward (100 episodes): 30.4000 best mean reward: 30.4000 current episode reward: 42.0000 episodes: 3063 exploration: 0.10900 learning_rate: 0.00010 elapsed time: 3646.6 seconds (1.01 hours) Timestep: 1000000 mean reward (100 episodes): 31.4200 best mean reward: 31.4200 current episode reward: 37.0000 episodes: 3073 exploration: 0.10000 learning_rate: 0.00010 elapsed time: 3690.1 seconds (1.03 hours) Timestep: 1010000 mean reward (100 episodes): 32.5200 best mean reward: 32.5400 current episode reward: 20.0000 episodes: 3083 exploration: 0.09978 learning_rate: 0.00010 elapsed time: 3734.1 seconds (1.04 hours) Timestep: 1020000 mean reward (100 episodes): 33.1100 best mean reward: 33.1100 current episode reward: 37.0000 episodes: 3092 exploration: 0.09955 learning_rate: 0.00010 elapsed time: 3778.0 seconds (1.05 hours) Timestep: 1030000 mean reward (100 episodes): 34.9400 best mean reward: 34.9400 current episode reward: 58.0000 episodes: 3102 exploration: 0.09933 learning_rate: 0.00010 elapsed time: 3822.1 seconds (1.06 hours) Timestep: 1040000 mean reward (100 episodes): 35.8100 best mean reward: 35.8600 current episode reward: 34.0000 episodes: 3111 exploration: 0.09910 learning_rate: 0.00010 elapsed time: 3865.4 seconds (1.07 hours) Timestep: 1050000 mean reward (100 episodes): 35.8700 best mean reward: 36.1100 current episode reward: 36.0000 episodes: 3121 exploration: 0.09888 learning_rate: 0.00010 elapsed time: 3908.8 seconds (1.09 hours) Timestep: 1060000 mean reward (100 episodes): 37.1500 best mean reward: 37.1500 current episode reward: 25.0000 episodes: 3130 exploration: 0.09865 learning_rate: 0.00010 elapsed time: 3952.8 seconds (1.10 hours) Timestep: 1070000 mean reward (100 episodes): 38.1000 best mean reward: 38.4700 current episode reward: 18.0000 episodes: 3139 exploration: 0.09842 learning_rate: 0.00010 elapsed time: 3996.8 seconds (1.11 hours) Timestep: 1080000 mean reward (100 episodes): 40.0500 best mean reward: 40.0500 current episode reward: 47.0000 episodes: 3148 exploration: 0.09820 learning_rate: 0.00010 elapsed time: 4040.3 seconds (1.12 hours) Timestep: 1090000 mean reward (100 episodes): 40.7000 best mean reward: 40.7300 current episode reward: 29.0000 episodes: 3157 exploration: 0.09798 learning_rate: 0.00010 elapsed time: 4083.5 seconds (1.13 hours) Timestep: 1100000 mean reward (100 episodes): 40.7500 best mean reward: 41.1300 current episode reward: 15.0000 episodes: 3166 exploration: 0.09775 learning_rate: 0.00010 elapsed time: 4127.3 seconds (1.15 hours) Timestep: 1110000 mean reward (100 episodes): 42.2200 best mean reward: 42.2200 current episode reward: 54.0000 episodes: 3175 exploration: 0.09753 learning_rate: 0.00010 elapsed time: 4170.4 seconds (1.16 hours) Timestep: 1120000 mean reward (100 episodes): 43.2900 best mean reward: 43.2900 current episode reward: 30.0000 episodes: 3183 exploration: 0.09730 learning_rate: 0.00010 elapsed time: 4214.7 seconds (1.17 hours) Timestep: 1130000 mean reward (100 episodes): 44.2100 best mean reward: 44.3900 current episode reward: 41.0000 episodes: 3192 exploration: 0.09708 learning_rate: 0.00010 elapsed time: 4259.0 seconds (1.18 hours) Timestep: 1140000 mean reward (100 episodes): 45.4100 best mean reward: 45.4100 current episode reward: 50.0000 episodes: 3201 exploration: 0.09685 learning_rate: 0.00010 elapsed time: 4302.3 seconds (1.20 hours) Timestep: 1150000 mean reward (100 episodes): 45.0600 best mean reward: 45.4100 current episode reward: 52.0000 episodes: 3211 exploration: 0.09663 learning_rate: 0.00010 elapsed time: 4346.1 seconds (1.21 hours) Timestep: 1160000 mean reward (100 episodes): 45.7400 best mean reward: 45.7400 current episode reward: 49.0000 episodes: 3220 exploration: 0.09640 learning_rate: 0.00010 elapsed time: 4389.8 seconds (1.22 hours) Timestep: 1170000 mean reward (100 episodes): 47.0400 best mean reward: 47.1500 current episode reward: 43.0000 episodes: 3227 exploration: 0.09618 learning_rate: 0.00010 elapsed time: 4434.1 seconds (1.23 hours) Timestep: 1180000 mean reward (100 episodes): 47.1900 best mean reward: 47.5900 current episode reward: 38.0000 episodes: 3236 exploration: 0.09595 learning_rate: 0.00010 elapsed time: 4476.4 seconds (1.24 hours) Timestep: 1190000 mean reward (100 episodes): 47.4500 best mean reward: 47.9400 current episode reward: 46.0000 episodes: 3245 exploration: 0.09573 learning_rate: 0.00010 elapsed time: 4519.7 seconds (1.26 hours) Timestep: 1200000 mean reward (100 episodes): 48.7300 best mean reward: 48.7300 current episode reward: 65.0000 episodes: 3253 exploration: 0.09550 learning_rate: 0.00010 elapsed time: 4563.2 seconds (1.27 hours) Timestep: 1210000 mean reward (100 episodes): 49.2600 best mean reward: 49.3100 current episode reward: 47.0000 episodes: 3260 exploration: 0.09527 learning_rate: 0.00010 elapsed time: 4607.1 seconds (1.28 hours) Timestep: 1220000 mean reward (100 episodes): 50.1300 best mean reward: 50.3300 current episode reward: 67.0000 episodes: 3269 exploration: 0.09505 learning_rate: 0.00010 elapsed time: 4651.0 seconds (1.29 hours) Timestep: 1230000 mean reward (100 episodes): 50.5100 best mean reward: 50.6300 current episode reward: 44.0000 episodes: 3277 exploration: 0.09483 learning_rate: 0.00010 elapsed time: 4694.5 seconds (1.30 hours) Timestep: 1240000 mean reward (100 episodes): 49.8300 best mean reward: 50.6300 current episode reward: 50.0000 episodes: 3287 exploration: 0.09460 learning_rate: 0.00010 elapsed time: 4738.9 seconds (1.32 hours) Timestep: 1250000 mean reward (100 episodes): 49.1500 best mean reward: 50.6300 current episode reward: 37.0000 episodes: 3296 exploration: 0.09438 learning_rate: 0.00010 elapsed time: 4783.0 seconds (1.33 hours) Timestep: 1260000 mean reward (100 episodes): 49.5200 best mean reward: 50.6300 current episode reward: 75.0000 episodes: 3304 exploration: 0.09415 learning_rate: 0.00010 elapsed time: 4827.5 seconds (1.34 hours) Timestep: 1270000 mean reward (100 episodes): 51.3300 best mean reward: 51.3300 current episode reward: 51.0000 episodes: 3312 exploration: 0.09393 learning_rate: 0.00010 elapsed time: 4870.8 seconds (1.35 hours) Timestep: 1280000 mean reward (100 episodes): 50.9300 best mean reward: 52.0300 current episode reward: 35.0000 episodes: 3322 exploration: 0.09370 learning_rate: 0.00010 elapsed time: 4914.4 seconds (1.37 hours) Timestep: 1290000 mean reward (100 episodes): 50.3700 best mean reward: 52.0300 current episode reward: 52.0000 episodes: 3330 exploration: 0.09348 learning_rate: 0.00010 elapsed time: 4957.9 seconds (1.38 hours) Timestep: 1300000 mean reward (100 episodes): 51.3400 best mean reward: 52.0300 current episode reward: 44.0000 episodes: 3338 exploration: 0.09325 learning_rate: 0.00010 elapsed time: 5001.5 seconds (1.39 hours) Timestep: 1310000 mean reward (100 episodes): 51.4500 best mean reward: 52.0700 current episode reward: 26.0000 episodes: 3347 exploration: 0.09303 learning_rate: 0.00010 elapsed time: 5045.0 seconds (1.40 hours) Timestep: 1320000 mean reward (100 episodes): 51.6600 best mean reward: 52.0700 current episode reward: 57.0000 episodes: 3356 exploration: 0.09280 learning_rate: 0.00010 elapsed time: 5088.8 seconds (1.41 hours) Timestep: 1330000 mean reward (100 episodes): 51.4000 best mean reward: 52.0700 current episode reward: 33.0000 episodes: 3363 exploration: 0.09258 learning_rate: 0.00010 elapsed time: 5132.1 seconds (1.43 hours) Timestep: 1340000 mean reward (100 episodes): 51.9000 best mean reward: 52.0700 current episode reward: 63.0000 episodes: 3372 exploration: 0.09235 learning_rate: 0.00010 elapsed time: 5176.2 seconds (1.44 hours) Timestep: 1350000 mean reward (100 episodes): 52.7400 best mean reward: 52.7400 current episode reward: 56.0000 episodes: 3379 exploration: 0.09213 learning_rate: 0.00010 elapsed time: 5220.8 seconds (1.45 hours) Timestep: 1360000 mean reward (100 episodes): 52.9300 best mean reward: 52.9300 current episode reward: 58.0000 episodes: 3388 exploration: 0.09190 learning_rate: 0.00010 elapsed time: 5265.0 seconds (1.46 hours) Timestep: 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current episode reward: 61.0000 episodes: 3465 exploration: 0.08965 learning_rate: 0.00009 elapsed time: 5703.3 seconds (1.58 hours) Timestep: 1470000 mean reward (100 episodes): 62.1800 best mean reward: 62.5400 current episode reward: 119.0000 episodes: 3473 exploration: 0.08943 learning_rate: 0.00009 elapsed time: 5747.2 seconds (1.60 hours) Timestep: 1480000 mean reward (100 episodes): 63.7500 best mean reward: 63.7500 current episode reward: 99.0000 episodes: 3480 exploration: 0.08920 learning_rate: 0.00009 elapsed time: 5790.7 seconds (1.61 hours) Timestep: 1490000 mean reward (100 episodes): 64.4700 best mean reward: 64.4700 current episode reward: 59.0000 episodes: 3488 exploration: 0.08897 learning_rate: 0.00009 elapsed time: 5834.8 seconds (1.62 hours) Timestep: 1500000 mean reward (100 episodes): 66.5700 best mean reward: 66.7800 current episode reward: 52.0000 episodes: 3495 exploration: 0.08875 learning_rate: 0.00009 elapsed time: 5878.6 seconds (1.63 hours) Timestep: 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current episode reward: 5.0000 episodes: 3613 exploration: 0.08650 learning_rate: 0.00009 elapsed time: 6316.3 seconds (1.75 hours) Timestep: 1610000 mean reward (100 episodes): 38.7800 best mean reward: 67.1500 current episode reward: 72.0000 episodes: 3625 exploration: 0.08628 learning_rate: 0.00009 elapsed time: 6360.5 seconds (1.77 hours) Timestep: 1620000 mean reward (100 episodes): 40.3100 best mean reward: 67.1500 current episode reward: 2.0000 episodes: 3639 exploration: 0.08605 learning_rate: 0.00009 elapsed time: 6403.8 seconds (1.78 hours) Timestep: 1630000 mean reward (100 episodes): 39.3600 best mean reward: 67.1500 current episode reward: 59.0000 episodes: 3647 exploration: 0.08582 learning_rate: 0.00009 elapsed time: 6447.7 seconds (1.79 hours) Timestep: 1640000 mean reward (100 episodes): 39.2500 best mean reward: 67.1500 current episode reward: 55.0000 episodes: 3656 exploration: 0.08560 learning_rate: 0.00009 elapsed time: 6491.0 seconds (1.80 hours) Timestep: 1650000 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episodes: 4011 exploration: 0.08020 learning_rate: 0.00009 elapsed time: 7549.0 seconds (2.10 hours) Timestep: 1890000 mean reward (100 episodes): 40.9800 best mean reward: 67.1500 current episode reward: 55.0000 episodes: 4018 exploration: 0.07998 learning_rate: 0.00009 elapsed time: 7593.7 seconds (2.11 hours) Timestep: 1900000 mean reward (100 episodes): 46.0700 best mean reward: 67.1500 current episode reward: 48.0000 episodes: 4025 exploration: 0.07975 learning_rate: 0.00009 elapsed time: 7637.5 seconds (2.12 hours) Timestep: 1910000 mean reward (100 episodes): 50.8200 best mean reward: 67.1500 current episode reward: 41.0000 episodes: 4034 exploration: 0.07952 learning_rate: 0.00009 elapsed time: 7681.1 seconds (2.13 hours) Timestep: 1920000 mean reward (100 episodes): 53.7400 best mean reward: 67.1500 current episode reward: 51.0000 episodes: 4041 exploration: 0.07930 learning_rate: 0.00009 elapsed time: 7725.1 seconds (2.15 hours) Timestep: 1930000 mean reward (100 episodes): 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seconds (2.21 hours) Timestep: 1980000 mean reward (100 episodes): 52.4800 best mean reward: 67.1500 current episode reward: 86.0000 episodes: 4109 exploration: 0.07795 learning_rate: 0.00009 elapsed time: 7985.7 seconds (2.22 hours) Timestep: 1990000 mean reward (100 episodes): 47.4800 best mean reward: 67.1500 current episode reward: 49.0000 episodes: 4122 exploration: 0.07773 learning_rate: 0.00009 elapsed time: 8029.8 seconds (2.23 hours) Timestep: 2000000 mean reward (100 episodes): 46.3600 best mean reward: 67.1500 current episode reward: 23.0000 episodes: 4130 exploration: 0.07750 learning_rate: 0.00009 elapsed time: 8073.6 seconds (2.24 hours) Timestep: 2010000 mean reward (100 episodes): 44.8400 best mean reward: 67.1500 current episode reward: 51.0000 episodes: 4139 exploration: 0.07728 learning_rate: 0.00009 elapsed time: 8117.3 seconds (2.25 hours) Timestep: 2020000 mean reward (100 episodes): 44.0600 best mean reward: 67.1500 current episode reward: 103.0000 episodes: 4149 exploration: 0.07705 learning_rate: 0.00009 elapsed time: 8161.1 seconds (2.27 hours) Timestep: 2030000 mean reward (100 episodes): 44.5300 best mean reward: 67.1500 current episode reward: 63.0000 episodes: 4158 exploration: 0.07683 learning_rate: 0.00009 elapsed time: 8205.0 seconds (2.28 hours) Timestep: 2040000 mean reward (100 episodes): 42.0000 best mean reward: 67.1500 current episode reward: 10.0000 episodes: 4167 exploration: 0.07660 learning_rate: 0.00009 elapsed time: 8248.8 seconds (2.29 hours) Timestep: 2050000 mean reward (100 episodes): 40.2700 best mean reward: 67.1500 current episode reward: 79.0000 episodes: 4176 exploration: 0.07637 learning_rate: 0.00009 elapsed time: 8292.6 seconds (2.30 hours) Timestep: 2060000 mean reward (100 episodes): 44.8200 best mean reward: 67.1500 current episode reward: 68.0000 episodes: 4183 exploration: 0.07615 learning_rate: 0.00009 elapsed time: 8336.7 seconds (2.32 hours) Timestep: 2070000 mean reward (100 episodes): 46.6700 best 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hours) Timestep: 2120000 mean reward (100 episodes): 46.9600 best mean reward: 67.1500 current episode reward: 47.0000 episodes: 4251 exploration: 0.07480 learning_rate: 0.00009 elapsed time: 8599.3 seconds (2.39 hours) Timestep: 2130000 mean reward (100 episodes): 45.2900 best mean reward: 67.1500 current episode reward: 95.0000 episodes: 4262 exploration: 0.07458 learning_rate: 0.00009 elapsed time: 8643.3 seconds (2.40 hours) Timestep: 2140000 mean reward (100 episodes): 46.7800 best mean reward: 67.1500 current episode reward: 64.0000 episodes: 4270 exploration: 0.07435 learning_rate: 0.00009 elapsed time: 8687.6 seconds (2.41 hours) Timestep: 2150000 mean reward (100 episodes): 45.1700 best mean reward: 67.1500 current episode reward: 126.0000 episodes: 4280 exploration: 0.07412 learning_rate: 0.00009 elapsed time: 8731.3 seconds (2.43 hours) Timestep: 2160000 mean reward (100 episodes): 42.9900 best mean reward: 67.1500 current episode reward: 63.0000 episodes: 4293 exploration: 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Timestep: 2260000 mean reward (100 episodes): 54.1000 best mean reward: 67.1500 current episode reward: 53.0000 episodes: 4380 exploration: 0.07165 learning_rate: 0.00008 elapsed time: 9217.1 seconds (2.56 hours) Timestep: 2270000 mean reward (100 episodes): 57.0500 best mean reward: 67.1500 current episode reward: 85.0000 episodes: 4391 exploration: 0.07143 learning_rate: 0.00008 elapsed time: 9260.7 seconds (2.57 hours) Timestep: 2280000 mean reward (100 episodes): 57.5100 best mean reward: 67.1500 current episode reward: 74.0000 episodes: 4398 exploration: 0.07120 learning_rate: 0.00008 elapsed time: 9305.2 seconds (2.58 hours) Timestep: 2290000 mean reward (100 episodes): 51.1600 best mean reward: 67.1500 current episode reward: 47.0000 episodes: 4413 exploration: 0.07097 learning_rate: 0.00008 elapsed time: 9349.3 seconds (2.60 hours) Timestep: 2300000 mean reward (100 episodes): 50.2600 best mean reward: 67.1500 current episode reward: 94.0000 episodes: 4423 exploration: 0.07075 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current episode reward: 73.0000 episodes: 4461 exploration: 0.06962 learning_rate: 0.00008 elapsed time: 9611.8 seconds (2.67 hours) Timestep: 2360000 mean reward (100 episodes): 65.9300 best mean reward: 67.1500 current episode reward: 26.0000 episodes: 4469 exploration: 0.06940 learning_rate: 0.00008 elapsed time: 9655.6 seconds (2.68 hours) Timestep: 2370000 mean reward (100 episodes): 65.0200 best mean reward: 67.1500 current episode reward: 88.0000 episodes: 4480 exploration: 0.06918 learning_rate: 0.00008 elapsed time: 9699.2 seconds (2.69 hours) Timestep: 2380000 mean reward (100 episodes): 64.7900 best mean reward: 67.1500 current episode reward: 7.0000 episodes: 4489 exploration: 0.06895 learning_rate: 0.00008 elapsed time: 9743.3 seconds (2.71 hours) Timestep: 2390000 mean reward (100 episodes): 64.8800 best mean reward: 67.1500 current episode reward: 57.0000 episodes: 4496 exploration: 0.06873 learning_rate: 0.00008 elapsed time: 9787.0 seconds (2.72 hours) Timestep: 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Timestep: 2540000 mean reward (100 episodes): 77.6600 best mean reward: 77.6600 current episode reward: 87.0000 episodes: 4619 exploration: 0.06535 learning_rate: 0.00008 elapsed time: 10445.7 seconds (2.90 hours) Timestep: 2550000 mean reward (100 episodes): 78.1200 best mean reward: 78.2100 current episode reward: 71.0000 episodes: 4626 exploration: 0.06513 learning_rate: 0.00008 elapsed time: 10490.5 seconds (2.91 hours) Timestep: 2560000 mean reward (100 episodes): 76.0000 best mean reward: 78.2900 current episode reward: 36.0000 episodes: 4635 exploration: 0.06490 learning_rate: 0.00008 elapsed time: 10534.3 seconds (2.93 hours) Timestep: 2570000 mean reward (100 episodes): 73.1200 best mean reward: 78.2900 current episode reward: 65.0000 episodes: 4645 exploration: 0.06468 learning_rate: 0.00008 elapsed time: 10578.2 seconds (2.94 hours) Timestep: 2580000 mean reward (100 episodes): 72.5700 best mean reward: 78.2900 current episode reward: 104.0000 episodes: 4653 exploration: 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hours) Timestep: 2680000 mean reward (100 episodes): 79.4000 best mean reward: 79.6900 current episode reward: 82.0000 episodes: 4729 exploration: 0.06220 learning_rate: 0.00008 elapsed time: 11063.7 seconds (3.07 hours) Timestep: 2690000 mean reward (100 episodes): 81.4500 best mean reward: 81.7700 current episode reward: 37.0000 episodes: 4739 exploration: 0.06198 learning_rate: 0.00008 elapsed time: 11108.2 seconds (3.09 hours) Timestep: 2700000 mean reward (100 episodes): 83.0400 best mean reward: 83.9900 current episode reward: 54.0000 episodes: 4747 exploration: 0.06175 learning_rate: 0.00008 elapsed time: 11151.4 seconds (3.10 hours) Timestep: 2710000 mean reward (100 episodes): 84.3500 best mean reward: 84.8800 current episode reward: 45.0000 episodes: 4755 exploration: 0.06153 learning_rate: 0.00008 elapsed time: 11194.9 seconds (3.11 hours) Timestep: 2720000 mean reward (100 episodes): 84.0900 best mean reward: 84.8800 current episode reward: 72.0000 episodes: 4762 exploration: 0.06130 learning_rate: 0.00008 elapsed time: 11239.2 seconds (3.12 hours) Timestep: 2730000 mean reward (100 episodes): 86.1800 best mean reward: 86.4700 current episode reward: 124.0000 episodes: 4770 exploration: 0.06108 learning_rate: 0.00008 elapsed time: 11282.8 seconds (3.13 hours) Timestep: 2740000 mean reward (100 episodes): 84.8000 best mean reward: 86.4700 current episode reward: 126.0000 episodes: 4777 exploration: 0.06085 learning_rate: 0.00008 elapsed time: 11327.3 seconds (3.15 hours) Timestep: 2750000 mean reward (100 episodes): 85.0100 best mean reward: 87.1300 current episode reward: 91.0000 episodes: 4784 exploration: 0.06062 learning_rate: 0.00008 elapsed time: 11371.7 seconds (3.16 hours) Timestep: 2760000 mean reward (100 episodes): 88.9200 best mean reward: 88.9200 current episode reward: 122.0000 episodes: 4792 exploration: 0.06040 learning_rate: 0.00008 elapsed time: 11415.5 seconds (3.17 hours) Timestep: 2770000 mean reward (100 episodes): 89.7000 best mean reward: 89.7900 current episode reward: 82.0000 episodes: 4801 exploration: 0.06017 learning_rate: 0.00008 elapsed time: 11459.5 seconds (3.18 hours) Timestep: 2780000 mean reward (100 episodes): 91.0400 best mean reward: 91.0400 current episode reward: 120.0000 episodes: 4808 exploration: 0.05995 learning_rate: 0.00008 elapsed time: 11503.2 seconds (3.20 hours) Timestep: 2790000 mean reward (100 episodes): 94.8700 best mean reward: 94.8700 current episode reward: 86.0000 episodes: 4815 exploration: 0.05973 learning_rate: 0.00008 elapsed time: 11546.9 seconds (3.21 hours) Timestep: 2800000 mean reward (100 episodes): 95.0600 best mean reward: 95.2300 current episode reward: 137.0000 episodes: 4822 exploration: 0.05950 learning_rate: 0.00008 elapsed time: 11590.3 seconds (3.22 hours) Timestep: 2810000 mean reward (100 episodes): 92.4300 best mean reward: 95.2300 current episode reward: 227.0000 episodes: 4829 exploration: 0.05928 learning_rate: 0.00008 elapsed time: 11633.7 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elapsed time: 12252.3 seconds (3.40 hours) Timestep: 2960000 mean reward (100 episodes): 93.8800 best mean reward: 95.2300 current episode reward: 88.0000 episodes: 4949 exploration: 0.05590 learning_rate: 0.00008 elapsed time: 12296.0 seconds (3.42 hours) Timestep: 2970000 mean reward (100 episodes): 93.2700 best mean reward: 95.2900 current episode reward: 116.0000 episodes: 4957 exploration: 0.05568 learning_rate: 0.00008 elapsed time: 12340.3 seconds (3.43 hours) Timestep: 2980000 mean reward (100 episodes): 90.9100 best mean reward: 95.2900 current episode reward: 148.0000 episodes: 4964 exploration: 0.05545 learning_rate: 0.00008 elapsed time: 12384.1 seconds (3.44 hours) Timestep: 2990000 mean reward (100 episodes): 93.6100 best mean reward: 95.2900 current episode reward: 38.0000 episodes: 4971 exploration: 0.05523 learning_rate: 0.00008 elapsed time: 12428.4 seconds (3.45 hours) Timestep: 3000000 mean reward (100 episodes): 94.1900 best mean reward: 95.6800 current episode 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reward (100 episodes): 97.4800 best mean reward: 101.3000 current episode reward: 58.0000 episodes: 5017 exploration: 0.05388 learning_rate: 0.00007 elapsed time: 12696.5 seconds (3.53 hours) Timestep: 3060000 mean reward (100 episodes): 94.4800 best mean reward: 101.3000 current episode reward: 163.0000 episodes: 5025 exploration: 0.05365 learning_rate: 0.00007 elapsed time: 12740.8 seconds (3.54 hours) Timestep: 3070000 mean reward (100 episodes): 93.9900 best mean reward: 101.3000 current episode reward: 9.0000 episodes: 5033 exploration: 0.05343 learning_rate: 0.00007 elapsed time: 12785.0 seconds (3.55 hours) Timestep: 3080000 mean reward (100 episodes): 98.3400 best mean reward: 101.3000 current episode reward: 153.0000 episodes: 5040 exploration: 0.05320 learning_rate: 0.00007 elapsed time: 12829.6 seconds (3.56 hours) Timestep: 3090000 mean reward (100 episodes): 100.5300 best mean reward: 101.3000 current episode reward: 122.0000 episodes: 5046 exploration: 0.05298 learning_rate: 0.00007 elapsed time: 12874.0 seconds (3.58 hours) Timestep: 3100000 mean reward (100 episodes): 103.3000 best mean reward: 103.3000 current episode reward: 283.0000 episodes: 5053 exploration: 0.05275 learning_rate: 0.00007 elapsed time: 12918.1 seconds (3.59 hours) Timestep: 3110000 mean reward (100 episodes): 107.6200 best mean reward: 107.7600 current episode reward: 83.0000 episodes: 5060 exploration: 0.05253 learning_rate: 0.00007 elapsed time: 12961.9 seconds (3.60 hours) Timestep: 3120000 mean reward (100 episodes): 107.3000 best mean reward: 108.1500 current episode reward: 61.0000 episodes: 5068 exploration: 0.05230 learning_rate: 0.00007 elapsed time: 13006.3 seconds (3.61 hours) Timestep: 3130000 mean reward (100 episodes): 111.8100 best mean reward: 111.8100 current episode reward: 45.0000 episodes: 5075 exploration: 0.05208 learning_rate: 0.00007 elapsed time: 13050.9 seconds (3.63 hours) Timestep: 3140000 mean reward (100 episodes): 111.7200 best mean reward: 113.3700 current episode reward: 97.0000 episodes: 5081 exploration: 0.05185 learning_rate: 0.00007 elapsed time: 13094.2 seconds (3.64 hours) Timestep: 3150000 mean reward (100 episodes): 115.4300 best mean reward: 115.4300 current episode reward: 187.0000 episodes: 5088 exploration: 0.05163 learning_rate: 0.00007 elapsed time: 13138.5 seconds (3.65 hours) Timestep: 3160000 mean reward (100 episodes): 121.3600 best mean reward: 121.3600 current episode reward: 174.0000 episodes: 5095 exploration: 0.05140 learning_rate: 0.00007 elapsed time: 13182.1 seconds (3.66 hours) Timestep: 3170000 mean reward (100 episodes): 122.8500 best mean reward: 122.8800 current episode reward: 109.0000 episodes: 5101 exploration: 0.05117 learning_rate: 0.00007 elapsed time: 13225.7 seconds (3.67 hours) Timestep: 3180000 mean reward (100 episodes): 124.1700 best mean reward: 125.1800 current episode reward: 128.0000 episodes: 5108 exploration: 0.05095 learning_rate: 0.00007 elapsed time: 13269.8 seconds (3.69 hours) Timestep: 3190000 mean reward (100 episodes): 122.4600 best mean reward: 126.1100 current episode reward: 16.0000 episodes: 5118 exploration: 0.05072 learning_rate: 0.00007 elapsed time: 13314.1 seconds (3.70 hours) Timestep: 3200000 mean reward (100 episodes): 122.5800 best mean reward: 126.1100 current episode reward: 77.0000 episodes: 5126 exploration: 0.05050 learning_rate: 0.00007 elapsed time: 13358.1 seconds (3.71 hours) Timestep: 3210000 mean reward (100 episodes): 122.9400 best mean reward: 126.1100 current episode reward: 126.0000 episodes: 5132 exploration: 0.05028 learning_rate: 0.00007 elapsed time: 13403.0 seconds (3.72 hours) Timestep: 3220000 mean reward (100 episodes): 125.1400 best mean reward: 126.1100 current episode reward: 143.0000 episodes: 5139 exploration: 0.05005 learning_rate: 0.00007 elapsed time: 13447.5 seconds (3.74 hours) Timestep: 3230000 mean reward (100 episodes): 125.5200 best mean reward: 126.1100 current episode reward: 236.0000 episodes: 5146 exploration: 0.04983 learning_rate: 0.00007 elapsed time: 13491.6 seconds (3.75 hours) Timestep: 3240000 mean reward (100 episodes): 122.2600 best mean reward: 126.1100 current episode reward: 193.0000 episodes: 5153 exploration: 0.04960 learning_rate: 0.00007 elapsed time: 13535.7 seconds (3.76 hours) Timestep: 3250000 mean reward (100 episodes): 121.0000 best mean reward: 126.1100 current episode reward: 63.0000 episodes: 5160 exploration: 0.04938 learning_rate: 0.00007 elapsed time: 13579.8 seconds (3.77 hours) Timestep: 3260000 mean reward (100 episodes): 122.2600 best mean reward: 126.1100 current episode reward: 89.0000 episodes: 5166 exploration: 0.04915 learning_rate: 0.00007 elapsed time: 13623.7 seconds (3.78 hours) Timestep: 3270000 mean reward (100 episodes): 121.9000 best mean reward: 126.1100 current episode reward: 204.0000 episodes: 5173 exploration: 0.04892 learning_rate: 0.00007 elapsed time: 13667.7 seconds (3.80 hours) Timestep: 3280000 mean reward (100 episodes): 123.5600 best mean reward: 126.1100 current episode reward: 103.0000 episodes: 5181 exploration: 0.04870 learning_rate: 0.00007 elapsed time: 13712.3 seconds (3.81 hours) Timestep: 3290000 mean reward (100 episodes): 122.2500 best mean reward: 126.1100 current episode reward: 3.0000 episodes: 5188 exploration: 0.04847 learning_rate: 0.00007 elapsed time: 13756.9 seconds (3.82 hours) Timestep: 3300000 mean reward (100 episodes): 115.3700 best mean reward: 126.1100 current episode reward: 131.0000 episodes: 5196 exploration: 0.04825 learning_rate: 0.00007 elapsed time: 13800.7 seconds (3.83 hours) Timestep: 3310000 mean reward (100 episodes): 111.0500 best mean reward: 126.1100 current episode reward: 113.0000 episodes: 5202 exploration: 0.04802 learning_rate: 0.00007 elapsed time: 13845.4 seconds (3.85 hours) Timestep: 3320000 mean reward (100 episodes): 114.0600 best mean reward: 126.1100 current episode reward: 54.0000 episodes: 5209 exploration: 0.04780 learning_rate: 0.00007 elapsed time: 13888.8 seconds (3.86 hours) Timestep: 3330000 mean reward (100 episodes): 118.6400 best mean reward: 126.1100 current episode reward: 125.0000 episodes: 5216 exploration: 0.04757 learning_rate: 0.00007 elapsed time: 13933.2 seconds (3.87 hours) Timestep: 3340000 mean reward (100 episodes): 125.0800 best mean reward: 126.1100 current episode reward: 128.0000 episodes: 5223 exploration: 0.04735 learning_rate: 0.00007 elapsed time: 13976.7 seconds (3.88 hours) Timestep: 3350000 mean reward (100 episodes): 129.3300 best mean reward: 129.3300 current episode reward: 244.0000 episodes: 5230 exploration: 0.04713 learning_rate: 0.00007 elapsed time: 14020.8 seconds (3.89 hours) Timestep: 3360000 mean reward (100 episodes): 127.2900 best mean reward: 129.9000 current episode reward: 81.0000 episodes: 5237 exploration: 0.04690 learning_rate: 0.00007 elapsed time: 14065.1 seconds (3.91 hours) Timestep: 3370000 mean reward (100 episodes): 127.1400 best mean reward: 129.9000 current episode reward: 131.0000 episodes: 5243 exploration: 0.04667 learning_rate: 0.00007 elapsed time: 14109.9 seconds (3.92 hours) Timestep: 3380000 mean reward (100 episodes): 132.2600 best mean reward: 132.2600 current episode reward: 87.0000 episodes: 5250 exploration: 0.04645 learning_rate: 0.00007 elapsed time: 14153.3 seconds (3.93 hours) Timestep: 3390000 mean reward (100 episodes): 134.8200 best mean reward: 135.0900 current episode reward: 135.0000 episodes: 5257 exploration: 0.04622 learning_rate: 0.00007 elapsed time: 14197.8 seconds (3.94 hours) Timestep: 3400000 mean reward (100 episodes): 138.5500 best mean reward: 138.5500 current episode reward: 279.0000 episodes: 5264 exploration: 0.04600 learning_rate: 0.00007 elapsed time: 14242.3 seconds (3.96 hours) Timestep: 3410000 mean reward (100 episodes): 140.9900 best mean reward: 141.5600 current episode reward: 260.0000 episodes: 5271 exploration: 0.04577 learning_rate: 0.00007 elapsed time: 14286.8 seconds (3.97 hours) Timestep: 3420000 mean reward (100 episodes): 138.1800 best mean reward: 141.5600 current episode reward: 106.0000 episodes: 5279 exploration: 0.04555 learning_rate: 0.00007 elapsed time: 14331.4 seconds (3.98 hours) Timestep: 3430000 mean reward (100 episodes): 138.7400 best mean reward: 141.5600 current episode reward: 140.0000 episodes: 5285 exploration: 0.04532 learning_rate: 0.00007 elapsed time: 14375.5 seconds (3.99 hours) Timestep: 3440000 mean reward (100 episodes): 148.6000 best mean reward: 148.6000 current episode reward: 96.0000 episodes: 5292 exploration: 0.04510 learning_rate: 0.00007 elapsed time: 14419.6 seconds (4.01 hours) Timestep: 3450000 mean reward (100 episodes): 150.7600 best mean reward: 152.1500 current episode reward: 20.0000 episodes: 5299 exploration: 0.04487 learning_rate: 0.00007 elapsed time: 14462.9 seconds (4.02 hours) Timestep: 3460000 mean reward (100 episodes): 146.0300 best mean reward: 152.1500 current episode reward: 97.0000 episodes: 5307 exploration: 0.04465 learning_rate: 0.00007 elapsed time: 14506.7 seconds (4.03 hours) Timestep: 3470000 mean reward (100 episodes): 146.0500 best mean reward: 152.1500 current episode reward: 203.0000 episodes: 5313 exploration: 0.04442 learning_rate: 0.00007 elapsed time: 14551.2 seconds (4.04 hours) Timestep: 3480000 mean reward (100 episodes): 142.6300 best mean reward: 152.1500 current episode reward: 100.0000 episodes: 5320 exploration: 0.04420 learning_rate: 0.00007 elapsed time: 14595.3 seconds (4.05 hours) Timestep: 3490000 mean reward (100 episodes): 139.7200 best mean reward: 152.1500 current episode reward: 92.0000 episodes: 5328 exploration: 0.04397 learning_rate: 0.00007 elapsed time: 14639.6 seconds (4.07 hours) Timestep: 3500000 mean reward (100 episodes): 139.6100 best mean reward: 152.1500 current episode reward: 113.0000 episodes: 5334 exploration: 0.04375 learning_rate: 0.00007 elapsed time: 14684.4 seconds (4.08 hours) Timestep: 3510000 mean reward (100 episodes): 134.0500 best mean reward: 152.1500 current episode reward: 76.0000 episodes: 5342 exploration: 0.04353 learning_rate: 0.00007 elapsed time: 14728.1 seconds (4.09 hours) Timestep: 3520000 mean reward (100 episodes): 132.4400 best mean reward: 152.1500 current episode reward: 139.0000 episodes: 5348 exploration: 0.04330 learning_rate: 0.00007 elapsed time: 14772.7 seconds (4.10 hours) Timestep: 3530000 mean reward (100 episodes): 130.8800 best mean reward: 152.1500 current episode reward: 175.0000 episodes: 5354 exploration: 0.04308 learning_rate: 0.00007 elapsed time: 14816.8 seconds (4.12 hours) Timestep: 3540000 mean reward (100 episodes): 128.9400 best mean reward: 152.1500 current episode reward: 109.0000 episodes: 5362 exploration: 0.04285 learning_rate: 0.00007 elapsed time: 14861.5 seconds (4.13 hours) Timestep: 3550000 mean reward (100 episodes): 126.3600 best mean reward: 152.1500 current episode reward: 20.0000 episodes: 5369 exploration: 0.04263 learning_rate: 0.00007 elapsed time: 14906.4 seconds (4.14 hours) Timestep: 3560000 mean reward (100 episodes): 120.9100 best mean reward: 152.1500 current episode reward: 75.0000 episodes: 5378 exploration: 0.04240 learning_rate: 0.00007 elapsed time: 14950.6 seconds (4.15 hours) Timestep: 3570000 mean reward (100 episodes): 112.8200 best mean reward: 152.1500 current episode reward: 23.0000 episodes: 5387 exploration: 0.04218 learning_rate: 0.00007 elapsed time: 14995.0 seconds (4.17 hours) Timestep: 3580000 mean reward (100 episodes): 103.1900 best mean reward: 152.1500 current episode reward: 105.0000 episodes: 5396 exploration: 0.04195 learning_rate: 0.00007 elapsed time: 15039.8 seconds (4.18 hours) Timestep: 3590000 mean reward (100 episodes): 103.8700 best mean reward: 152.1500 current episode reward: 34.0000 episodes: 5405 exploration: 0.04173 learning_rate: 0.00007 elapsed time: 15083.8 seconds (4.19 hours) Timestep: 3600000 mean reward (100 episodes): 99.5200 best mean reward: 152.1500 current episode reward: 112.0000 episodes: 5413 exploration: 0.04150 learning_rate: 0.00007 elapsed time: 15128.7 seconds (4.20 hours) Timestep: 3610000 mean reward (100 episodes): 98.6500 best mean reward: 152.1500 current episode reward: 79.0000 episodes: 5420 exploration: 0.04127 learning_rate: 0.00007 elapsed time: 15172.9 seconds (4.21 hours) Timestep: 3620000 mean reward (100 episodes): 95.2500 best mean reward: 152.1500 current episode reward: 39.0000 episodes: 5430 exploration: 0.04105 learning_rate: 0.00007 elapsed time: 15217.1 seconds (4.23 hours) Timestep: 3630000 mean reward (100 episodes): 80.0700 best mean reward: 152.1500 current episode reward: 50.0000 episodes: 5448 exploration: 0.04083 learning_rate: 0.00007 elapsed time: 15262.1 seconds (4.24 hours) Timestep: 3640000 mean reward (100 episodes): 73.5800 best mean reward: 152.1500 current episode reward: 47.0000 episodes: 5456 exploration: 0.04060 learning_rate: 0.00007 elapsed time: 15306.7 seconds (4.25 hours) Timestep: 3650000 mean reward (100 episodes): 66.4300 best mean reward: 152.1500 current episode reward: 34.0000 episodes: 5466 exploration: 0.04038 learning_rate: 0.00007 elapsed time: 15351.1 seconds (4.26 hours) Timestep: 3660000 mean reward (100 episodes): 56.1100 best mean reward: 152.1500 current episode reward: 26.0000 episodes: 5483 exploration: 0.04015 learning_rate: 0.00007 elapsed time: 15394.9 seconds (4.28 hours) Timestep: 3670000 mean reward (100 episodes): 42.6200 best mean reward: 152.1500 current episode reward: 14.0000 episodes: 5504 exploration: 0.03993 learning_rate: 0.00007 elapsed time: 15439.4 seconds (4.29 hours) Timestep: 3680000 mean reward (100 episodes): 32.2600 best mean reward: 152.1500 current episode reward: 5.0000 episodes: 5522 exploration: 0.03970 learning_rate: 0.00007 elapsed time: 15483.3 seconds (4.30 hours) Timestep: 3690000 mean reward (100 episodes): 23.3300 best mean reward: 152.1500 current episode reward: 10.0000 episodes: 5545 exploration: 0.03947 learning_rate: 0.00007 elapsed time: 15528.3 seconds (4.31 hours) Timestep: 3700000 mean reward (100 episodes): 10.5200 best mean reward: 152.1500 current episode reward: 8.0000 episodes: 5576 exploration: 0.03925 learning_rate: 0.00007 elapsed time: 15572.6 seconds (4.33 hours) Timestep: 3710000 mean reward (100 episodes): 8.5600 best mean reward: 152.1500 current episode reward: 10.0000 episodes: 5605 exploration: 0.03902 learning_rate: 0.00007 elapsed time: 15616.8 seconds (4.34 hours) Timestep: 3720000 mean reward (100 episodes): 6.0800 best mean reward: 152.1500 current episode reward: 8.0000 episodes: 5639 exploration: 0.03880 learning_rate: 0.00007 elapsed time: 15661.6 seconds (4.35 hours) Timestep: 3730000 mean reward (100 episodes): 4.6700 best mean reward: 152.1500 current episode reward: 3.0000 episodes: 5680 exploration: 0.03857 learning_rate: 0.00007 elapsed time: 15706.0 seconds (4.36 hours) Timestep: 3740000 mean reward (100 episodes): 3.2600 best mean reward: 152.1500 current episode reward: 2.0000 episodes: 5727 exploration: 0.03835 learning_rate: 0.00007 elapsed time: 15750.7 seconds (4.38 hours) Timestep: 3750000 mean reward (100 episodes): 2.4600 best mean reward: 152.1500 current episode reward: 4.0000 episodes: 5777 exploration: 0.03812 learning_rate: 0.00007 elapsed time: 15795.7 seconds (4.39 hours) Timestep: 3760000 mean reward (100 episodes): 2.0300 best mean reward: 152.1500 current episode reward: 3.0000 episodes: 5833 exploration: 0.03790 learning_rate: 0.00007 elapsed time: 15840.9 seconds (4.40 hours) Timestep: 3770000 mean reward (100 episodes): 1.8000 best mean reward: 152.1500 current episode reward: 0.0000 episodes: 5888 exploration: 0.03768 learning_rate: 0.00007 elapsed time: 15885.5 seconds (4.41 hours) Timestep: 3780000 mean reward (100 episodes): 1.6600 best mean reward: 152.1500 current episode reward: 4.0000 episodes: 5943 exploration: 0.03745 learning_rate: 0.00007 elapsed time: 15930.0 seconds (4.42 hours) Timestep: 3790000 mean reward (100 episodes): 1.6700 best mean reward: 152.1500 current episode reward: 3.0000 episodes: 6000 exploration: 0.03722 learning_rate: 0.00007 elapsed time: 15975.5 seconds (4.44 hours) Timestep: 3800000 mean reward (100 episodes): 1.4800 best mean reward: 152.1500 current episode reward: 1.0000 episodes: 6058 exploration: 0.03700 learning_rate: 0.00007 elapsed time: 16020.8 seconds (4.45 hours) Timestep: 3810000 mean reward (100 episodes): 1.8000 best mean reward: 152.1500 current episode reward: 0.0000 episodes: 6110 exploration: 0.03678 learning_rate: 0.00006 elapsed time: 16065.5 seconds (4.46 hours) Timestep: 3820000 mean reward (100 episodes): 1.7500 best mean reward: 152.1500 current episode reward: 1.0000 episodes: 6169 exploration: 0.03655 learning_rate: 0.00006 elapsed time: 16110.3 seconds (4.48 hours) Timestep: 3830000 mean reward (100 episodes): 1.5500 best mean reward: 152.1500 current episode reward: 1.0000 episodes: 6227 exploration: 0.03632 learning_rate: 0.00006 elapsed time: 16155.1 seconds (4.49 hours) Timestep: 3840000 mean reward (100 episodes): 1.8500 best mean reward: 152.1500 current episode reward: 2.0000 episodes: 6281 exploration: 0.03610 learning_rate: 0.00006 elapsed time: 16200.6 seconds (4.50 hours) Timestep: 3850000 mean reward (100 episodes): 1.9400 best mean reward: 152.1500 current episode reward: 4.0000 episodes: 6334 exploration: 0.03587 learning_rate: 0.00006 elapsed time: 16245.6 seconds (4.51 hours) Timestep: 3860000 mean reward (100 episodes): 1.9700 best mean reward: 152.1500 current episode reward: 1.0000 episodes: 6388 exploration: 0.03565 learning_rate: 0.00006 elapsed time: 16290.5 seconds (4.53 hours) Timestep: 3870000 mean reward (100 episodes): 1.8800 best mean reward: 152.1500 current episode reward: 5.0000 episodes: 6443 exploration: 0.03542 learning_rate: 0.00006 elapsed time: 16335.7 seconds (4.54 hours) Timestep: 3880000 mean reward (100 episodes): 1.6400 best mean reward: 152.1500 current episode reward: 1.0000 episodes: 6501 exploration: 0.03520 learning_rate: 0.00006 elapsed time: 16380.8 seconds (4.55 hours) Timestep: 3890000 mean reward (100 episodes): 1.5600 best mean reward: 152.1500 current episode reward: 0.0000 episodes: 6559 exploration: 0.03497 learning_rate: 0.00006 elapsed time: 16425.4 seconds (4.56 hours) Timestep: 3900000 mean reward (100 episodes): 1.6500 best mean reward: 152.1500 current episode reward: 0.0000 episodes: 6615 exploration: 0.03475 learning_rate: 0.00006 elapsed time: 16469.9 seconds (4.57 hours) Timestep: 3910000 mean reward (100 episodes): 1.6100 best mean reward: 152.1500 current episode reward: 2.0000 episodes: 6674 exploration: 0.03453 learning_rate: 0.00006 elapsed time: 16514.5 seconds (4.59 hours) Timestep: 3920000 mean reward (100 episodes): 1.1800 best mean reward: 152.1500 current episode reward: 1.0000 episodes: 6737 exploration: 0.03430 learning_rate: 0.00006 elapsed time: 16559.7 seconds (4.60 hours) Timestep: 3930000 mean reward (100 episodes): 1.2500 best mean reward: 152.1500 current episode reward: 0.0000 episodes: 6798 exploration: 0.03407 learning_rate: 0.00006 elapsed time: 16604.5 seconds (4.61 hours) Timestep: 3940000 mean reward (100 episodes): 1.5200 best mean reward: 152.1500 current episode reward: 2.0000 episodes: 6854 exploration: 0.03385 learning_rate: 0.00006 elapsed time: 16648.7 seconds (4.62 hours) Timestep: 3950000 mean reward (100 episodes): 2.0600 best mean reward: 152.1500 current episode reward: 1.0000 episodes: 6903 exploration: 0.03362 learning_rate: 0.00006 elapsed time: 16693.6 seconds (4.64 hours) Timestep: 3960000 mean reward (100 episodes): 2.2000 best mean reward: 152.1500 current episode reward: 3.0000 episodes: 6955 exploration: 0.03340 learning_rate: 0.00006 elapsed time: 16737.9 seconds (4.65 hours) Timestep: 3970000 mean reward (100 episodes): 1.9700 best mean reward: 152.1500 current episode reward: 1.0000 episodes: 7008 exploration: 0.03317 learning_rate: 0.00006 elapsed time: 16783.1 seconds (4.66 hours) Timestep: 3980000 mean reward (100 episodes): 1.9300 best mean reward: 152.1500 current episode reward: 0.0000 episodes: 7060 exploration: 0.03295 learning_rate: 0.00006 elapsed time: 16828.1 seconds (4.67 hours) Timestep: 3990000 mean reward (100 episodes): 2.1100 best mean reward: 152.1500 current episode reward: 3.0000 episodes: 7111 exploration: 0.03272 learning_rate: 0.00006 elapsed time: 16873.0 seconds (4.69 hours) Timestep: 4000000 mean reward (100 episodes): 2.2500 best mean reward: 152.1500 current episode reward: 3.0000 episodes: 7160 exploration: 0.03250 learning_rate: 0.00006 elapsed time: 16917.6 seconds (4.70 hours) Timestep: 4010000 mean reward (100 episodes): 2.3500 best mean reward: 152.1500 current episode reward: 4.0000 episodes: 7210 exploration: 0.03227 learning_rate: 0.00006 elapsed time: 16962.5 seconds (4.71 hours) Timestep: 4020000 mean reward (100 episodes): 2.1700 best mean reward: 152.1500 current episode reward: 7.0000 episodes: 7262 exploration: 0.03205 learning_rate: 0.00006 elapsed time: 17007.3 seconds (4.72 hours) Timestep: 4030000 mean reward (100 episodes): 2.2000 best mean reward: 152.1500 current episode reward: 4.0000 episodes: 7311 exploration: 0.03183 learning_rate: 0.00006 elapsed time: 17052.1 seconds (4.74 hours) Timestep: 4040000 mean reward (100 episodes): 2.1700 best mean reward: 152.1500 current episode reward: 4.0000 episodes: 7363 exploration: 0.03160 learning_rate: 0.00006 elapsed time: 17096.3 seconds (4.75 hours) Timestep: 4050000 mean reward (100 episodes): 1.9900 best mean reward: 152.1500 current episode reward: 0.0000 episodes: 7416 exploration: 0.03138 learning_rate: 0.00006 elapsed time: 17141.1 seconds (4.76 hours) Timestep: 4060000 mean reward (100 episodes): 2.0500 best mean reward: 152.1500 current episode reward: 0.0000 episodes: 7466 exploration: 0.03115 learning_rate: 0.00006 elapsed time: 17185.8 seconds (4.77 hours) Timestep: 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learning_rate: 0.00006 elapsed time: 17408.3 seconds (4.84 hours) Timestep: 4120000 mean reward (100 episodes): 3.4100 best mean reward: 152.1500 current episode reward: 4.0000 episodes: 7740 exploration: 0.02980 learning_rate: 0.00006 elapsed time: 17452.7 seconds (4.85 hours) Timestep: 4130000 mean reward (100 episodes): 4.0000 best mean reward: 152.1500 current episode reward: 5.0000 episodes: 7776 exploration: 0.02958 learning_rate: 0.00006 elapsed time: 17497.2 seconds (4.86 hours) Timestep: 4140000 mean reward (100 episodes): 4.3500 best mean reward: 152.1500 current episode reward: 10.0000 episodes: 7813 exploration: 0.02935 learning_rate: 0.00006 elapsed time: 17541.5 seconds (4.87 hours) Timestep: 4150000 mean reward (100 episodes): 4.5300 best mean reward: 152.1500 current episode reward: 3.0000 episodes: 7846 exploration: 0.02912 learning_rate: 0.00006 elapsed time: 17585.9 seconds (4.88 hours) Timestep: 4160000 mean reward (100 episodes): 4.7600 best mean reward: 152.1500 current episode reward: 3.0000 episodes: 7879 exploration: 0.02890 learning_rate: 0.00006 elapsed time: 17630.0 seconds (4.90 hours) Timestep: 4170000 mean reward (100 episodes): 4.9400 best mean reward: 152.1500 current episode reward: 3.0000 episodes: 7914 exploration: 0.02867 learning_rate: 0.00006 elapsed time: 17674.3 seconds (4.91 hours) Timestep: 4180000 mean reward (100 episodes): 5.0200 best mean reward: 152.1500 current episode reward: 5.0000 episodes: 7945 exploration: 0.02845 learning_rate: 0.00006 elapsed time: 17718.5 seconds (4.92 hours) Timestep: 4190000 mean reward (100 episodes): 5.0600 best mean reward: 152.1500 current episode reward: 2.0000 episodes: 7978 exploration: 0.02823 learning_rate: 0.00006 elapsed time: 17762.4 seconds (4.93 hours) Timestep: 4200000 mean reward (100 episodes): 5.4800 best mean reward: 152.1500 current episode reward: 4.0000 episodes: 8007 exploration: 0.02800 learning_rate: 0.00006 elapsed time: 17808.3 seconds (4.95 hours) Timestep: 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learning_rate: 0.00006 elapsed time: 18031.7 seconds (5.01 hours) Timestep: 4260000 mean reward (100 episodes): 8.9700 best mean reward: 152.1500 current episode reward: 6.0000 episodes: 8157 exploration: 0.02665 learning_rate: 0.00006 elapsed time: 18077.2 seconds (5.02 hours) Timestep: 4270000 mean reward (100 episodes): 9.3000 best mean reward: 152.1500 current episode reward: 10.0000 episodes: 8179 exploration: 0.02642 learning_rate: 0.00006 elapsed time: 18121.6 seconds (5.03 hours) Timestep: 4280000 mean reward (100 episodes): 9.8300 best mean reward: 152.1500 current episode reward: 15.0000 episodes: 8199 exploration: 0.02620 learning_rate: 0.00006 elapsed time: 18166.4 seconds (5.05 hours) Timestep: 4290000 mean reward (100 episodes): 10.2800 best mean reward: 152.1500 current episode reward: 11.0000 episodes: 8220 exploration: 0.02597 learning_rate: 0.00006 elapsed time: 18210.5 seconds (5.06 hours) Timestep: 4300000 mean reward (100 episodes): 10.4500 best mean reward: 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elapsed time: 20290.6 seconds (5.64 hours) Timestep: 4770000 mean reward (100 episodes): 46.1000 best mean reward: 152.1500 current episode reward: 64.0000 episodes: 8870 exploration: 0.01517 learning_rate: 0.00005 elapsed time: 20334.9 seconds (5.65 hours) Timestep: 4780000 mean reward (100 episodes): 46.3600 best mean reward: 152.1500 current episode reward: 50.0000 episodes: 8879 exploration: 0.01495 learning_rate: 0.00005 elapsed time: 20378.9 seconds (5.66 hours) Timestep: 4790000 mean reward (100 episodes): 47.5600 best mean reward: 152.1500 current episode reward: 75.0000 episodes: 8886 exploration: 0.01472 learning_rate: 0.00005 elapsed time: 20422.9 seconds (5.67 hours) Timestep: 4800000 mean reward (100 episodes): 48.5600 best mean reward: 152.1500 current episode reward: 88.0000 episodes: 8895 exploration: 0.01450 learning_rate: 0.00005 elapsed time: 20467.5 seconds (5.69 hours) Timestep: 4810000 mean reward (100 episodes): 50.1200 best mean reward: 152.1500 current episode 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hours) Timestep: 5140000 mean reward (100 episodes): 84.1300 best mean reward: 152.1500 current episode reward: 119.0000 episodes: 9152 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 21983.8 seconds (6.11 hours) Timestep: 5150000 mean reward (100 episodes): 84.8700 best mean reward: 152.1500 current episode reward: 61.0000 episodes: 9159 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22028.0 seconds (6.12 hours) Timestep: 5160000 mean reward (100 episodes): 85.6300 best mean reward: 152.1500 current episode reward: 88.0000 episodes: 9166 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22072.6 seconds (6.13 hours) Timestep: 5170000 mean reward (100 episodes): 82.3100 best mean reward: 152.1500 current episode reward: 115.0000 episodes: 9173 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22116.8 seconds (6.14 hours) Timestep: 5180000 mean reward (100 episodes): 84.7200 best mean reward: 152.1500 current episode reward: 90.0000 episodes: 9180 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reward: 128.0000 episodes: 9438 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23810.4 seconds (6.61 hours) Timestep: 5560000 mean reward (100 episodes): 131.6400 best mean reward: 152.1500 current episode reward: 147.0000 episodes: 9445 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23855.5 seconds (6.63 hours) Timestep: 5570000 mean reward (100 episodes): 130.1100 best mean reward: 152.1500 current episode reward: 74.0000 episodes: 9452 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23899.7 seconds (6.64 hours) Timestep: 5580000 mean reward (100 episodes): 134.6300 best mean reward: 152.1500 current episode reward: 101.0000 episodes: 9460 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23944.0 seconds (6.65 hours) Timestep: 5590000 mean reward (100 episodes): 136.4100 best mean reward: 152.1500 current episode reward: 97.0000 episodes: 9466 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23988.6 seconds (6.66 hours) Timestep: 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mean reward: 152.1500 current episode reward: 147.0000 episodes: 9535 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24433.9 seconds (6.79 hours) Timestep: 5700000 mean reward (100 episodes): 131.6600 best mean reward: 152.1500 current episode reward: 165.0000 episodes: 9542 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24478.6 seconds (6.80 hours) Timestep: 5710000 mean reward (100 episodes): 132.1300 best mean reward: 152.1500 current episode reward: 186.0000 episodes: 9548 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24524.1 seconds (6.81 hours) Timestep: 5720000 mean reward (100 episodes): 137.0500 best mean reward: 152.1500 current episode reward: 168.0000 episodes: 9555 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24568.8 seconds (6.82 hours) Timestep: 5730000 mean reward (100 episodes): 136.9100 best mean reward: 152.1500 current episode reward: 100.0000 episodes: 9561 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 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reward: 197.0000 episodes: 9595 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24836.5 seconds (6.90 hours) Timestep: 5790000 mean reward (100 episodes): 144.0600 best mean reward: 152.1500 current episode reward: 269.0000 episodes: 9601 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24881.8 seconds (6.91 hours) Timestep: 5800000 mean reward (100 episodes): 146.9800 best mean reward: 152.1500 current episode reward: 176.0000 episodes: 9608 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24927.3 seconds (6.92 hours) Timestep: 5810000 mean reward (100 episodes): 147.8400 best mean reward: 152.1500 current episode reward: 136.0000 episodes: 9615 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24972.0 seconds (6.94 hours) Timestep: 5820000 mean reward (100 episodes): 146.2800 best mean reward: 152.1500 current episode reward: 246.0000 episodes: 9621 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25016.7 seconds (6.95 hours) Timestep: 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mean reward: 160.7700 current episode reward: 204.0000 episodes: 9689 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25462.7 seconds (7.07 hours) Timestep: 5930000 mean reward (100 episodes): 159.7300 best mean reward: 160.7700 current episode reward: 267.0000 episodes: 9695 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25506.9 seconds (7.09 hours) Timestep: 5940000 mean reward (100 episodes): 159.9900 best mean reward: 160.7700 current episode reward: 305.0000 episodes: 9701 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25552.0 seconds (7.10 hours) Timestep: 5950000 mean reward (100 episodes): 168.5300 best mean reward: 168.5300 current episode reward: 219.0000 episodes: 9707 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25597.0 seconds (7.11 hours) Timestep: 5960000 mean reward (100 episodes): 176.2600 best mean reward: 176.2600 current episode reward: 300.0000 episodes: 9713 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25641.3 seconds (7.12 hours) Timestep: 5970000 mean reward (100 episodes): 175.6700 best mean reward: 178.6700 current episode reward: 303.0000 episodes: 9721 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25685.5 seconds (7.13 hours) Timestep: 5980000 mean reward (100 episodes): 172.9900 best mean reward: 178.6700 current episode reward: 101.0000 episodes: 9728 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25729.7 seconds (7.15 hours) Timestep: 5990000 mean reward (100 episodes): 174.6800 best mean reward: 178.6700 current episode reward: 312.0000 episodes: 9734 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25774.3 seconds (7.16 hours) Timestep: 6000000 mean reward (100 episodes): 178.1700 best mean reward: 178.6700 current episode reward: 255.0000 episodes: 9741 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25818.6 seconds (7.17 hours) Timestep: 6010000 mean reward (100 episodes): 178.2500 best mean reward: 180.6000 current episode reward: 152.0000 episodes: 9747 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25863.5 seconds (7.18 hours) Timestep: 6020000 mean reward (100 episodes): 183.9600 best mean reward: 184.3200 current episode reward: 190.0000 episodes: 9753 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25908.0 seconds (7.20 hours) Timestep: 6030000 mean reward (100 episodes): 184.0500 best mean reward: 186.7000 current episode reward: 184.0000 episodes: 9760 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25952.7 seconds (7.21 hours) Timestep: 6040000 mean reward (100 episodes): 186.5000 best mean reward: 187.4800 current episode reward: 128.0000 episodes: 9766 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25997.7 seconds (7.22 hours) Timestep: 6050000 mean reward (100 episodes): 191.9300 best mean reward: 193.5900 current episode reward: 105.0000 episodes: 9773 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26043.2 seconds (7.23 hours) Timestep: 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best mean reward: 219.9400 current episode reward: 177.0000 episodes: 9837 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26490.5 seconds (7.36 hours) Timestep: 6160000 mean reward (100 episodes): 216.1500 best mean reward: 219.9400 current episode reward: 192.0000 episodes: 9844 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26534.2 seconds (7.37 hours) Timestep: 6170000 mean reward (100 episodes): 219.8800 best mean reward: 220.0300 current episode reward: 236.0000 episodes: 9850 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26578.6 seconds (7.38 hours) Timestep: 6180000 mean reward (100 episodes): 217.4700 best mean reward: 220.0300 current episode reward: 68.0000 episodes: 9856 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26623.1 seconds (7.40 hours) Timestep: 6190000 mean reward (100 episodes): 223.9700 best mean reward: 223.9700 current episode reward: 328.0000 episodes: 9862 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 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best mean reward: 256.4000 current episode reward: 339.0000 episodes: 9972 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27517.0 seconds (7.64 hours) Timestep: 6390000 mean reward (100 episodes): 255.8100 best mean reward: 256.4000 current episode reward: 255.0000 episodes: 9978 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27561.8 seconds (7.66 hours) Timestep: 6400000 mean reward (100 episodes): 262.1000 best mean reward: 262.1000 current episode reward: 336.0000 episodes: 9983 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27605.9 seconds (7.67 hours) Timestep: 6410000 mean reward (100 episodes): 264.2100 best mean reward: 264.2100 current episode reward: 327.0000 episodes: 9988 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27649.4 seconds (7.68 hours) Timestep: 6420000 mean reward (100 episodes): 267.2900 best mean reward: 267.2900 current episode reward: 241.0000 episodes: 9994 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 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learning_rate: 0.00005 elapsed time: 28724.5 seconds (7.98 hours) Timestep: 6660000 mean reward (100 episodes): 287.3900 best mean reward: 288.2900 current episode reward: 198.0000 episodes: 10128 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28768.4 seconds (7.99 hours) Timestep: 6670000 mean reward (100 episodes): 287.6600 best mean reward: 288.2900 current episode reward: 269.0000 episodes: 10134 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28813.1 seconds (8.00 hours) Timestep: 6680000 mean reward (100 episodes): 287.4200 best mean reward: 288.2900 current episode reward: 331.0000 episodes: 10139 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28857.2 seconds (8.02 hours) Timestep: 6690000 mean reward (100 episodes): 290.3200 best mean reward: 290.3200 current episode reward: 404.0000 episodes: 10145 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28902.2 seconds (8.03 hours) Timestep: 6700000 mean reward (100 episodes): 289.9000 best 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reward: 300.0000 episodes: 10201 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29345.5 seconds (8.15 hours) Timestep: 6800000 mean reward (100 episodes): 295.6300 best mean reward: 297.2000 current episode reward: 377.0000 episodes: 10206 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29390.0 seconds (8.16 hours) Timestep: 6810000 mean reward (100 episodes): 297.9700 best mean reward: 297.9700 current episode reward: 380.0000 episodes: 10212 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29434.5 seconds (8.18 hours) Timestep: 6820000 mean reward (100 episodes): 298.4600 best mean reward: 298.7900 current episode reward: 292.0000 episodes: 10218 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29478.6 seconds (8.19 hours) Timestep: 6830000 mean reward (100 episodes): 304.1100 best mean reward: 304.1100 current episode reward: 393.0000 episodes: 10222 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29523.3 seconds (8.20 hours) Timestep: 6840000 mean reward (100 episodes): 300.4900 best mean reward: 304.1100 current episode reward: 205.0000 episodes: 10229 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29567.6 seconds (8.21 hours) Timestep: 6850000 mean reward (100 episodes): 299.9500 best mean reward: 304.1100 current episode reward: 222.0000 episodes: 10234 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29612.0 seconds (8.23 hours) Timestep: 6860000 mean reward (100 episodes): 301.1400 best mean reward: 304.1100 current episode reward: 380.0000 episodes: 10239 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29656.1 seconds (8.24 hours) Timestep: 6870000 mean reward (100 episodes): 306.1700 best mean reward: 306.1700 current episode reward: 374.0000 episodes: 10244 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29700.3 seconds (8.25 hours) Timestep: 6880000 mean reward (100 episodes): 305.4200 best mean reward: 306.1700 current episode reward: 355.0000 episodes: 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learning_rate: 0.00005 elapsed time: 30146.1 seconds (8.37 hours) Timestep: 6980000 mean reward (100 episodes): 312.1100 best mean reward: 312.8500 current episode reward: 419.0000 episodes: 10301 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30189.8 seconds (8.39 hours) Timestep: 6990000 mean reward (100 episodes): 309.6200 best mean reward: 312.8500 current episode reward: 196.0000 episodes: 10306 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30234.4 seconds (8.40 hours) Timestep: 7000000 mean reward (100 episodes): 308.5900 best mean reward: 312.8500 current episode reward: 306.0000 episodes: 10312 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30279.0 seconds (8.41 hours) Timestep: 7010000 mean reward (100 episodes): 309.9800 best mean reward: 312.8500 current episode reward: 252.0000 episodes: 10316 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30323.4 seconds (8.42 hours) Timestep: 7020000 mean reward (100 episodes): 302.5700 best mean reward: 312.8500 current episode reward: 1.0000 episodes: 10323 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30367.7 seconds (8.44 hours) Timestep: 7030000 mean reward (100 episodes): 306.3100 best mean reward: 312.8500 current episode reward: 412.0000 episodes: 10328 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30412.2 seconds (8.45 hours) Timestep: 7040000 mean reward (100 episodes): 311.4700 best mean reward: 312.8500 current episode reward: 374.0000 episodes: 10333 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30456.5 seconds (8.46 hours) Timestep: 7050000 mean reward (100 episodes): 315.2500 best mean reward: 315.2500 current episode reward: 411.0000 episodes: 10337 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30501.6 seconds (8.47 hours) Timestep: 7060000 mean reward (100 episodes): 317.4300 best mean reward: 317.4300 current episode reward: 405.0000 episodes: 10340 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 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reward: 386.0000 episodes: 10365 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30767.4 seconds (8.55 hours) Timestep: 7120000 mean reward (100 episodes): 328.1100 best mean reward: 328.8700 current episode reward: 345.0000 episodes: 10370 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30812.4 seconds (8.56 hours) Timestep: 7130000 mean reward (100 episodes): 331.1800 best mean reward: 331.1800 current episode reward: 334.0000 episodes: 10374 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30856.3 seconds (8.57 hours) Timestep: 7140000 mean reward (100 episodes): 333.4600 best mean reward: 333.4600 current episode reward: 373.0000 episodes: 10379 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30900.7 seconds (8.58 hours) Timestep: 7150000 mean reward (100 episodes): 334.3200 best mean reward: 334.3200 current episode reward: 406.0000 episodes: 10384 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30945.3 seconds (8.60 hours) 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learning_rate: 0.00005 elapsed time: 31566.5 seconds (8.77 hours) Timestep: 7300000 mean reward (100 episodes): 363.6800 best mean reward: 363.6800 current episode reward: 389.0000 episodes: 10449 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31611.3 seconds (8.78 hours) Timestep: 7310000 mean reward (100 episodes): 364.4600 best mean reward: 364.4600 current episode reward: 427.0000 episodes: 10454 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31656.0 seconds (8.79 hours) Timestep: 7320000 mean reward (100 episodes): 363.0300 best mean reward: 364.4800 current episode reward: 404.0000 episodes: 10458 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31700.3 seconds (8.81 hours) Timestep: 7330000 mean reward (100 episodes): 364.7700 best mean reward: 364.9300 current episode reward: 389.0000 episodes: 10463 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31744.5 seconds (8.82 hours) Timestep: 7340000 mean reward (100 episodes): 366.6600 best mean reward: 366.6600 current episode reward: 421.0000 episodes: 10467 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31788.4 seconds (8.83 hours) Timestep: 7350000 mean reward (100 episodes): 365.0900 best mean reward: 366.6600 current episode reward: 375.0000 episodes: 10471 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31832.4 seconds (8.84 hours) Timestep: 7360000 mean reward (100 episodes): 365.8900 best mean reward: 366.6600 current episode reward: 407.0000 episodes: 10474 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31877.1 seconds (8.85 hours) Timestep: 7370000 mean reward (100 episodes): 366.4400 best mean reward: 366.6600 current episode reward: 427.0000 episodes: 10478 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31921.6 seconds (8.87 hours) Timestep: 7380000 mean reward (100 episodes): 368.3700 best mean reward: 368.3700 current episode reward: 413.0000 episodes: 10481 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 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Timestep: 8940000 mean reward (100 episodes): 379.9300 best mean reward: 387.6300 current episode reward: 399.0000 episodes: 11093 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38916.4 seconds (10.81 hours) Timestep: 8950000 mean reward (100 episodes): 381.4200 best mean reward: 387.6300 current episode reward: 406.0000 episodes: 11095 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38960.3 seconds (10.82 hours) Timestep: 8960000 mean reward (100 episodes): 383.2000 best mean reward: 387.6300 current episode reward: 367.0000 episodes: 11099 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39004.6 seconds (10.83 hours) Timestep: 8970000 mean reward (100 episodes): 382.8000 best mean reward: 387.6300 current episode reward: 427.0000 episodes: 11102 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39049.8 seconds (10.85 hours) Timestep: 8980000 mean reward (100 episodes): 380.7000 best mean reward: 387.6300 current episode reward: 409.0000 episodes: 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(100 episodes): 381.6100 best mean reward: 387.6300 current episode reward: 371.0000 episodes: 11122 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39316.4 seconds (10.92 hours) Timestep: 9040000 mean reward (100 episodes): 383.3800 best mean reward: 387.6300 current episode reward: 419.0000 episodes: 11125 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39361.3 seconds (10.93 hours) Timestep: 9050000 mean reward (100 episodes): 384.5000 best mean reward: 387.6300 current episode reward: 399.0000 episodes: 11129 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39405.9 seconds (10.95 hours) Timestep: 9060000 mean reward (100 episodes): 382.4800 best mean reward: 387.6300 current episode reward: 420.0000 episodes: 11133 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39450.6 seconds (10.96 hours) Timestep: 9070000 mean reward (100 episodes): 381.4500 best mean reward: 387.6300 current episode reward: 411.0000 episodes: 11137 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39494.7 seconds (10.97 hours) Timestep: 9080000 mean reward (100 episodes): 381.7400 best mean reward: 387.6300 current episode reward: 431.0000 episodes: 11141 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39539.4 seconds (10.98 hours) Timestep: 9090000 mean reward (100 episodes): 384.1500 best mean reward: 387.6300 current episode reward: 384.0000 episodes: 11143 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39583.8 seconds (11.00 hours) Timestep: 9100000 mean reward (100 episodes): 382.7000 best mean reward: 387.6300 current episode reward: 294.0000 episodes: 11149 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39628.6 seconds (11.01 hours) Timestep: 9110000 mean reward (100 episodes): 386.8300 best mean reward: 387.6300 current episode reward: 293.0000 episodes: 11152 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39672.5 seconds (11.02 hours) Timestep: 9120000 mean reward (100 episodes): 385.7800 best mean reward: 388.4400 current episode reward: 162.0000 episodes: 11155 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39716.3 seconds (11.03 hours) Timestep: 9130000 mean reward (100 episodes): 382.1700 best mean reward: 388.4400 current episode reward: 379.0000 episodes: 11158 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39761.3 seconds (11.04 hours) Timestep: 9140000 mean reward (100 episodes): 383.1600 best mean reward: 388.4400 current episode reward: 417.0000 episodes: 11160 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39805.9 seconds (11.06 hours) Timestep: 9150000 mean reward (100 episodes): 383.8800 best mean reward: 388.4400 current episode reward: 412.0000 episodes: 11164 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39851.0 seconds (11.07 hours) Timestep: 9160000 mean reward (100 episodes): 385.1000 best mean reward: 388.4400 current episode reward: 320.0000 episodes: 11167 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39895.8 seconds (11.08 hours) Timestep: 9170000 mean reward (100 episodes): 384.7100 best mean reward: 388.4400 current episode reward: 303.0000 episodes: 11171 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39940.3 seconds (11.09 hours) Timestep: 9180000 mean reward (100 episodes): 381.7300 best mean reward: 388.4400 current episode reward: 417.0000 episodes: 11175 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39984.5 seconds (11.11 hours) Timestep: 9190000 mean reward (100 episodes): 383.1700 best mean reward: 388.4400 current episode reward: 432.0000 episodes: 11178 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40029.4 seconds (11.12 hours) Timestep: 9200000 mean reward (100 episodes): 381.7900 best mean reward: 388.4400 current episode reward: 260.0000 episodes: 11182 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40073.9 seconds (11.13 hours) Timestep: 9210000 mean reward (100 episodes): 381.5100 best mean reward: 388.4400 current episode reward: 412.0000 episodes: 11185 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40118.7 seconds (11.14 hours) Timestep: 9220000 mean reward (100 episodes): 378.3200 best mean reward: 388.4400 current episode reward: 301.0000 episodes: 11188 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40163.4 seconds (11.16 hours) Timestep: 9230000 mean reward (100 episodes): 375.4700 best mean reward: 388.4400 current episode reward: 387.0000 episodes: 11193 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40208.5 seconds (11.17 hours) Timestep: 9240000 mean reward (100 episodes): 375.2100 best mean reward: 388.4400 current episode reward: 417.0000 episodes: 11195 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40253.3 seconds (11.18 hours) Timestep: 9250000 mean reward (100 episodes): 373.3800 best mean reward: 388.4400 current episode reward: 393.0000 episodes: 11198 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40298.1 seconds (11.19 hours) Timestep: 9260000 mean reward (100 episodes): 373.2700 best mean reward: 388.4400 current episode reward: 381.0000 episodes: 11202 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40342.5 seconds (11.21 hours) Timestep: 9270000 mean reward (100 episodes): 371.1300 best mean reward: 388.4400 current episode reward: 342.0000 episodes: 11207 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40387.1 seconds (11.22 hours) Timestep: 9280000 mean reward (100 episodes): 372.3600 best mean reward: 388.4400 current episode reward: 421.0000 episodes: 11211 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40431.7 seconds (11.23 hours) Timestep: 9290000 mean reward (100 episodes): 372.6800 best mean reward: 388.4400 current episode reward: 399.0000 episodes: 11215 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40476.0 seconds (11.24 hours) Timestep: 9300000 mean reward (100 episodes): 371.9000 best mean reward: 388.4400 current episode reward: 374.0000 episodes: 11218 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40520.6 seconds (11.26 hours) Timestep: 9310000 mean reward (100 episodes): 369.9800 best mean reward: 388.4400 current episode reward: 383.0000 episodes: 11222 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40564.4 seconds (11.27 hours) Timestep: 9320000 mean reward (100 episodes): 368.5400 best mean reward: 388.4400 current episode reward: 382.0000 episodes: 11226 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40609.5 seconds (11.28 hours) Timestep: 9330000 mean reward (100 episodes): 370.6500 best mean reward: 388.4400 current episode reward: 341.0000 episodes: 11231 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40653.9 seconds (11.29 hours) Timestep: 9340000 mean reward (100 episodes): 370.6700 best mean reward: 388.4400 current episode reward: 403.0000 episodes: 11233 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40698.8 seconds (11.31 hours) Timestep: 9350000 mean reward (100 episodes): 373.1900 best mean reward: 388.4400 current episode reward: 399.0000 episodes: 11236 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40742.8 seconds (11.32 hours) Timestep: 9360000 mean reward (100 episodes): 367.0400 best mean reward: 388.4400 current episode reward: 84.0000 episodes: 11242 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40788.4 seconds (11.33 hours) Timestep: 9370000 mean reward (100 episodes): 367.6200 best mean reward: 388.4400 current episode reward: 422.0000 episodes: 11246 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40833.3 seconds (11.34 hours) Timestep: 9380000 mean reward (100 episodes): 372.7200 best mean reward: 388.4400 current episode reward: 413.0000 episodes: 11249 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40877.9 seconds (11.35 hours) Timestep: 9390000 mean reward (100 episodes): 369.4800 best mean reward: 388.4400 current episode reward: 408.0000 episodes: 11254 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40923.3 seconds (11.37 hours) Timestep: 9400000 mean reward (100 episodes): 374.6900 best mean reward: 388.4400 current episode reward: 406.0000 episodes: 11258 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40968.9 seconds (11.38 hours) Timestep: 9410000 mean reward (100 episodes): 373.8400 best mean reward: 388.4400 current episode reward: 423.0000 episodes: 11262 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41013.6 seconds (11.39 hours) Timestep: 9420000 mean reward (100 episodes): 374.3000 best mean reward: 388.4400 current episode reward: 425.0000 episodes: 11265 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41057.7 seconds (11.40 hours) Timestep: 9430000 mean reward (100 episodes): 372.7300 best mean reward: 388.4400 current episode reward: 224.0000 episodes: 11268 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41102.1 seconds (11.42 hours) Timestep: 9440000 mean reward (100 episodes): 372.6600 best mean reward: 388.4400 current episode reward: 406.0000 episodes: 11272 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41146.4 seconds (11.43 hours) Timestep: 9450000 mean reward (100 episodes): 373.4700 best mean reward: 388.4400 current episode reward: 411.0000 episodes: 11274 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41189.8 seconds (11.44 hours) Timestep: 9460000 mean reward (100 episodes): 372.1000 best mean reward: 388.4400 current episode reward: 406.0000 episodes: 11278 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41234.5 seconds (11.45 hours) Timestep: 9470000 mean reward (100 episodes): 372.6800 best mean reward: 388.4400 current episode reward: 232.0000 episodes: 11282 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41278.4 seconds (11.47 hours) Timestep: 9480000 mean reward (100 episodes): 372.3000 best mean reward: 388.4400 current episode reward: 382.0000 episodes: 11285 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41322.8 seconds (11.48 hours) Timestep: 9490000 mean reward (100 episodes): 375.1200 best mean reward: 388.4400 current episode reward: 346.0000 episodes: 11288 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41367.6 seconds (11.49 hours) Timestep: 9500000 mean reward (100 episodes): 377.9900 best mean reward: 388.4400 current episode reward: 421.0000 episodes: 11291 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41412.0 seconds (11.50 hours) Timestep: 9510000 mean reward (100 episodes): 378.9500 best mean reward: 388.4400 current episode reward: 410.0000 episodes: 11294 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41456.1 seconds (11.52 hours) Timestep: 9520000 mean reward (100 episodes): 377.5800 best mean reward: 388.4400 current episode reward: 393.0000 episodes: 11299 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41500.7 seconds (11.53 hours) Timestep: 9530000 mean reward (100 episodes): 378.0900 best mean reward: 388.4400 current episode reward: 404.0000 episodes: 11302 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41545.3 seconds (11.54 hours) Timestep: 9540000 mean reward (100 episodes): 379.0600 best mean reward: 388.4400 current episode reward: 433.0000 episodes: 11304 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41589.5 seconds (11.55 hours) Timestep: 9550000 mean reward (100 episodes): 380.7800 best mean reward: 388.4400 current episode reward: 246.0000 episodes: 11308 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41634.4 seconds (11.57 hours) Timestep: 9560000 mean reward (100 episodes): 379.5600 best mean reward: 388.4400 current episode reward: 334.0000 episodes: 11312 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41679.0 seconds (11.58 hours) Timestep: 9570000 mean reward (100 episodes): 375.7000 best mean reward: 388.4400 current episode reward: 410.0000 episodes: 11316 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41723.4 seconds (11.59 hours) Timestep: 9580000 mean reward (100 episodes): 375.3700 best mean reward: 388.4400 current episode reward: 401.0000 episodes: 11319 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41768.2 seconds (11.60 hours) Timestep: 9590000 mean reward (100 episodes): 377.5300 best mean reward: 388.4400 current episode reward: 354.0000 episodes: 11323 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41812.9 seconds (11.61 hours) Timestep: 9600000 mean reward (100 episodes): 379.3700 best mean reward: 388.4400 current episode reward: 408.0000 episodes: 11325 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41856.8 seconds (11.63 hours) Timestep: 9610000 mean reward (100 episodes): 379.5900 best mean reward: 388.4400 current episode reward: 424.0000 episodes: 11329 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41901.0 seconds (11.64 hours) Timestep: 9620000 mean reward (100 episodes): 378.7900 best mean reward: 388.4400 current episode reward: 346.0000 episodes: 11332 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41945.0 seconds (11.65 hours) Timestep: 9630000 mean reward (100 episodes): 374.1400 best mean reward: 388.4400 current episode reward: 113.0000 episodes: 11336 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41990.6 seconds (11.66 hours) Timestep: 9640000 mean reward (100 episodes): 376.0700 best mean reward: 388.4400 current episode reward: 424.0000 episodes: 11340 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42034.9 seconds (11.68 hours) Timestep: 9650000 mean reward (100 episodes): 379.8300 best mean reward: 388.4400 current episode reward: 413.0000 episodes: 11343 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42079.6 seconds (11.69 hours) Timestep: 9660000 mean reward (100 episodes): 378.7500 best mean reward: 388.4400 current episode reward: 350.0000 episodes: 11347 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42124.2 seconds (11.70 hours) Timestep: 9670000 mean reward (100 episodes): 379.2200 best mean reward: 388.4400 current episode reward: 423.0000 episodes: 11350 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42168.7 seconds (11.71 hours) Timestep: 9680000 mean reward (100 episodes): 378.4500 best mean reward: 388.4400 current episode reward: 180.0000 episodes: 11354 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42212.7 seconds (11.73 hours) Timestep: 9684338 mean reward (100 episodes): 378.3600 best mean reward: 388.4400 current episode reward: 343.0000 episodes: 11356 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42232.3 seconds (11.73 hours) ================================================ FILE: dqn/logs_text/Enduro_s001.text ================================================ ('AVAILABLE GPUS: ', [u'device: 0, name: TITAN X (Pascal), pci bus id: 0000:01:00.0']) task = Task Timestep: 60000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 18 exploration: 0.94600 learning_rate: 0.00010 elapsed time: 100.9 seconds (0.03 hours) Timestep: 70000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 21 exploration: 0.93700 learning_rate: 0.00010 elapsed time: 134.7 seconds (0.04 hours) Timestep: 80000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 24 exploration: 0.92800 learning_rate: 0.00010 elapsed time: 168.5 seconds (0.05 hours) Timestep: 90000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 27 exploration: 0.91900 learning_rate: 0.00010 elapsed time: 203.3 seconds (0.06 hours) Timestep: 100000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 30 exploration: 0.91000 learning_rate: 0.00010 elapsed time: 240.6 seconds (0.07 hours) Timestep: 110000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 33 exploration: 0.90100 learning_rate: 0.00010 elapsed time: 275.1 seconds (0.08 hours) Timestep: 120000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 36 exploration: 0.89200 learning_rate: 0.00010 elapsed time: 309.8 seconds (0.09 hours) Timestep: 130000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 39 exploration: 0.88300 learning_rate: 0.00010 elapsed time: 344.6 seconds (0.10 hours) Timestep: 140000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 42 exploration: 0.87400 learning_rate: 0.00010 elapsed time: 379.4 seconds (0.11 hours) Timestep: 150000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 45 exploration: 0.86500 learning_rate: 0.00010 elapsed time: 414.3 seconds (0.12 hours) Timestep: 160000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 48 exploration: 0.85600 learning_rate: 0.00010 elapsed time: 449.3 seconds (0.12 hours) Timestep: 170000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 51 exploration: 0.84700 learning_rate: 0.00010 elapsed time: 484.5 seconds (0.13 hours) Timestep: 180000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 54 exploration: 0.83800 learning_rate: 0.00010 elapsed time: 519.9 seconds (0.14 hours) Timestep: 190000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 57 exploration: 0.82900 learning_rate: 0.00010 elapsed time: 555.4 seconds (0.15 hours) Timestep: 200000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 60 exploration: 0.82000 learning_rate: 0.00010 elapsed time: 591.0 seconds (0.16 hours) Timestep: 210000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 63 exploration: 0.81100 learning_rate: 0.00010 elapsed time: 626.9 seconds (0.17 hours) Timestep: 220000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 66 exploration: 0.80200 learning_rate: 0.00010 elapsed time: 665.6 seconds (0.18 hours) Timestep: 230000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 69 exploration: 0.79300 learning_rate: 0.00010 elapsed time: 701.5 seconds (0.19 hours) Timestep: 240000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 72 exploration: 0.78400 learning_rate: 0.00010 elapsed time: 737.6 seconds (0.20 hours) Timestep: 250000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 75 exploration: 0.77500 learning_rate: 0.00010 elapsed time: 773.9 seconds (0.21 hours) Timestep: 260000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 78 exploration: 0.76600 learning_rate: 0.00010 elapsed time: 810.8 seconds (0.23 hours) Timestep: 270000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 81 exploration: 0.75700 learning_rate: 0.00010 elapsed time: 847.4 seconds (0.24 hours) Timestep: 280000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 84 exploration: 0.74800 learning_rate: 0.00010 elapsed time: 883.8 seconds (0.25 hours) Timestep: 290000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 87 exploration: 0.73900 learning_rate: 0.00010 elapsed time: 921.0 seconds (0.26 hours) Timestep: 300000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 90 exploration: 0.73000 learning_rate: 0.00010 elapsed time: 958.3 seconds (0.27 hours) Timestep: 310000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 93 exploration: 0.72100 learning_rate: 0.00010 elapsed time: 995.8 seconds (0.28 hours) Timestep: 320000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 96 exploration: 0.71200 learning_rate: 0.00010 elapsed time: 1033.7 seconds (0.29 hours) Timestep: 330000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 99 exploration: 0.70300 learning_rate: 0.00010 elapsed time: 1071.6 seconds (0.30 hours) Timestep: 340000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 102 exploration: 0.69400 learning_rate: 0.00010 elapsed time: 1109.5 seconds (0.31 hours) Timestep: 350000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 105 exploration: 0.68500 learning_rate: 0.00010 elapsed time: 1147.4 seconds (0.32 hours) Timestep: 360000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 108 exploration: 0.67600 learning_rate: 0.00010 elapsed time: 1185.4 seconds (0.33 hours) Timestep: 370000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 111 exploration: 0.66700 learning_rate: 0.00010 elapsed time: 1223.8 seconds (0.34 hours) Timestep: 380000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 114 exploration: 0.65800 learning_rate: 0.00010 elapsed time: 1261.3 seconds (0.35 hours) Timestep: 390000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 117 exploration: 0.64900 learning_rate: 0.00010 elapsed time: 1299.5 seconds (0.36 hours) Timestep: 400000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 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reward: 0.0100 current episode reward: 0.0000 episodes: 135 exploration: 0.59500 learning_rate: 0.00010 elapsed time: 1534.7 seconds (0.43 hours) Timestep: 460000 mean reward (100 episodes): 0.0100 best mean reward: 0.0100 current episode reward: 0.0000 episodes: 138 exploration: 0.58600 learning_rate: 0.00010 elapsed time: 1574.2 seconds (0.44 hours) Timestep: 470000 mean reward (100 episodes): 0.0100 best mean reward: 0.0100 current episode reward: 0.0000 episodes: 141 exploration: 0.57700 learning_rate: 0.00010 elapsed time: 1613.7 seconds (0.45 hours) Timestep: 480000 mean reward (100 episodes): 0.0100 best mean reward: 0.0100 current episode reward: 0.0000 episodes: 144 exploration: 0.56800 learning_rate: 0.00010 elapsed time: 1653.0 seconds (0.46 hours) Timestep: 490000 mean reward (100 episodes): 0.0100 best mean reward: 0.0100 current episode reward: 0.0000 episodes: 147 exploration: 0.55900 learning_rate: 0.00010 elapsed time: 1693.6 seconds (0.47 hours) Timestep: 500000 mean 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reward: 0.2900 current episode reward: 0.0000 episodes: 1252 exploration: 0.02890 learning_rate: 0.00006 elapsed time: 18396.2 seconds (5.11 hours) Timestep: 4170000 mean reward (100 episodes): 0.2800 best mean reward: 0.2900 current episode reward: 4.0000 episodes: 1255 exploration: 0.02867 learning_rate: 0.00006 elapsed time: 18442.6 seconds (5.12 hours) Timestep: 4180000 mean reward (100 episodes): 0.2500 best mean reward: 0.2900 current episode reward: 0.0000 episodes: 1258 exploration: 0.02845 learning_rate: 0.00006 elapsed time: 18488.9 seconds (5.14 hours) Timestep: 4190000 mean reward (100 episodes): 0.2500 best mean reward: 0.2900 current episode reward: 0.0000 episodes: 1261 exploration: 0.02823 learning_rate: 0.00006 elapsed time: 18535.9 seconds (5.15 hours) Timestep: 4200000 mean reward (100 episodes): 0.2400 best mean reward: 0.2900 current episode reward: 0.0000 episodes: 1264 exploration: 0.02800 learning_rate: 0.00006 elapsed time: 18582.5 seconds (5.16 hours) Timestep: 4210000 mean reward (100 episodes): 0.2300 best mean reward: 0.2900 current episode reward: 0.0000 episodes: 1267 exploration: 0.02777 learning_rate: 0.00006 elapsed time: 18627.9 seconds (5.17 hours) Timestep: 4220000 mean reward (100 episodes): 0.2200 best mean reward: 0.2900 current episode reward: 0.0000 episodes: 1270 exploration: 0.02755 learning_rate: 0.00006 elapsed time: 18673.6 seconds (5.19 hours) Timestep: 4230000 mean reward (100 episodes): 0.2300 best mean reward: 0.2900 current episode reward: 0.0000 episodes: 1273 exploration: 0.02733 learning_rate: 0.00006 elapsed time: 18719.6 seconds (5.20 hours) Timestep: 4240000 mean reward (100 episodes): 0.2700 best mean reward: 0.2900 current episode reward: 4.0000 episodes: 1276 exploration: 0.02710 learning_rate: 0.00006 elapsed time: 18766.0 seconds (5.21 hours) Timestep: 4250000 mean reward (100 episodes): 0.2600 best mean reward: 0.2900 current episode reward: 0.0000 episodes: 1279 exploration: 0.02687 learning_rate: 0.00006 elapsed time: 18813.0 seconds (5.23 hours) Timestep: 4260000 mean reward (100 episodes): 0.2800 best mean reward: 0.2900 current episode reward: 0.0000 episodes: 1282 exploration: 0.02665 learning_rate: 0.00006 elapsed time: 18859.5 seconds (5.24 hours) Timestep: 4270000 mean reward (100 episodes): 0.2600 best mean reward: 0.2900 current episode reward: 0.0000 episodes: 1285 exploration: 0.02642 learning_rate: 0.00006 elapsed time: 18905.7 seconds (5.25 hours) Timestep: 4280000 mean reward (100 episodes): 0.3000 best mean reward: 0.3000 current episode reward: 3.0000 episodes: 1288 exploration: 0.02620 learning_rate: 0.00006 elapsed time: 18952.0 seconds (5.26 hours) Timestep: 4290000 mean reward (100 episodes): 0.5500 best mean reward: 0.5500 current episode reward: 3.0000 episodes: 1291 exploration: 0.02597 learning_rate: 0.00006 elapsed time: 18997.5 seconds (5.28 hours) Timestep: 4300000 mean reward (100 episodes): 0.5300 best mean reward: 0.5500 current episode reward: 0.0000 episodes: 1294 exploration: 0.02575 learning_rate: 0.00006 elapsed time: 19043.5 seconds (5.29 hours) Timestep: 4310000 mean reward (100 episodes): 0.5300 best mean reward: 0.5500 current episode reward: 0.0000 episodes: 1297 exploration: 0.02552 learning_rate: 0.00006 elapsed time: 19089.5 seconds (5.30 hours) Timestep: 4320000 mean reward (100 episodes): 0.5400 best mean reward: 0.5500 current episode reward: 0.0000 episodes: 1300 exploration: 0.02530 learning_rate: 0.00006 elapsed time: 19136.3 seconds (5.32 hours) Timestep: 4330000 mean reward (100 episodes): 0.5400 best mean reward: 0.5500 current episode reward: 0.0000 episodes: 1303 exploration: 0.02508 learning_rate: 0.00006 elapsed time: 19181.9 seconds (5.33 hours) Timestep: 4340000 mean reward (100 episodes): 0.6200 best mean reward: 0.6200 current episode reward: 3.0000 episodes: 1306 exploration: 0.02485 learning_rate: 0.00006 elapsed time: 19228.3 seconds (5.34 hours) Timestep: 4350000 mean reward (100 episodes): 0.6500 best mean reward: 0.6500 current episode reward: 3.0000 episodes: 1309 exploration: 0.02462 learning_rate: 0.00006 elapsed time: 19275.8 seconds (5.35 hours) Timestep: 4360000 mean reward (100 episodes): 0.6400 best mean reward: 0.6500 current episode reward: 1.0000 episodes: 1312 exploration: 0.02440 learning_rate: 0.00006 elapsed time: 19322.1 seconds (5.37 hours) Timestep: 4370000 mean reward (100 episodes): 0.6700 best mean reward: 0.6700 current episode reward: 0.0000 episodes: 1315 exploration: 0.02417 learning_rate: 0.00006 elapsed time: 19368.0 seconds (5.38 hours) Timestep: 4380000 mean reward (100 episodes): 1.0000 best mean reward: 1.0000 current episode reward: 21.0000 episodes: 1318 exploration: 0.02395 learning_rate: 0.00006 elapsed time: 19414.2 seconds (5.39 hours) Timestep: 4390000 mean reward (100 episodes): 1.1200 best mean reward: 1.1200 current episode reward: 0.0000 episodes: 1321 exploration: 0.02372 learning_rate: 0.00006 elapsed time: 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exploration: 0.02260 learning_rate: 0.00006 elapsed time: 19694.3 seconds (5.47 hours) Timestep: 4450000 mean reward (100 episodes): 1.3500 best mean reward: 1.3500 current episode reward: 0.0000 episodes: 1339 exploration: 0.02237 learning_rate: 0.00006 elapsed time: 19740.7 seconds (5.48 hours) Timestep: 4460000 mean reward (100 episodes): 1.3400 best mean reward: 1.3500 current episode reward: 1.0000 episodes: 1342 exploration: 0.02215 learning_rate: 0.00006 elapsed time: 19787.0 seconds (5.50 hours) Timestep: 4470000 mean reward (100 episodes): 1.3800 best mean reward: 1.3800 current episode reward: 1.0000 episodes: 1345 exploration: 0.02192 learning_rate: 0.00006 elapsed time: 19833.2 seconds (5.51 hours) Timestep: 4480000 mean reward (100 episodes): 1.4400 best mean reward: 1.4400 current episode reward: 1.0000 episodes: 1348 exploration: 0.02170 learning_rate: 0.00006 elapsed time: 19879.1 seconds (5.52 hours) Timestep: 4490000 mean reward (100 episodes): 1.6300 best mean reward: 1.6300 current episode reward: 3.0000 episodes: 1351 exploration: 0.02147 learning_rate: 0.00006 elapsed time: 19926.5 seconds (5.54 hours) Timestep: 4500000 mean reward (100 episodes): 1.6600 best mean reward: 1.6600 current episode reward: 0.0000 episodes: 1354 exploration: 0.02125 learning_rate: 0.00006 elapsed time: 19973.2 seconds (5.55 hours) Timestep: 4510000 mean reward (100 episodes): 1.6400 best mean reward: 1.6600 current episode reward: 1.0000 episodes: 1357 exploration: 0.02103 learning_rate: 0.00006 elapsed time: 20019.8 seconds (5.56 hours) Timestep: 4520000 mean reward (100 episodes): 1.7300 best mean reward: 1.7300 current episode reward: 7.0000 episodes: 1360 exploration: 0.02080 learning_rate: 0.00006 elapsed time: 20066.3 seconds (5.57 hours) Timestep: 4530000 mean reward (100 episodes): 1.9200 best mean reward: 1.9200 current episode reward: 0.0000 episodes: 1363 exploration: 0.02057 learning_rate: 0.00006 elapsed time: 20112.7 seconds (5.59 hours) Timestep: 4540000 mean reward (100 episodes): 1.9500 best mean reward: 1.9500 current episode reward: 2.0000 episodes: 1366 exploration: 0.02035 learning_rate: 0.00006 elapsed time: 20158.6 seconds (5.60 hours) Timestep: 4550000 mean reward (100 episodes): 2.2200 best mean reward: 2.2200 current episode reward: 9.0000 episodes: 1369 exploration: 0.02013 learning_rate: 0.00006 elapsed time: 20204.4 seconds (5.61 hours) Timestep: 4560000 mean reward (100 episodes): 2.2500 best mean reward: 2.2500 current episode reward: 3.0000 episodes: 1372 exploration: 0.01990 learning_rate: 0.00006 elapsed time: 20250.2 seconds (5.63 hours) Timestep: 4570000 mean reward (100 episodes): 2.2700 best mean reward: 2.2700 current episode reward: 0.0000 episodes: 1375 exploration: 0.01967 learning_rate: 0.00006 elapsed time: 20296.1 seconds (5.64 hours) Timestep: 4580000 mean reward (100 episodes): 2.3900 best mean reward: 2.3900 current episode reward: 0.0000 episodes: 1378 exploration: 0.01945 learning_rate: 0.00006 elapsed time: 20343.0 seconds (5.65 hours) Timestep: 4590000 mean reward (100 episodes): 2.3900 best mean reward: 2.4000 current episode reward: 1.0000 episodes: 1381 exploration: 0.01923 learning_rate: 0.00006 elapsed time: 20390.0 seconds (5.66 hours) Timestep: 4600000 mean reward (100 episodes): 2.4500 best mean reward: 2.4500 current episode reward: 0.0000 episodes: 1384 exploration: 0.01900 learning_rate: 0.00006 elapsed time: 20436.6 seconds (5.68 hours) Timestep: 4610000 mean reward (100 episodes): 2.4300 best mean reward: 2.4500 current episode reward: 0.0000 episodes: 1387 exploration: 0.01878 learning_rate: 0.00005 elapsed time: 20482.6 seconds (5.69 hours) Timestep: 4620000 mean reward (100 episodes): 2.2100 best mean reward: 2.4500 current episode reward: 1.0000 episodes: 1390 exploration: 0.01855 learning_rate: 0.00005 elapsed time: 20528.9 seconds (5.70 hours) Timestep: 4630000 mean reward (100 episodes): 2.2100 best mean reward: 2.4500 current episode reward: 3.0000 episodes: 1393 exploration: 0.01832 learning_rate: 0.00005 elapsed time: 20574.5 seconds (5.72 hours) Timestep: 4640000 mean reward (100 episodes): 2.2900 best mean reward: 2.4500 current episode reward: 1.0000 episodes: 1396 exploration: 0.01810 learning_rate: 0.00005 elapsed time: 20621.5 seconds (5.73 hours) Timestep: 4650000 mean reward (100 episodes): 2.2900 best mean reward: 2.4500 current episode reward: 0.0000 episodes: 1399 exploration: 0.01788 learning_rate: 0.00005 elapsed time: 20667.3 seconds (5.74 hours) Timestep: 4660000 mean reward (100 episodes): 2.3000 best mean reward: 2.4500 current episode reward: 0.0000 episodes: 1402 exploration: 0.01765 learning_rate: 0.00005 elapsed time: 20713.3 seconds (5.75 hours) Timestep: 4670000 mean reward (100 episodes): 2.2500 best mean reward: 2.4500 current episode reward: 0.0000 episodes: 1405 exploration: 0.01742 learning_rate: 0.00005 elapsed time: 20760.0 seconds (5.77 hours) Timestep: 4680000 mean reward 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exploration: 0.01517 learning_rate: 0.00005 elapsed time: 21222.4 seconds (5.90 hours) Timestep: 4780000 mean reward (100 episodes): 2.5700 best mean reward: 2.5700 current episode reward: 5.0000 episodes: 1438 exploration: 0.01495 learning_rate: 0.00005 elapsed time: 21269.1 seconds (5.91 hours) Timestep: 4790000 mean reward (100 episodes): 2.8000 best mean reward: 2.8000 current episode reward: 23.0000 episodes: 1441 exploration: 0.01472 learning_rate: 0.00005 elapsed time: 21315.0 seconds (5.92 hours) Timestep: 4800000 mean reward (100 episodes): 2.8800 best mean reward: 2.8800 current episode reward: 12.0000 episodes: 1444 exploration: 0.01450 learning_rate: 0.00005 elapsed time: 21361.9 seconds (5.93 hours) Timestep: 4810000 mean reward (100 episodes): 2.9600 best mean reward: 2.9600 current episode reward: 1.0000 episodes: 1447 exploration: 0.01427 learning_rate: 0.00005 elapsed time: 21409.0 seconds (5.95 hours) Timestep: 4820000 mean reward (100 episodes): 2.8000 best mean reward: 2.9600 current episode reward: 0.0000 episodes: 1450 exploration: 0.01405 learning_rate: 0.00005 elapsed time: 21455.5 seconds (5.96 hours) Timestep: 4830000 mean reward (100 episodes): 3.0200 best mean reward: 3.0200 current episode reward: 0.0000 episodes: 1453 exploration: 0.01382 learning_rate: 0.00005 elapsed time: 21502.5 seconds (5.97 hours) Timestep: 4840000 mean reward (100 episodes): 3.5000 best mean reward: 3.5100 current episode reward: 0.0000 episodes: 1456 exploration: 0.01360 learning_rate: 0.00005 elapsed time: 21549.4 seconds (5.99 hours) Timestep: 4850000 mean reward (100 episodes): 3.9400 best mean reward: 3.9400 current episode reward: 15.0000 episodes: 1459 exploration: 0.01337 learning_rate: 0.00005 elapsed time: 21596.7 seconds (6.00 hours) Timestep: 4860000 mean reward (100 episodes): 3.7700 best mean reward: 3.9600 current episode reward: 0.0000 episodes: 1462 exploration: 0.01315 learning_rate: 0.00005 elapsed time: 21643.2 seconds (6.01 hours) Timestep: 4870000 mean reward (100 episodes): 3.8500 best mean reward: 3.9600 current episode reward: 0.0000 episodes: 1465 exploration: 0.01292 learning_rate: 0.00005 elapsed time: 21689.6 seconds (6.02 hours) Timestep: 4880000 mean reward (100 episodes): 3.6900 best mean reward: 3.9600 current episode reward: 2.0000 episodes: 1468 exploration: 0.01270 learning_rate: 0.00005 elapsed time: 21734.9 seconds (6.04 hours) Timestep: 4890000 mean reward (100 episodes): 3.8400 best mean reward: 3.9600 current episode reward: 17.0000 episodes: 1471 exploration: 0.01247 learning_rate: 0.00005 elapsed time: 21780.3 seconds (6.05 hours) Timestep: 4900000 mean reward (100 episodes): 3.8300 best mean reward: 3.9600 current episode reward: 0.0000 episodes: 1474 exploration: 0.01225 learning_rate: 0.00005 elapsed time: 21825.8 seconds (6.06 hours) Timestep: 4910000 mean reward (100 episodes): 3.8800 best mean reward: 3.9600 current episode reward: 8.0000 episodes: 1477 exploration: 0.01202 learning_rate: 0.00005 elapsed time: 21871.4 seconds (6.08 hours) Timestep: 4920000 mean reward (100 episodes): 3.9000 best mean reward: 3.9600 current episode reward: 0.0000 episodes: 1480 exploration: 0.01180 learning_rate: 0.00005 elapsed time: 21916.8 seconds (6.09 hours) Timestep: 4930000 mean reward (100 episodes): 3.8700 best mean reward: 3.9600 current episode reward: 4.0000 episodes: 1483 exploration: 0.01157 learning_rate: 0.00005 elapsed time: 21963.6 seconds (6.10 hours) Timestep: 4940000 mean reward (100 episodes): 3.9100 best mean reward: 3.9600 current episode reward: 0.0000 episodes: 1486 exploration: 0.01135 learning_rate: 0.00005 elapsed time: 22009.7 seconds (6.11 hours) Timestep: 4950000 mean reward (100 episodes): 4.2700 best mean reward: 4.2700 current episode reward: 0.0000 episodes: 1489 exploration: 0.01112 learning_rate: 0.00005 elapsed time: 22056.4 seconds (6.13 hours) Timestep: 4960000 mean reward (100 episodes): 4.7500 best mean reward: 4.7500 current episode reward: 9.0000 episodes: 1492 exploration: 0.01090 learning_rate: 0.00005 elapsed time: 22103.2 seconds (6.14 hours) Timestep: 4970000 mean reward (100 episodes): 5.1300 best mean reward: 5.1300 current episode reward: 13.0000 episodes: 1495 exploration: 0.01067 learning_rate: 0.00005 elapsed time: 22150.1 seconds (6.15 hours) Timestep: 4980000 mean reward (100 episodes): 5.4800 best mean reward: 5.4800 current episode reward: 0.0000 episodes: 1498 exploration: 0.01045 learning_rate: 0.00005 elapsed time: 22196.1 seconds (6.17 hours) Timestep: 4990000 mean reward (100 episodes): 5.6200 best mean reward: 5.6200 current episode reward: 13.0000 episodes: 1501 exploration: 0.01022 learning_rate: 0.00005 elapsed time: 22242.2 seconds (6.18 hours) Timestep: 5000000 mean reward (100 episodes): 6.6600 best mean reward: 6.6600 current episode reward: 67.0000 episodes: 1504 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22288.5 seconds (6.19 hours) Timestep: 5010000 mean reward (100 episodes): 7.6600 best mean reward: 7.6600 current episode reward: 28.0000 episodes: 1507 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22335.4 seconds (6.20 hours) Timestep: 5020000 mean reward (100 episodes): 8.4400 best mean reward: 8.4400 current episode reward: 33.0000 episodes: 1510 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22382.4 seconds (6.22 hours) Timestep: 5030000 mean reward (100 episodes): 8.7100 best mean reward: 8.7100 current episode reward: 0.0000 episodes: 1513 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22428.8 seconds (6.23 hours) Timestep: 5040000 mean reward (100 episodes): 9.5200 best mean reward: 9.5200 current episode reward: 2.0000 episodes: 1516 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22475.5 seconds (6.24 hours) Timestep: 5050000 mean reward (100 episodes): 10.4400 best mean reward: 10.4400 current episode reward: 59.0000 episodes: 1519 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22522.3 seconds (6.26 hours) Timestep: 5060000 mean reward (100 episodes): 11.3500 best mean reward: 11.3500 current episode reward: 13.0000 episodes: 1522 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22568.4 seconds (6.27 hours) Timestep: 5070000 mean reward (100 episodes): 11.8200 best mean reward: 11.8200 current episode reward: 15.0000 episodes: 1525 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22614.8 seconds (6.28 hours) Timestep: 5080000 mean reward (100 episodes): 13.1100 best mean reward: 13.1100 current episode reward: 40.0000 episodes: 1528 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22661.8 seconds (6.29 hours) Timestep: 5090000 mean reward (100 episodes): 13.6900 best mean reward: 13.6900 current episode reward: 34.0000 episodes: 1531 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22708.6 seconds (6.31 hours) Timestep: 5100000 mean reward (100 episodes): 14.8800 best mean reward: 14.8800 current episode reward: 72.0000 episodes: 1534 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22755.4 seconds (6.32 hours) Timestep: 5110000 mean reward (100 episodes): 16.0800 best mean reward: 16.0800 current episode reward: 42.0000 episodes: 1537 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22802.2 seconds (6.33 hours) Timestep: 5120000 mean reward (100 episodes): 17.3600 best mean reward: 17.3600 current episode reward: 87.0000 episodes: 1540 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22849.3 seconds (6.35 hours) Timestep: 5130000 mean reward (100 episodes): 19.1600 best mean reward: 19.1600 current episode reward: 63.0000 episodes: 1543 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22896.4 seconds (6.36 hours) Timestep: 5140000 mean reward (100 episodes): 19.8000 best mean reward: 19.8000 current episode reward: 30.0000 episodes: 1547 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22942.6 seconds (6.37 hours) Timestep: 5150000 mean reward (100 episodes): 22.2400 best mean reward: 22.2400 current episode reward: 77.0000 episodes: 1550 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22989.7 seconds (6.39 hours) Timestep: 5160000 mean reward (100 episodes): 23.6500 best mean reward: 23.6500 current episode reward: 44.0000 episodes: 1553 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23035.4 seconds (6.40 hours) Timestep: 5170000 mean reward (100 episodes): 25.3600 best mean reward: 25.3600 current episode reward: 51.0000 episodes: 1556 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23081.6 seconds (6.41 hours) Timestep: 5180000 mean reward (100 episodes): 26.8100 best mean reward: 26.8100 current episode reward: 115.0000 episodes: 1559 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23128.7 seconds (6.42 hours) Timestep: 5190000 mean reward (100 episodes): 28.0500 best mean reward: 28.0500 current episode reward: 33.0000 episodes: 1562 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23174.7 seconds (6.44 hours) Timestep: 5200000 mean reward (100 episodes): 29.8700 best mean reward: 29.8700 current episode reward: 27.0000 episodes: 1565 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23221.2 seconds (6.45 hours) Timestep: 5210000 mean reward (100 episodes): 31.9400 best mean reward: 31.9400 current episode reward: 84.0000 episodes: 1568 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23268.1 seconds (6.46 hours) Timestep: 5220000 mean reward (100 episodes): 33.5500 best mean reward: 33.5500 current episode reward: 76.0000 episodes: 1571 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23315.6 seconds (6.48 hours) Timestep: 5230000 mean reward (100 episodes): 35.7200 best mean reward: 35.7200 current episode reward: 75.0000 episodes: 1574 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23362.8 seconds (6.49 hours) Timestep: 5240000 mean reward (100 episodes): 36.6300 best mean reward: 36.6300 current episode reward: 30.0000 episodes: 1577 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23409.5 seconds (6.50 hours) Timestep: 5250000 mean reward (100 episodes): 38.5000 best mean reward: 38.5000 current episode reward: 93.0000 episodes: 1580 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23456.7 seconds (6.52 hours) Timestep: 5260000 mean reward (100 episodes): 39.7000 best mean reward: 39.7000 current episode reward: 48.0000 episodes: 1583 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23503.2 seconds (6.53 hours) Timestep: 5270000 mean reward (100 episodes): 40.7900 best mean reward: 40.7900 current episode reward: 51.0000 episodes: 1586 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23549.9 seconds (6.54 hours) Timestep: 5280000 mean reward (100 episodes): 42.5700 best mean reward: 42.5700 current episode reward: 66.0000 episodes: 1589 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23596.9 seconds (6.55 hours) Timestep: 5290000 mean reward (100 episodes): 44.2100 best mean reward: 44.2100 current episode reward: 82.0000 episodes: 1592 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23644.0 seconds (6.57 hours) Timestep: 5300000 mean reward (100 episodes): 46.0200 best mean reward: 46.0200 current episode reward: 111.0000 episodes: 1595 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23691.2 seconds (6.58 hours) Timestep: 5310000 mean reward (100 episodes): 47.1700 best mean reward: 47.1700 current episode reward: 39.0000 episodes: 1598 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23737.6 seconds (6.59 hours) Timestep: 5320000 mean reward (100 episodes): 49.6700 best mean reward: 49.6700 current episode reward: 74.0000 episodes: 1601 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23784.2 seconds (6.61 hours) Timestep: 5330000 mean reward (100 episodes): 50.8400 best mean reward: 50.8400 current episode reward: 106.0000 episodes: 1604 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23830.3 seconds (6.62 hours) Timestep: 5340000 mean reward (100 episodes): 52.5100 best mean reward: 52.5100 current episode reward: 80.0000 episodes: 1607 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23877.0 seconds (6.63 hours) Timestep: 5350000 mean reward (100 episodes): 53.8000 best mean reward: 53.8000 current episode reward: 68.0000 episodes: 1610 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23923.2 seconds (6.65 hours) Timestep: 5360000 mean reward (100 episodes): 55.7100 best mean reward: 55.7100 current episode reward: 47.0000 episodes: 1613 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 23969.7 seconds (6.66 hours) Timestep: 5370000 mean reward (100 episodes): 57.1100 best mean reward: 57.1100 current episode reward: 48.0000 episodes: 1616 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24017.0 seconds (6.67 hours) Timestep: 5380000 mean reward (100 episodes): 59.0800 best mean reward: 59.0800 current episode reward: 108.0000 episodes: 1619 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24064.7 seconds (6.68 hours) Timestep: 5390000 mean reward (100 episodes): 60.7600 best mean reward: 60.7600 current episode reward: 103.0000 episodes: 1622 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24111.1 seconds (6.70 hours) Timestep: 5400000 mean reward (100 episodes): 61.6100 best mean reward: 61.6100 current episode reward: 58.0000 episodes: 1625 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24156.6 seconds (6.71 hours) Timestep: 5410000 mean reward (100 episodes): 62.6700 best mean reward: 62.6700 current episode reward: 90.0000 episodes: 1628 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24203.2 seconds (6.72 hours) Timestep: 5420000 mean reward (100 episodes): 64.8800 best mean reward: 64.8800 current episode reward: 93.0000 episodes: 1631 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24250.6 seconds (6.74 hours) Timestep: 5430000 mean reward (100 episodes): 65.2200 best mean reward: 65.2500 current episode reward: 92.0000 episodes: 1634 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24296.9 seconds (6.75 hours) Timestep: 5440000 mean reward (100 episodes): 64.5100 best mean reward: 65.3700 current episode reward: 2.0000 episodes: 1637 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24342.9 seconds (6.76 hours) Timestep: 5450000 mean reward (100 episodes): 64.7700 best mean reward: 65.3700 current episode reward: 102.0000 episodes: 1640 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24389.4 seconds (6.77 hours) Timestep: 5460000 mean reward (100 episodes): 64.5000 best mean reward: 65.3700 current episode reward: 28.0000 episodes: 1643 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24436.6 seconds (6.79 hours) Timestep: 5470000 mean reward (100 episodes): 65.3200 best mean reward: 65.3700 current episode reward: 29.0000 episodes: 1646 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24483.0 seconds (6.80 hours) Timestep: 5480000 mean reward (100 episodes): 66.2600 best mean reward: 66.2600 current episode reward: 93.0000 episodes: 1649 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24529.0 seconds (6.81 hours) Timestep: 5490000 mean reward (100 episodes): 65.8300 best mean reward: 66.2600 current episode reward: 83.0000 episodes: 1652 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24575.0 seconds (6.83 hours) Timestep: 5500000 mean reward (100 episodes): 65.9900 best mean reward: 66.2600 current episode reward: 72.0000 episodes: 1655 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24621.2 seconds (6.84 hours) Timestep: 5510000 mean reward (100 episodes): 67.0000 best mean reward: 67.0000 current episode reward: 132.0000 episodes: 1658 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24667.6 seconds (6.85 hours) Timestep: 5520000 mean reward (100 episodes): 67.1100 best mean reward: 67.1100 current episode reward: 94.0000 episodes: 1661 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24714.3 seconds (6.87 hours) Timestep: 5530000 mean reward (100 episodes): 67.8800 best mean reward: 67.8800 current episode reward: 111.0000 episodes: 1664 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24761.0 seconds (6.88 hours) Timestep: 5540000 mean reward (100 episodes): 68.2000 best mean reward: 68.2800 current episode reward: 51.0000 episodes: 1667 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24807.5 seconds (6.89 hours) Timestep: 5550000 mean reward (100 episodes): 68.3300 best mean reward: 68.3300 current episode reward: 91.0000 episodes: 1670 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24854.1 seconds (6.90 hours) Timestep: 5560000 mean reward (100 episodes): 69.7800 best mean reward: 69.7800 current episode reward: 105.0000 episodes: 1673 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24900.8 seconds (6.92 hours) Timestep: 5570000 mean reward (100 episodes): 71.0700 best mean reward: 71.0700 current episode reward: 81.0000 episodes: 1676 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24947.0 seconds (6.93 hours) Timestep: 5580000 mean reward (100 episodes): 72.5600 best mean reward: 72.5600 current episode reward: 73.0000 episodes: 1679 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24993.4 seconds (6.94 hours) Timestep: 5590000 mean reward (100 episodes): 73.4700 best mean reward: 73.4700 current episode reward: 50.0000 episodes: 1682 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25039.1 seconds (6.96 hours) Timestep: 5600000 mean reward (100 episodes): 74.8500 best mean reward: 74.8500 current episode reward: 88.0000 episodes: 1685 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25084.9 seconds (6.97 hours) Timestep: 5610000 mean reward (100 episodes): 76.3400 best mean reward: 76.3400 current episode reward: 115.0000 episodes: 1688 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25131.1 seconds (6.98 hours) Timestep: 5620000 mean reward (100 episodes): 77.5000 best mean reward: 77.5000 current episode reward: 105.0000 episodes: 1691 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25177.6 seconds (6.99 hours) Timestep: 5630000 mean reward (100 episodes): 78.6100 best mean reward: 78.6100 current episode reward: 111.0000 episodes: 1694 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25224.3 seconds (7.01 hours) Timestep: 5640000 mean reward (100 episodes): 79.0600 best mean reward: 79.0600 current episode reward: 103.0000 episodes: 1697 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25270.8 seconds (7.02 hours) Timestep: 5650000 mean reward (100 episodes): 79.3900 best mean reward: 79.8400 current episode reward: 104.0000 episodes: 1700 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25316.5 seconds (7.03 hours) Timestep: 5660000 mean reward (100 episodes): 80.8400 best mean reward: 80.8400 current episode reward: 116.0000 episodes: 1703 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25363.2 seconds (7.05 hours) Timestep: 5670000 mean reward (100 episodes): 79.9500 best mean reward: 80.8400 current episode reward: 41.0000 episodes: 1706 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25408.8 seconds (7.06 hours) Timestep: 5680000 mean reward (100 episodes): 81.4600 best mean reward: 81.4600 current episode reward: 97.0000 episodes: 1709 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25455.4 seconds (7.07 hours) Timestep: 5690000 mean reward (100 episodes): 81.9300 best mean reward: 82.0100 current episode reward: 94.0000 episodes: 1712 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25501.7 seconds (7.08 hours) Timestep: 5700000 mean reward (100 episodes): 81.9800 best mean reward: 82.4200 current episode reward: 30.0000 episodes: 1715 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25547.1 seconds (7.10 hours) Timestep: 5710000 mean reward (100 episodes): 82.5100 best mean reward: 82.5500 current episode reward: 116.0000 episodes: 1718 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25593.5 seconds (7.11 hours) Timestep: 5720000 mean reward (100 episodes): 82.2100 best mean reward: 82.5500 current episode reward: 126.0000 episodes: 1721 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25640.7 seconds (7.12 hours) Timestep: 5730000 mean reward (100 episodes): 82.0700 best mean reward: 82.5500 current episode reward: 8.0000 episodes: 1724 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25687.3 seconds (7.14 hours) Timestep: 5740000 mean reward (100 episodes): 82.1900 best mean reward: 82.5500 current episode reward: 100.0000 episodes: 1727 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25733.7 seconds (7.15 hours) Timestep: 5750000 mean reward (100 episodes): 81.8300 best mean reward: 82.5500 current episode reward: 98.0000 episodes: 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episodes: 1772 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26432.2 seconds (7.34 hours) Timestep: 5900000 mean reward (100 episodes): 90.5300 best mean reward: 91.0700 current episode reward: 52.0000 episodes: 1775 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26479.0 seconds (7.36 hours) Timestep: 5910000 mean reward (100 episodes): 91.5300 best mean reward: 91.5300 current episode reward: 140.0000 episodes: 1778 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26524.7 seconds (7.37 hours) Timestep: 5920000 mean reward (100 episodes): 91.9200 best mean reward: 92.2800 current episode reward: 128.0000 episodes: 1781 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26570.7 seconds (7.38 hours) Timestep: 5930000 mean reward (100 episodes): 93.6200 best mean reward: 93.6200 current episode reward: 116.0000 episodes: 1784 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26617.5 seconds (7.39 hours) Timestep: 5940000 mean reward (100 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elapsed time: 26849.9 seconds (7.46 hours) Timestep: 5990000 mean reward (100 episodes): 97.0400 best mean reward: 97.0600 current episode reward: 109.0000 episodes: 1802 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26896.0 seconds (7.47 hours) Timestep: 6000000 mean reward (100 episodes): 96.9700 best mean reward: 97.0600 current episode reward: 108.0000 episodes: 1805 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26942.8 seconds (7.48 hours) Timestep: 6010000 mean reward (100 episodes): 96.4700 best mean reward: 97.7200 current episode reward: 49.0000 episodes: 1808 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 26989.2 seconds (7.50 hours) Timestep: 6020000 mean reward (100 episodes): 94.7600 best mean reward: 97.7200 current episode reward: 39.0000 episodes: 1811 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27035.7 seconds (7.51 hours) Timestep: 6030000 mean reward (100 episodes): 94.8600 best mean reward: 97.7200 current episode reward: 129.0000 episodes: 1814 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27082.7 seconds (7.52 hours) Timestep: 6040000 mean reward (100 episodes): 95.0300 best mean reward: 97.7200 current episode reward: 2.0000 episodes: 1817 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27129.5 seconds (7.54 hours) Timestep: 6050000 mean reward (100 episodes): 95.6000 best mean reward: 97.7200 current episode reward: 158.0000 episodes: 1820 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27176.0 seconds (7.55 hours) Timestep: 6060000 mean reward (100 episodes): 95.4100 best mean reward: 97.7200 current episode reward: 135.0000 episodes: 1823 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27222.9 seconds (7.56 hours) Timestep: 6070000 mean reward (100 episodes): 97.2300 best mean reward: 97.7200 current episode reward: 103.0000 episodes: 1826 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27268.5 seconds (7.57 hours) Timestep: 6080000 mean 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episode reward: 111.0000 episodes: 1857 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27732.1 seconds (7.70 hours) Timestep: 6180000 mean reward (100 episodes): 102.4600 best mean reward: 102.4600 current episode reward: 129.0000 episodes: 1860 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27778.9 seconds (7.72 hours) Timestep: 6190000 mean reward (100 episodes): 103.0100 best mean reward: 103.0100 current episode reward: 130.0000 episodes: 1863 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27826.2 seconds (7.73 hours) Timestep: 6200000 mean reward (100 episodes): 104.0400 best mean reward: 104.0400 current episode reward: 160.0000 episodes: 1866 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27872.5 seconds (7.74 hours) Timestep: 6210000 mean reward (100 episodes): 103.5400 best mean reward: 104.1600 current episode reward: 99.0000 episodes: 1869 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27919.3 seconds (7.76 hours) Timestep: 6220000 mean reward (100 episodes): 103.8200 best mean reward: 104.1600 current episode reward: 96.0000 episodes: 1872 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 27965.9 seconds (7.77 hours) Timestep: 6230000 mean reward (100 episodes): 100.9800 best mean reward: 104.1600 current episode reward: 7.0000 episodes: 1875 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28012.5 seconds (7.78 hours) Timestep: 6240000 mean reward (100 episodes): 99.4100 best mean reward: 104.1600 current episode reward: 104.0000 episodes: 1878 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28058.6 seconds (7.79 hours) Timestep: 6250000 mean reward (100 episodes): 99.5400 best mean reward: 104.1600 current episode reward: 131.0000 episodes: 1881 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28105.0 seconds (7.81 hours) Timestep: 6260000 mean reward (100 episodes): 99.2400 best mean reward: 104.1600 current episode reward: 102.0000 episodes: 1884 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elapsed time: 28567.0 seconds (7.94 hours) Timestep: 6360000 mean reward (100 episodes): 103.8500 best mean reward: 104.1600 current episode reward: 166.0000 episodes: 1914 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28614.5 seconds (7.95 hours) Timestep: 6370000 mean reward (100 episodes): 106.0200 best mean reward: 106.0200 current episode reward: 132.0000 episodes: 1917 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28660.8 seconds (7.96 hours) Timestep: 6380000 mean reward (100 episodes): 107.9400 best mean reward: 107.9400 current episode reward: 174.0000 episodes: 1920 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28707.9 seconds (7.97 hours) Timestep: 6390000 mean reward (100 episodes): 109.1600 best mean reward: 109.2400 current episode reward: 127.0000 episodes: 1923 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28754.2 seconds (7.99 hours) Timestep: 6400000 mean reward (100 episodes): 109.2100 best mean reward: 109.2400 current episode reward: 129.0000 episodes: 1926 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28800.7 seconds (8.00 hours) Timestep: 6410000 mean reward (100 episodes): 110.6000 best mean reward: 110.6000 current episode reward: 110.0000 episodes: 1929 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28846.4 seconds (8.01 hours) Timestep: 6420000 mean reward (100 episodes): 112.1400 best mean reward: 112.1400 current episode reward: 172.0000 episodes: 1932 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28892.3 seconds (8.03 hours) Timestep: 6430000 mean reward (100 episodes): 113.5000 best mean reward: 113.5000 current episode reward: 123.0000 episodes: 1935 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28938.5 seconds (8.04 hours) Timestep: 6440000 mean reward (100 episodes): 114.6600 best mean reward: 114.6600 current episode reward: 175.0000 episodes: 1938 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28984.3 seconds (8.05 hours) Timestep: 6450000 mean reward (100 episodes): 116.5300 best mean reward: 116.5300 current episode reward: 140.0000 episodes: 1941 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29031.0 seconds (8.06 hours) Timestep: 6460000 mean reward (100 episodes): 117.6400 best mean reward: 117.6400 current episode reward: 109.0000 episodes: 1944 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29077.2 seconds (8.08 hours) Timestep: 6470000 mean reward (100 episodes): 119.3900 best mean reward: 119.3900 current episode reward: 185.0000 episodes: 1947 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29123.7 seconds (8.09 hours) Timestep: 6480000 mean reward (100 episodes): 120.4600 best mean reward: 120.4600 current episode reward: 175.0000 episodes: 1950 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29170.1 seconds (8.10 hours) Timestep: 6490000 mean reward (100 episodes): 123.8500 best mean reward: 123.8500 current episode reward: 174.0000 episodes: 1953 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29216.6 seconds (8.12 hours) Timestep: 6500000 mean reward (100 episodes): 123.7900 best mean reward: 123.9000 current episode reward: 139.0000 episodes: 1956 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29264.2 seconds (8.13 hours) Timestep: 6510000 mean reward (100 episodes): 124.7600 best mean reward: 124.7600 current episode reward: 165.0000 episodes: 1959 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29310.1 seconds (8.14 hours) Timestep: 6520000 mean reward (100 episodes): 126.8800 best mean reward: 126.8800 current episode reward: 166.0000 episodes: 1962 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29356.8 seconds (8.15 hours) Timestep: 6530000 mean reward (100 episodes): 128.2500 best mean reward: 128.2500 current episode reward: 272.0000 episodes: 1964 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29403.5 seconds (8.17 hours) Timestep: 6540000 mean reward (100 episodes): 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elapsed time: 29636.3 seconds (8.23 hours) Timestep: 6590000 mean reward (100 episodes): 145.9900 best mean reward: 145.9900 current episode reward: 199.0000 episodes: 1979 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29682.5 seconds (8.25 hours) Timestep: 6600000 mean reward (100 episodes): 144.5900 best mean reward: 145.9900 current episode reward: 153.0000 episodes: 1982 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29729.7 seconds (8.26 hours) Timestep: 6610000 mean reward (100 episodes): 146.3600 best mean reward: 146.3600 current episode reward: 136.0000 episodes: 1984 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29776.9 seconds (8.27 hours) Timestep: 6620000 mean reward (100 episodes): 147.8200 best mean reward: 147.8700 current episode reward: 174.0000 episodes: 1986 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29822.6 seconds (8.28 hours) Timestep: 6630000 mean reward (100 episodes): 148.9400 best mean reward: 148.9400 current episode reward: 157.0000 episodes: 1989 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29869.8 seconds (8.30 hours) Timestep: 6640000 mean reward (100 episodes): 151.5900 best mean reward: 151.5900 current episode reward: 364.0000 episodes: 1990 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29916.2 seconds (8.31 hours) Timestep: 6650000 mean reward (100 episodes): 156.0400 best mean reward: 156.0400 current episode reward: 313.0000 episodes: 1992 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29963.1 seconds (8.32 hours) Timestep: 6660000 mean reward (100 episodes): 159.0600 best mean reward: 159.0600 current episode reward: 187.0000 episodes: 1994 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30010.1 seconds (8.34 hours) Timestep: 6670000 mean reward (100 episodes): 158.1700 best mean reward: 159.0700 current episode reward: 83.0000 episodes: 1996 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30056.5 seconds (8.35 hours) Timestep: 6680000 mean reward (100 episodes): 160.0000 best mean reward: 160.0000 current episode reward: 171.0000 episodes: 1998 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30103.2 seconds (8.36 hours) Timestep: 6690000 mean reward (100 episodes): 162.5900 best mean reward: 162.5900 current episode reward: 120.0000 episodes: 2001 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30150.3 seconds (8.38 hours) Timestep: 6700000 mean reward (100 episodes): 165.6800 best mean reward: 165.6800 current episode reward: 325.0000 episodes: 2003 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30196.6 seconds (8.39 hours) Timestep: 6710000 mean reward (100 episodes): 168.7100 best mean reward: 168.7100 current episode reward: 407.0000 episodes: 2005 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 30244.2 seconds (8.40 hours) Timestep: 6720000 mean reward (100 episodes): 172.5900 best mean reward: 172.5900 current episode reward: 385.0000 episodes: 2007 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exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34576.8 seconds (9.60 hours) Timestep: 7650000 mean reward (100 episodes): 320.3700 best mean reward: 320.7000 current episode reward: 474.0000 episodes: 2185 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34623.1 seconds (9.62 hours) Timestep: 7660000 mean reward (100 episodes): 323.1600 best mean reward: 325.3400 current episode reward: 163.0000 episodes: 2187 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34669.8 seconds (9.63 hours) Timestep: 7670000 mean reward (100 episodes): 324.3500 best mean reward: 325.3400 current episode reward: 438.0000 episodes: 2189 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34715.4 seconds (9.64 hours) Timestep: 7680000 mean reward (100 episodes): 318.2900 best mean reward: 325.3400 current episode reward: 183.0000 episodes: 2192 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 34761.4 seconds (9.66 hours) Timestep: 7690000 mean reward (100 episodes): 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elapsed time: 34993.9 seconds (9.72 hours) Timestep: 7740000 mean reward (100 episodes): 319.7600 best mean reward: 325.3400 current episode reward: 368.0000 episodes: 2203 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35041.0 seconds (9.73 hours) Timestep: 7750000 mean reward (100 episodes): 322.8600 best mean reward: 325.3400 current episode reward: 397.0000 episodes: 2205 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35088.3 seconds (9.75 hours) Timestep: 7760000 mean reward (100 episodes): 322.4100 best mean reward: 325.3400 current episode reward: 420.0000 episodes: 2206 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35134.9 seconds (9.76 hours) Timestep: 7770000 mean reward (100 episodes): 323.9100 best mean reward: 325.3400 current episode reward: 385.0000 episodes: 2208 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35181.9 seconds (9.77 hours) Timestep: 7780000 mean reward (100 episodes): 324.1000 best mean reward: 325.3400 current episode reward: 185.0000 episodes: 2209 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35228.9 seconds (9.79 hours) Timestep: 7790000 mean reward (100 episodes): 328.1000 best mean reward: 328.1000 current episode reward: 396.0000 episodes: 2211 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35276.6 seconds (9.80 hours) Timestep: 7800000 mean reward (100 episodes): 324.8100 best mean reward: 328.1000 current episode reward: 161.0000 episodes: 2214 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35322.4 seconds (9.81 hours) Timestep: 7810000 mean reward (100 episodes): 324.8400 best mean reward: 328.1000 current episode reward: 431.0000 episodes: 2215 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35369.6 seconds (9.82 hours) Timestep: 7820000 mean reward (100 episodes): 325.0200 best mean reward: 328.1000 current episode reward: 411.0000 episodes: 2217 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35416.0 seconds (9.84 hours) Timestep: 7830000 mean reward (100 episodes): 325.7500 best mean reward: 328.1000 current episode reward: 456.0000 episodes: 2218 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35462.6 seconds (9.85 hours) Timestep: 7840000 mean reward (100 episodes): 324.6600 best mean reward: 328.1000 current episode reward: 170.0000 episodes: 2221 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35508.8 seconds (9.86 hours) Timestep: 7850000 mean reward (100 episodes): 324.4900 best mean reward: 328.1000 current episode reward: 325.0000 episodes: 2223 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35555.7 seconds (9.88 hours) Timestep: 7860000 mean reward (100 episodes): 318.4700 best mean reward: 328.1000 current episode reward: 153.0000 episodes: 2226 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35601.8 seconds (9.89 hours) Timestep: 7870000 mean reward (100 episodes): 315.4300 best mean reward: 328.1000 current episode reward: 128.0000 episodes: 2229 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35648.3 seconds (9.90 hours) Timestep: 7880000 mean reward (100 episodes): 315.1000 best mean reward: 328.1000 current episode reward: 359.0000 episodes: 2230 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35695.2 seconds (9.92 hours) Timestep: 7890000 mean reward (100 episodes): 313.4600 best mean reward: 328.1000 current episode reward: 189.0000 episodes: 2232 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35742.7 seconds (9.93 hours) Timestep: 7900000 mean reward (100 episodes): 316.4500 best mean reward: 328.1000 current episode reward: 424.0000 episodes: 2234 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35789.4 seconds (9.94 hours) Timestep: 7910000 mean reward (100 episodes): 311.1200 best mean reward: 328.1000 current episode reward: 193.0000 episodes: 2237 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35836.0 seconds (9.95 hours) Timestep: 7920000 mean reward (100 episodes): 312.1800 best mean reward: 328.1000 current episode reward: 495.0000 episodes: 2238 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35882.3 seconds (9.97 hours) Timestep: 7930000 mean reward (100 episodes): 314.7200 best mean reward: 328.1000 current episode reward: 627.0000 episodes: 2239 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35929.7 seconds (9.98 hours) Timestep: 7940000 mean reward (100 episodes): 315.1100 best mean reward: 328.1000 current episode reward: 413.0000 episodes: 2241 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 35976.3 seconds (9.99 hours) Timestep: 7950000 mean reward (100 episodes): 314.7000 best mean reward: 328.1000 current episode reward: 348.0000 episodes: 2243 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36022.7 seconds (10.01 hours) Timestep: 7960000 mean reward (100 episodes): 311.7500 best mean reward: 328.1000 current episode reward: 111.0000 episodes: 2245 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36069.8 seconds (10.02 hours) Timestep: 7970000 mean reward (100 episodes): 312.7900 best mean reward: 328.1000 current episode reward: 403.0000 episodes: 2247 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36116.4 seconds (10.03 hours) Timestep: 7980000 mean reward (100 episodes): 311.7400 best mean reward: 328.1000 current episode reward: 351.0000 episodes: 2248 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36163.0 seconds (10.05 hours) Timestep: 7990000 mean reward (100 episodes): 314.6200 best mean reward: 328.1000 current episode reward: 461.0000 episodes: 2250 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36209.0 seconds (10.06 hours) Timestep: 8000000 mean reward (100 episodes): 317.2400 best mean reward: 328.1000 current episode reward: 370.0000 episodes: 2251 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36256.0 seconds (10.07 hours) Timestep: 8010000 mean reward (100 episodes): 316.7700 best mean reward: 328.1000 current episode reward: 401.0000 episodes: 2253 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36302.6 seconds (10.08 hours) Timestep: 8020000 mean reward (100 episodes): 316.9000 best mean reward: 328.1000 current episode reward: 430.0000 episodes: 2254 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36348.9 seconds (10.10 hours) Timestep: 8030000 mean reward (100 episodes): 317.1100 best mean reward: 328.1000 current episode reward: 341.0000 episodes: 2256 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36395.2 seconds (10.11 hours) Timestep: 8040000 mean reward (100 episodes): 319.3800 best mean reward: 328.1000 current episode reward: 408.0000 episodes: 2257 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36442.2 seconds (10.12 hours) Timestep: 8050000 mean reward (100 episodes): 321.2600 best mean reward: 328.1000 current episode reward: 436.0000 episodes: 2259 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36488.8 seconds (10.14 hours) Timestep: 8060000 mean reward (100 episodes): 325.9400 best mean reward: 328.1000 current episode reward: 667.0000 episodes: 2260 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36535.9 seconds (10.15 hours) Timestep: 8070000 mean reward (100 episodes): 325.8700 best mean reward: 328.1000 current episode reward: 387.0000 episodes: 2261 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36582.5 seconds (10.16 hours) Timestep: 8080000 mean reward (100 episodes): 326.7500 best mean reward: 328.1000 current episode reward: 407.0000 episodes: 2263 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36629.0 seconds (10.17 hours) Timestep: 8090000 mean reward (100 episodes): 324.7600 best mean reward: 328.1000 current episode reward: 192.0000 episodes: 2264 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36676.3 seconds (10.19 hours) Timestep: 8100000 mean reward (100 episodes): 329.4000 best mean reward: 329.4000 current episode reward: 481.0000 episodes: 2266 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36723.8 seconds (10.20 hours) Timestep: 8110000 mean reward (100 episodes): 332.4000 best mean reward: 332.4000 current episode reward: 494.0000 episodes: 2267 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36771.6 seconds (10.21 hours) Timestep: 8120000 mean reward (100 episodes): 331.2400 best mean reward: 332.5200 current episode reward: 195.0000 episodes: 2269 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36818.1 seconds (10.23 hours) Timestep: 8130000 mean reward (100 episodes): 334.3000 best mean reward: 334.3000 current episode reward: 353.0000 episodes: 2271 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36865.0 seconds (10.24 hours) Timestep: 8140000 mean reward (100 episodes): 334.1900 best mean reward: 334.3000 current episode reward: 441.0000 episodes: 2273 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36912.0 seconds (10.25 hours) Timestep: 8150000 mean reward (100 episodes): 336.6900 best mean reward: 336.6900 current episode reward: 449.0000 episodes: 2274 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 36958.9 seconds (10.27 hours) Timestep: 8160000 mean reward (100 episodes): 340.6500 best mean reward: 340.6500 current episode reward: 394.0000 episodes: 2276 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37004.9 seconds (10.28 hours) Timestep: 8170000 mean reward (100 episodes): 343.0400 best mean reward: 343.0400 current episode reward: 409.0000 episodes: 2277 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37051.8 seconds (10.29 hours) Timestep: 8180000 mean reward (100 episodes): 344.0100 best mean reward: 346.3000 current episode reward: 155.0000 episodes: 2280 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37098.7 seconds (10.31 hours) Timestep: 8190000 mean reward (100 episodes): 342.1900 best mean reward: 346.3000 current episode reward: 196.0000 episodes: 2282 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37145.8 seconds (10.32 hours) Timestep: 8200000 mean reward (100 episodes): 336.8600 best mean reward: 346.3000 current episode reward: 177.0000 episodes: 2284 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37192.5 seconds (10.33 hours) Timestep: 8210000 mean reward (100 episodes): 333.1600 best mean reward: 346.3000 current episode reward: 383.0000 episodes: 2286 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37238.5 seconds (10.34 hours) Timestep: 8220000 mean reward (100 episodes): 338.3600 best mean reward: 346.3000 current episode reward: 683.0000 episodes: 2287 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37284.8 seconds (10.36 hours) Timestep: 8230000 mean reward (100 episodes): 341.2200 best mean reward: 346.3000 current episode reward: 721.0000 episodes: 2288 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37331.8 seconds (10.37 hours) Timestep: 8240000 mean reward (100 episodes): 342.1500 best mean reward: 346.3000 current episode reward: 410.0000 episodes: 2290 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37377.8 seconds (10.38 hours) Timestep: 8250000 mean reward (100 episodes): 345.4200 best mean reward: 346.3000 current episode reward: 446.0000 episodes: 2291 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37424.7 seconds (10.40 hours) Timestep: 8260000 mean reward (100 episodes): 351.1400 best mean reward: 351.1400 current episode reward: 453.0000 episodes: 2293 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37471.7 seconds (10.41 hours) Timestep: 8270000 mean reward (100 episodes): 351.6800 best mean reward: 351.6800 current episode reward: 445.0000 episodes: 2294 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37517.8 seconds (10.42 hours) Timestep: 8280000 mean reward (100 episodes): 355.2100 best mean reward: 355.2100 current episode reward: 384.0000 episodes: 2296 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 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reward: 377.0000 episodes: 2305 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37798.2 seconds (10.50 hours) Timestep: 8340000 mean reward (100 episodes): 348.8400 best mean reward: 358.9600 current episode reward: 197.0000 episodes: 2307 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37845.2 seconds (10.51 hours) Timestep: 8350000 mean reward (100 episodes): 349.0400 best mean reward: 358.9600 current episode reward: 166.0000 episodes: 2309 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37891.7 seconds (10.53 hours) Timestep: 8360000 mean reward (100 episodes): 344.2000 best mean reward: 358.9600 current episode reward: 161.0000 episodes: 2311 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37938.3 seconds (10.54 hours) Timestep: 8370000 mean reward (100 episodes): 348.1100 best mean reward: 358.9600 current episode reward: 377.0000 episodes: 2313 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37984.8 seconds (10.55 hours) Timestep: 8380000 mean reward (100 episodes): 350.5100 best mean reward: 358.9600 current episode reward: 401.0000 episodes: 2314 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38031.2 seconds (10.56 hours) Timestep: 8390000 mean reward (100 episodes): 346.3100 best mean reward: 358.9600 current episode reward: 197.0000 episodes: 2316 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38077.6 seconds (10.58 hours) Timestep: 8400000 mean reward (100 episodes): 346.6300 best mean reward: 358.9600 current episode reward: 443.0000 episodes: 2317 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38123.9 seconds (10.59 hours) Timestep: 8410000 mean reward (100 episodes): 348.9500 best mean reward: 358.9600 current episode reward: 490.0000 episodes: 2319 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38171.5 seconds (10.60 hours) Timestep: 8420000 mean reward (100 episodes): 351.0600 best mean reward: 358.9600 current episode reward: 404.0000 episodes: 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Timestep: 8700000 mean reward (100 episodes): 380.7100 best mean reward: 385.7900 current episode reward: 413.0000 episodes: 2365 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39522.7 seconds (10.98 hours) Timestep: 8710000 mean reward (100 episodes): 376.4400 best mean reward: 385.7900 current episode reward: 185.0000 episodes: 2367 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39569.7 seconds (10.99 hours) Timestep: 8720000 mean reward (100 episodes): 370.9500 best mean reward: 385.7900 current episode reward: 146.0000 episodes: 2370 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39615.7 seconds (11.00 hours) Timestep: 8730000 mean reward (100 episodes): 372.2200 best mean reward: 385.7900 current episode reward: 475.0000 episodes: 2372 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39661.3 seconds (11.02 hours) Timestep: 8740000 mean reward (100 episodes): 369.9400 best mean reward: 385.7900 current episode reward: 479.0000 episodes: 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episodes): 376.0100 best mean reward: 385.7900 current episode reward: 699.0000 episodes: 2382 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39940.0 seconds (11.09 hours) Timestep: 8800000 mean reward (100 episodes): 378.1800 best mean reward: 385.7900 current episode reward: 404.0000 episodes: 2383 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39987.1 seconds (11.11 hours) Timestep: 8810000 mean reward (100 episodes): 380.7400 best mean reward: 385.7900 current episode reward: 373.0000 episodes: 2385 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40034.5 seconds (11.12 hours) Timestep: 8820000 mean reward (100 episodes): 381.0000 best mean reward: 385.7900 current episode reward: 409.0000 episodes: 2386 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40081.5 seconds (11.13 hours) Timestep: 8830000 mean reward (100 episodes): 381.4300 best mean reward: 385.7900 current episode reward: 726.0000 episodes: 2387 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40129.2 seconds (11.15 hours) Timestep: 8840000 mean reward (100 episodes): 381.4800 best mean reward: 385.7900 current episode reward: 477.0000 episodes: 2389 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40175.4 seconds (11.16 hours) Timestep: 8850000 mean reward (100 episodes): 382.2000 best mean reward: 385.7900 current episode reward: 482.0000 episodes: 2390 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40222.2 seconds (11.17 hours) Timestep: 8860000 mean reward (100 episodes): 379.3300 best mean reward: 385.7900 current episode reward: 162.0000 episodes: 2392 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40268.7 seconds (11.19 hours) Timestep: 8870000 mean reward (100 episodes): 377.1100 best mean reward: 385.7900 current episode reward: 192.0000 episodes: 2394 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40315.0 seconds (11.20 hours) Timestep: 8880000 mean reward (100 episodes): 379.0100 best mean reward: 385.7900 current episode reward: 653.0000 episodes: 2395 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40361.3 seconds (11.21 hours) Timestep: 8890000 mean reward (100 episodes): 380.7400 best mean reward: 385.7900 current episode reward: 495.0000 episodes: 2397 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40407.3 seconds (11.22 hours) Timestep: 8900000 mean reward (100 episodes): 377.6700 best mean reward: 385.7900 current episode reward: 187.0000 episodes: 2399 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40453.9 seconds (11.24 hours) Timestep: 8910000 mean reward (100 episodes): 378.5800 best mean reward: 385.7900 current episode reward: 480.0000 episodes: 2400 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40501.1 seconds (11.25 hours) Timestep: 8920000 mean reward (100 episodes): 382.5200 best mean reward: 385.7900 current episode reward: 408.0000 episodes: 2402 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40547.5 seconds (11.26 hours) Timestep: 8930000 mean reward (100 episodes): 386.1100 best mean reward: 386.1100 current episode reward: 479.0000 episodes: 2404 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40593.1 seconds (11.28 hours) Timestep: 8940000 mean reward (100 episodes): 387.1200 best mean reward: 387.1200 current episode reward: 195.0000 episodes: 2406 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40638.6 seconds (11.29 hours) Timestep: 8950000 mean reward (100 episodes): 389.6100 best mean reward: 389.6100 current episode reward: 446.0000 episodes: 2407 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40685.9 seconds (11.30 hours) Timestep: 8960000 mean reward (100 episodes): 393.0800 best mean reward: 393.0800 current episode reward: 479.0000 episodes: 2409 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40732.5 seconds (11.31 hours) Timestep: 8970000 mean reward (100 episodes): 392.7000 best mean reward: 393.0800 current episode reward: 397.0000 episodes: 2410 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40779.3 seconds (11.33 hours) Timestep: 8980000 mean reward (100 episodes): 396.3400 best mean reward: 396.3400 current episode reward: 490.0000 episodes: 2412 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40825.4 seconds (11.34 hours) Timestep: 8990000 mean reward (100 episodes): 395.3600 best mean reward: 397.4600 current episode reward: 191.0000 episodes: 2414 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40871.1 seconds (11.35 hours) Timestep: 9000000 mean reward (100 episodes): 397.2500 best mean reward: 397.4600 current episode reward: 435.0000 episodes: 2415 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40917.2 seconds (11.37 hours) Timestep: 9010000 mean reward (100 episodes): 399.4600 best mean reward: 399.4600 current episode reward: 446.0000 episodes: 2417 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40964.6 seconds (11.38 hours) Timestep: 9020000 mean reward (100 episodes): 397.5100 best mean reward: 399.4600 current episode reward: 499.0000 episodes: 2418 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41011.1 seconds (11.39 hours) Timestep: 9030000 mean reward (100 episodes): 397.1700 best mean reward: 399.4600 current episode reward: 424.0000 episodes: 2420 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41057.1 seconds (11.40 hours) Timestep: 9040000 mean reward (100 episodes): 398.2900 best mean reward: 399.4600 current episode reward: 709.0000 episodes: 2421 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41103.7 seconds (11.42 hours) Timestep: 9050000 mean reward (100 episodes): 396.4000 best mean reward: 401.4000 current episode reward: 183.0000 episodes: 2423 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 41150.5 seconds (11.43 hours) Timestep: 9060000 mean reward (100 episodes): 396.6600 best mean reward: 401.4000 current episode reward: 466.0000 episodes: 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reward: 493.0000 episodes: 2461 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42266.7 seconds (11.74 hours) Timestep: 9300000 mean reward (100 episodes): 400.7800 best mean reward: 413.1900 current episode reward: 486.0000 episodes: 2463 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42312.9 seconds (11.75 hours) Timestep: 9310000 mean reward (100 episodes): 400.6000 best mean reward: 413.1900 current episode reward: 449.0000 episodes: 2464 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42359.5 seconds (11.77 hours) Timestep: 9320000 mean reward (100 episodes): 401.7700 best mean reward: 413.1900 current episode reward: 478.0000 episodes: 2466 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42405.9 seconds (11.78 hours) Timestep: 9330000 mean reward (100 episodes): 403.9400 best mean reward: 413.1900 current episode reward: 402.0000 episodes: 2467 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42452.9 seconds (11.79 hours) 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reward: 491.0000 episodes: 2508 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43753.3 seconds (12.15 hours) Timestep: 9620000 mean reward (100 episodes): 430.3400 best mean reward: 435.8900 current episode reward: 183.0000 episodes: 2509 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43799.1 seconds (12.17 hours) Timestep: 9630000 mean reward (100 episodes): 431.1000 best mean reward: 435.8900 current episode reward: 174.0000 episodes: 2511 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43845.5 seconds (12.18 hours) Timestep: 9640000 mean reward (100 episodes): 434.0200 best mean reward: 435.8900 current episode reward: 782.0000 episodes: 2512 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43892.7 seconds (12.19 hours) Timestep: 9650000 mean reward (100 episodes): 428.8700 best mean reward: 435.8900 current episode reward: 31.0000 episodes: 2515 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43939.2 seconds (12.21 hours) Timestep: 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exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44171.6 seconds (12.27 hours) Timestep: 9710000 mean reward (100 episodes): 394.9200 best mean reward: 435.8900 current episode reward: 64.0000 episodes: 2528 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44217.7 seconds (12.28 hours) Timestep: 9720000 mean reward (100 episodes): 395.1600 best mean reward: 435.8900 current episode reward: 428.0000 episodes: 2530 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44263.7 seconds (12.30 hours) Timestep: 9730000 mean reward (100 episodes): 388.5700 best mean reward: 435.8900 current episode reward: 290.0000 episodes: 2532 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44310.0 seconds (12.31 hours) Timestep: 9740000 mean reward (100 episodes): 388.4900 best mean reward: 435.8900 current episode reward: 189.0000 episodes: 2534 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44357.3 seconds (12.32 hours) Timestep: 9750000 mean reward (100 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mean reward: 435.8900 current episode reward: 192.0000 episodes: 2553 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44822.7 seconds (12.45 hours) Timestep: 9850000 mean reward (100 episodes): 376.0500 best mean reward: 435.8900 current episode reward: 396.0000 episodes: 2555 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44869.3 seconds (12.46 hours) Timestep: 9860000 mean reward (100 episodes): 370.7100 best mean reward: 435.8900 current episode reward: 157.0000 episodes: 2557 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44915.5 seconds (12.48 hours) Timestep: 9870000 mean reward (100 episodes): 370.1300 best mean reward: 435.8900 current episode reward: 188.0000 episodes: 2559 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44961.5 seconds (12.49 hours) Timestep: 9880000 mean reward (100 episodes): 364.8900 best mean reward: 435.8900 current episode reward: 160.0000 episodes: 2562 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 45007.6 seconds (12.50 hours) Timestep: 9890000 mean reward (100 episodes): 367.9600 best mean reward: 435.8900 current episode reward: 793.0000 episodes: 2563 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 45053.8 seconds (12.51 hours) Timestep: 9900000 mean reward (100 episodes): 370.5500 best mean reward: 435.8900 current episode reward: 708.0000 episodes: 2564 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 45100.4 seconds (12.53 hours) Timestep: 9910000 mean reward (100 episodes): 368.2700 best mean reward: 435.8900 current episode reward: 187.0000 episodes: 2565 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 45146.8 seconds (12.54 hours) Timestep: 9920000 mean reward (100 episodes): 371.2700 best mean reward: 435.8900 current episode reward: 492.0000 episodes: 2567 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 45193.4 seconds (12.55 hours) Timestep: 9930000 mean reward (100 episodes): 371.2500 best mean reward: 435.8900 current episode reward: 448.0000 episodes: 2568 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 45240.4 seconds (12.57 hours) Timestep: 9940000 mean reward (100 episodes): 373.5700 best mean reward: 435.8900 current episode reward: 693.0000 episodes: 2569 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 45286.9 seconds (12.58 hours) Timestep: 9950000 mean reward (100 episodes): 373.8200 best mean reward: 435.8900 current episode reward: 496.0000 episodes: 2571 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 45333.7 seconds (12.59 hours) Timestep: 9960000 mean reward (100 episodes): 371.4300 best mean reward: 435.8900 current episode reward: 489.0000 episodes: 2572 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 45380.2 seconds (12.61 hours) Timestep: 9970000 mean reward (100 episodes): 370.9500 best mean reward: 435.8900 current episode reward: 197.0000 episodes: 2574 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 45426.3 seconds (12.62 hours) Timestep: 9980000 mean reward (100 episodes): 363.4900 best mean reward: 435.8900 current episode reward: 193.0000 episodes: 2576 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 45473.5 seconds (12.63 hours) Timestep: 9985964 mean reward (100 episodes): 363.4900 best mean reward: 435.8900 current episode reward: 193.0000 episodes: 2576 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 45501.3 seconds (12.64 hours) ================================================ FILE: dqn/logs_text/Enduro_s002.text ================================================ ('AVAILABLE GPUS: ', [u'device: 0, name: TITAN X (Pascal), pci bus id: 0000:01:00.0']) task = Task Timestep: 60000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 18 exploration: 0.94600 learning_rate: 0.00010 elapsed time: 101.6 seconds (0.03 hours) Timestep: 70000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 21 exploration: 0.93700 learning_rate: 0.00010 elapsed time: 135.5 seconds (0.04 hours) Timestep: 80000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 24 exploration: 0.92800 learning_rate: 0.00010 elapsed time: 169.5 seconds (0.05 hours) Timestep: 90000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 27 exploration: 0.91900 learning_rate: 0.00010 elapsed time: 204.0 seconds (0.06 hours) Timestep: 100000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 30 exploration: 0.91000 learning_rate: 0.00010 elapsed time: 241.1 seconds (0.07 hours) Timestep: 110000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 33 exploration: 0.90100 learning_rate: 0.00010 elapsed time: 275.7 seconds (0.08 hours) Timestep: 120000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 36 exploration: 0.89200 learning_rate: 0.00010 elapsed time: 310.4 seconds (0.09 hours) Timestep: 130000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 39 exploration: 0.88300 learning_rate: 0.00010 elapsed time: 345.1 seconds (0.10 hours) Timestep: 140000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 42 exploration: 0.87400 learning_rate: 0.00010 elapsed time: 380.0 seconds (0.11 hours) Timestep: 150000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 45 exploration: 0.86500 learning_rate: 0.00010 elapsed time: 414.9 seconds (0.12 hours) Timestep: 160000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 48 exploration: 0.85600 learning_rate: 0.00010 elapsed time: 449.7 seconds (0.12 hours) Timestep: 170000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 51 exploration: 0.84700 learning_rate: 0.00010 elapsed time: 485.1 seconds (0.13 hours) Timestep: 180000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 54 exploration: 0.83800 learning_rate: 0.00010 elapsed time: 520.4 seconds (0.14 hours) Timestep: 190000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 57 exploration: 0.82900 learning_rate: 0.00010 elapsed time: 556.0 seconds (0.15 hours) Timestep: 200000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 60 exploration: 0.82000 learning_rate: 0.00010 elapsed time: 591.7 seconds (0.16 hours) Timestep: 210000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 63 exploration: 0.81100 learning_rate: 0.00010 elapsed time: 627.7 seconds (0.17 hours) Timestep: 220000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 66 exploration: 0.80200 learning_rate: 0.00010 elapsed time: 666.3 seconds (0.19 hours) Timestep: 230000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 69 exploration: 0.79300 learning_rate: 0.00010 elapsed time: 702.5 seconds (0.20 hours) Timestep: 240000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 72 exploration: 0.78400 learning_rate: 0.00010 elapsed time: 739.2 seconds (0.21 hours) Timestep: 250000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 75 exploration: 0.77500 learning_rate: 0.00010 elapsed time: 775.9 seconds (0.22 hours) Timestep: 260000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 78 exploration: 0.76600 learning_rate: 0.00010 elapsed time: 812.9 seconds (0.23 hours) Timestep: 270000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 81 exploration: 0.75700 learning_rate: 0.00010 elapsed time: 849.5 seconds (0.24 hours) Timestep: 280000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 84 exploration: 0.74800 learning_rate: 0.00010 elapsed time: 886.8 seconds (0.25 hours) Timestep: 290000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 87 exploration: 0.73900 learning_rate: 0.00010 elapsed time: 924.0 seconds (0.26 hours) Timestep: 300000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 90 exploration: 0.73000 learning_rate: 0.00010 elapsed time: 961.4 seconds (0.27 hours) Timestep: 310000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 93 exploration: 0.72100 learning_rate: 0.00010 elapsed time: 998.6 seconds (0.28 hours) Timestep: 320000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 96 exploration: 0.71200 learning_rate: 0.00010 elapsed time: 1035.8 seconds (0.29 hours) Timestep: 330000 mean reward (100 episodes): 0.0000 best mean reward: -inf current episode reward: 0.0000 episodes: 99 exploration: 0.70300 learning_rate: 0.00010 elapsed time: 1073.8 seconds (0.30 hours) Timestep: 340000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 102 exploration: 0.69400 learning_rate: 0.00010 elapsed time: 1111.1 seconds (0.31 hours) Timestep: 350000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 105 exploration: 0.68500 learning_rate: 0.00010 elapsed time: 1148.8 seconds (0.32 hours) Timestep: 360000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 108 exploration: 0.67600 learning_rate: 0.00010 elapsed time: 1186.6 seconds (0.33 hours) Timestep: 370000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 111 exploration: 0.66700 learning_rate: 0.00010 elapsed time: 1224.0 seconds (0.34 hours) Timestep: 380000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 114 exploration: 0.65800 learning_rate: 0.00010 elapsed time: 1261.6 seconds (0.35 hours) Timestep: 390000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 117 exploration: 0.64900 learning_rate: 0.00010 elapsed time: 1300.3 seconds (0.36 hours) Timestep: 400000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 120 exploration: 0.64000 learning_rate: 0.00010 elapsed time: 1338.9 seconds (0.37 hours) Timestep: 410000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 123 exploration: 0.63100 learning_rate: 0.00010 elapsed time: 1377.2 seconds (0.38 hours) Timestep: 420000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 126 exploration: 0.62200 learning_rate: 0.00010 elapsed time: 1418.2 seconds (0.39 hours) Timestep: 430000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 129 exploration: 0.61300 learning_rate: 0.00010 elapsed time: 1457.4 seconds (0.40 hours) Timestep: 440000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 132 exploration: 0.60400 learning_rate: 0.00010 elapsed time: 1496.4 seconds (0.42 hours) Timestep: 450000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 135 exploration: 0.59500 learning_rate: 0.00010 elapsed time: 1535.6 seconds (0.43 hours) Timestep: 460000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 138 exploration: 0.58600 learning_rate: 0.00010 elapsed time: 1574.9 seconds (0.44 hours) Timestep: 470000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 141 exploration: 0.57700 learning_rate: 0.00010 elapsed time: 1614.1 seconds (0.45 hours) Timestep: 480000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 144 exploration: 0.56800 learning_rate: 0.00010 elapsed time: 1653.5 seconds (0.46 hours) Timestep: 490000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 147 exploration: 0.55900 learning_rate: 0.00010 elapsed time: 1693.6 seconds (0.47 hours) Timestep: 500000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 150 exploration: 0.55000 learning_rate: 0.00010 elapsed time: 1732.8 seconds (0.48 hours) Timestep: 510000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 153 exploration: 0.54100 learning_rate: 0.00010 elapsed time: 1772.4 seconds (0.49 hours) Timestep: 520000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 156 exploration: 0.53200 learning_rate: 0.00010 elapsed time: 1812.6 seconds (0.50 hours) Timestep: 530000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 159 exploration: 0.52300 learning_rate: 0.00010 elapsed time: 1852.1 seconds (0.51 hours) Timestep: 540000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 162 exploration: 0.51400 learning_rate: 0.00010 elapsed time: 1892.2 seconds (0.53 hours) Timestep: 550000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 165 exploration: 0.50500 learning_rate: 0.00010 elapsed time: 1932.5 seconds (0.54 hours) Timestep: 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time: 2136.3 seconds (0.59 hours) Timestep: 610000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 183 exploration: 0.45100 learning_rate: 0.00010 elapsed time: 2178.1 seconds (0.61 hours) Timestep: 620000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 186 exploration: 0.44200 learning_rate: 0.00010 elapsed time: 2219.1 seconds (0.62 hours) Timestep: 630000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 189 exploration: 0.43300 learning_rate: 0.00010 elapsed time: 2260.8 seconds (0.63 hours) Timestep: 640000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 192 exploration: 0.42400 learning_rate: 0.00010 elapsed time: 2302.4 seconds (0.64 hours) Timestep: 650000 mean reward (100 episodes): 0.0000 best mean reward: 0.0000 current episode reward: 0.0000 episodes: 195 exploration: 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reward: 0.0000 episodes: 210 exploration: 0.37000 learning_rate: 0.00010 elapsed time: 2555.0 seconds (0.71 hours) Timestep: 710000 mean reward (100 episodes): 0.0100 best mean reward: 0.0100 current episode reward: 0.0000 episodes: 213 exploration: 0.36100 learning_rate: 0.00010 elapsed time: 2597.4 seconds (0.72 hours) Timestep: 720000 mean reward (100 episodes): 0.0100 best mean reward: 0.0100 current episode reward: 0.0000 episodes: 216 exploration: 0.35200 learning_rate: 0.00010 elapsed time: 2640.8 seconds (0.73 hours) Timestep: 730000 mean reward (100 episodes): 0.0100 best mean reward: 0.0100 current episode reward: 0.0000 episodes: 219 exploration: 0.34300 learning_rate: 0.00010 elapsed time: 2683.8 seconds (0.75 hours) Timestep: 740000 mean reward (100 episodes): 0.0100 best mean reward: 0.0100 current episode reward: 0.0000 episodes: 222 exploration: 0.33400 learning_rate: 0.00010 elapsed time: 2726.6 seconds (0.76 hours) Timestep: 750000 mean reward (100 episodes): 0.0100 best mean reward: 0.0100 current episode reward: 0.0000 episodes: 225 exploration: 0.32500 learning_rate: 0.00010 elapsed time: 2769.0 seconds (0.77 hours) Timestep: 760000 mean reward (100 episodes): 0.0100 best mean reward: 0.0100 current episode reward: 0.0000 episodes: 228 exploration: 0.31600 learning_rate: 0.00010 elapsed time: 2813.2 seconds (0.78 hours) Timestep: 770000 mean reward (100 episodes): 0.0100 best mean reward: 0.0100 current episode reward: 0.0000 episodes: 231 exploration: 0.30700 learning_rate: 0.00010 elapsed time: 2856.4 seconds (0.79 hours) Timestep: 780000 mean reward (100 episodes): 0.0100 best mean reward: 0.0100 current episode reward: 0.0000 episodes: 234 exploration: 0.29800 learning_rate: 0.00010 elapsed time: 2900.3 seconds (0.81 hours) Timestep: 790000 mean reward (100 episodes): 0.0100 best mean reward: 0.0100 current episode reward: 0.0000 episodes: 237 exploration: 0.28900 learning_rate: 0.00010 elapsed time: 2943.8 seconds (0.82 hours) Timestep: 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reward: 0.0000 episodes: 282 exploration: 0.15400 learning_rate: 0.00010 elapsed time: 3607.6 seconds (1.00 hours) Timestep: 950000 mean reward (100 episodes): 0.1100 best mean reward: 0.1100 current episode reward: 0.0000 episodes: 285 exploration: 0.14500 learning_rate: 0.00010 elapsed time: 3653.0 seconds (1.01 hours) Timestep: 960000 mean reward (100 episodes): 0.1100 best mean reward: 0.1100 current episode reward: 0.0000 episodes: 288 exploration: 0.13600 learning_rate: 0.00010 elapsed time: 3698.3 seconds (1.03 hours) Timestep: 970000 mean reward (100 episodes): 0.1100 best mean reward: 0.1100 current episode reward: 0.0000 episodes: 291 exploration: 0.12700 learning_rate: 0.00010 elapsed time: 3742.7 seconds (1.04 hours) Timestep: 980000 mean reward (100 episodes): 0.1400 best mean reward: 0.1400 current episode reward: 0.0000 episodes: 294 exploration: 0.11800 learning_rate: 0.00010 elapsed time: 3788.0 seconds (1.05 hours) Timestep: 990000 mean reward (100 episodes): 0.1400 best mean reward: 0.1500 current episode reward: 0.0000 episodes: 297 exploration: 0.10900 learning_rate: 0.00010 elapsed time: 3833.8 seconds (1.06 hours) Timestep: 1000000 mean reward (100 episodes): 0.1400 best mean reward: 0.1500 current episode reward: 0.0000 episodes: 300 exploration: 0.10000 learning_rate: 0.00010 elapsed time: 3878.9 seconds (1.08 hours) Timestep: 1010000 mean reward (100 episodes): 0.1400 best mean reward: 0.1500 current episode reward: 0.0000 episodes: 303 exploration: 0.09978 learning_rate: 0.00010 elapsed time: 3924.3 seconds (1.09 hours) Timestep: 1020000 mean reward (100 episodes): 0.1500 best mean reward: 0.1500 current episode reward: 0.0000 episodes: 307 exploration: 0.09955 learning_rate: 0.00010 elapsed time: 3969.6 seconds (1.10 hours) Timestep: 1030000 mean reward (100 episodes): 0.1500 best mean reward: 0.1500 current episode reward: 0.0000 episodes: 310 exploration: 0.09933 learning_rate: 0.00010 elapsed time: 4016.8 seconds (1.12 hours) 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reward: 0.2600 current episode reward: 0.0000 episodes: 1411 exploration: 0.01697 learning_rate: 0.00005 elapsed time: 20807.3 seconds (5.78 hours) Timestep: 4700000 mean reward (100 episodes): 0.6800 best mean reward: 0.6800 current episode reward: 42.0000 episodes: 1414 exploration: 0.01675 learning_rate: 0.00005 elapsed time: 20853.6 seconds (5.79 hours) Timestep: 4710000 mean reward (100 episodes): 1.4700 best mean reward: 1.4700 current episode reward: 25.0000 episodes: 1417 exploration: 0.01652 learning_rate: 0.00005 elapsed time: 20899.2 seconds (5.81 hours) Timestep: 4720000 mean reward (100 episodes): 1.8900 best mean reward: 1.8900 current episode reward: 21.0000 episodes: 1420 exploration: 0.01630 learning_rate: 0.00005 elapsed time: 20946.5 seconds (5.82 hours) Timestep: 4730000 mean reward (100 episodes): 2.7100 best mean reward: 2.7100 current episode reward: 29.0000 episodes: 1423 exploration: 0.01607 learning_rate: 0.00005 elapsed time: 20992.7 seconds (5.83 hours) Timestep: 4740000 mean reward (100 episodes): 3.7300 best mean reward: 3.7300 current episode reward: 30.0000 episodes: 1426 exploration: 0.01585 learning_rate: 0.00005 elapsed time: 21039.3 seconds (5.84 hours) Timestep: 4750000 mean reward (100 episodes): 4.4400 best mean reward: 4.4400 current episode reward: 26.0000 episodes: 1429 exploration: 0.01562 learning_rate: 0.00005 elapsed time: 21086.0 seconds (5.86 hours) Timestep: 4760000 mean reward (100 episodes): 5.4000 best mean reward: 5.4000 current episode reward: 31.0000 episodes: 1432 exploration: 0.01540 learning_rate: 0.00005 elapsed time: 21132.6 seconds (5.87 hours) Timestep: 4770000 mean reward (100 episodes): 6.1500 best mean reward: 6.1500 current episode reward: 12.0000 episodes: 1435 exploration: 0.01517 learning_rate: 0.00005 elapsed time: 21178.9 seconds (5.88 hours) Timestep: 4780000 mean reward (100 episodes): 7.2200 best mean reward: 7.2200 current episode reward: 38.0000 episodes: 1438 exploration: 0.01495 learning_rate: 0.00005 elapsed time: 21224.4 seconds (5.90 hours) Timestep: 4790000 mean reward (100 episodes): 8.0800 best mean reward: 8.0800 current episode reward: 39.0000 episodes: 1441 exploration: 0.01472 learning_rate: 0.00005 elapsed time: 21270.6 seconds (5.91 hours) Timestep: 4800000 mean reward (100 episodes): 8.4700 best mean reward: 8.4700 current episode reward: 18.0000 episodes: 1444 exploration: 0.01450 learning_rate: 0.00005 elapsed time: 21316.8 seconds (5.92 hours) Timestep: 4810000 mean reward (100 episodes): 9.5000 best mean reward: 9.5000 current episode reward: 41.0000 episodes: 1447 exploration: 0.01427 learning_rate: 0.00005 elapsed time: 21362.7 seconds (5.93 hours) Timestep: 4820000 mean reward (100 episodes): 10.1100 best mean reward: 10.1100 current episode reward: 5.0000 episodes: 1450 exploration: 0.01405 learning_rate: 0.00005 elapsed time: 21408.6 seconds (5.95 hours) Timestep: 4830000 mean reward (100 episodes): 10.8100 best mean reward: 10.8100 current episode reward: 32.0000 episodes: 1453 exploration: 0.01382 learning_rate: 0.00005 elapsed time: 21455.3 seconds (5.96 hours) Timestep: 4840000 mean reward (100 episodes): 11.9200 best mean reward: 11.9200 current episode reward: 26.0000 episodes: 1456 exploration: 0.01360 learning_rate: 0.00005 elapsed time: 21501.2 seconds (5.97 hours) Timestep: 4850000 mean reward (100 episodes): 13.5100 best mean reward: 13.5100 current episode reward: 41.0000 episodes: 1459 exploration: 0.01337 learning_rate: 0.00005 elapsed time: 21548.2 seconds (5.99 hours) Timestep: 4860000 mean reward (100 episodes): 14.6300 best mean reward: 14.6300 current episode reward: 30.0000 episodes: 1462 exploration: 0.01315 learning_rate: 0.00005 elapsed time: 21595.0 seconds (6.00 hours) Timestep: 4870000 mean reward (100 episodes): 15.1100 best mean reward: 15.1100 current episode reward: 26.0000 episodes: 1465 exploration: 0.01292 learning_rate: 0.00005 elapsed time: 21640.8 seconds (6.01 hours) Timestep: 4880000 mean reward (100 episodes): 15.7500 best mean reward: 15.7500 current episode reward: 19.0000 episodes: 1468 exploration: 0.01270 learning_rate: 0.00005 elapsed time: 21687.4 seconds (6.02 hours) Timestep: 4890000 mean reward (100 episodes): 16.5600 best mean reward: 16.5600 current episode reward: 21.0000 episodes: 1471 exploration: 0.01247 learning_rate: 0.00005 elapsed time: 21733.7 seconds (6.04 hours) Timestep: 4900000 mean reward (100 episodes): 17.3400 best mean reward: 17.3400 current episode reward: 45.0000 episodes: 1474 exploration: 0.01225 learning_rate: 0.00005 elapsed time: 21779.1 seconds (6.05 hours) Timestep: 4910000 mean reward (100 episodes): 17.8700 best mean reward: 17.8700 current episode reward: 10.0000 episodes: 1477 exploration: 0.01202 learning_rate: 0.00005 elapsed time: 21825.6 seconds (6.06 hours) Timestep: 4920000 mean reward (100 episodes): 18.7700 best mean reward: 18.7700 current episode reward: 18.0000 episodes: 1480 exploration: 0.01180 learning_rate: 0.00005 elapsed time: 21871.8 seconds (6.08 hours) Timestep: 4930000 mean reward (100 episodes): 19.3600 best mean reward: 19.3600 current episode reward: 36.0000 episodes: 1483 exploration: 0.01157 learning_rate: 0.00005 elapsed time: 21918.1 seconds (6.09 hours) Timestep: 4940000 mean reward (100 episodes): 20.0000 best mean reward: 20.0000 current episode reward: 25.0000 episodes: 1486 exploration: 0.01135 learning_rate: 0.00005 elapsed time: 21964.4 seconds (6.10 hours) Timestep: 4950000 mean reward (100 episodes): 20.8000 best mean reward: 20.8000 current episode reward: 7.0000 episodes: 1489 exploration: 0.01112 learning_rate: 0.00005 elapsed time: 22010.1 seconds (6.11 hours) Timestep: 4960000 mean reward (100 episodes): 21.3100 best mean reward: 21.3200 current episode reward: 0.0000 episodes: 1492 exploration: 0.01090 learning_rate: 0.00005 elapsed time: 22057.4 seconds (6.13 hours) Timestep: 4970000 mean reward (100 episodes): 22.1200 best mean reward: 22.1200 current episode reward: 26.0000 episodes: 1495 exploration: 0.01067 learning_rate: 0.00005 elapsed time: 22103.5 seconds (6.14 hours) Timestep: 4980000 mean reward (100 episodes): 22.7500 best mean reward: 22.7500 current episode reward: 34.0000 episodes: 1498 exploration: 0.01045 learning_rate: 0.00005 elapsed time: 22149.5 seconds (6.15 hours) Timestep: 4990000 mean reward (100 episodes): 23.3700 best mean reward: 23.3700 current episode reward: 32.0000 episodes: 1501 exploration: 0.01022 learning_rate: 0.00005 elapsed time: 22196.3 seconds (6.17 hours) Timestep: 5000000 mean reward (100 episodes): 23.7900 best mean reward: 23.7900 current episode reward: 6.0000 episodes: 1504 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22242.8 seconds (6.18 hours) Timestep: 5010000 mean reward (100 episodes): 24.5900 best mean reward: 24.5900 current episode reward: 29.0000 episodes: 1507 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22289.3 seconds (6.19 hours) Timestep: 5020000 mean reward (100 episodes): 25.3900 best mean reward: 25.3900 current episode reward: 27.0000 episodes: 1510 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22336.7 seconds (6.20 hours) Timestep: 5030000 mean reward (100 episodes): 25.7300 best mean reward: 25.7300 current episode reward: 25.0000 episodes: 1513 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22383.4 seconds (6.22 hours) Timestep: 5040000 mean reward (100 episodes): 25.7900 best mean reward: 25.7900 current episode reward: 31.0000 episodes: 1516 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22430.0 seconds (6.23 hours) Timestep: 5050000 mean reward (100 episodes): 26.1000 best mean reward: 26.1000 current episode reward: 43.0000 episodes: 1519 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22476.0 seconds (6.24 hours) Timestep: 5060000 mean reward (100 episodes): 26.0800 best mean reward: 26.1000 current episode reward: 18.0000 episodes: 1522 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22522.2 seconds (6.26 hours) Timestep: 5070000 mean reward (100 episodes): 25.8900 best mean reward: 26.2000 current episode reward: 6.0000 episodes: 1525 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22568.6 seconds (6.27 hours) Timestep: 5080000 mean reward (100 episodes): 26.1100 best mean reward: 26.2000 current episode reward: 35.0000 episodes: 1528 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22615.6 seconds (6.28 hours) Timestep: 5090000 mean reward (100 episodes): 26.0600 best mean reward: 26.2000 current episode reward: 22.0000 episodes: 1531 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22661.6 seconds (6.29 hours) Timestep: 5100000 mean reward (100 episodes): 25.7500 best mean reward: 26.2000 current episode reward: 10.0000 episodes: 1534 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22707.9 seconds (6.31 hours) Timestep: 5110000 mean reward (100 episodes): 25.6100 best mean reward: 26.2000 current episode reward: 31.0000 episodes: 1537 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22754.4 seconds (6.32 hours) Timestep: 5120000 mean reward (100 episodes): 25.1400 best mean reward: 26.2000 current episode reward: 23.0000 episodes: 1541 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22799.8 seconds (6.33 hours) Timestep: 5130000 mean reward (100 episodes): 25.5100 best mean reward: 26.2000 current episode reward: 25.0000 episodes: 1544 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22845.4 seconds (6.35 hours) Timestep: 5140000 mean reward (100 episodes): 24.8100 best mean reward: 26.2000 current episode reward: 1.0000 episodes: 1547 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22891.0 seconds (6.36 hours) Timestep: 5150000 mean reward (100 episodes): 24.8100 best mean reward: 26.2000 current episode reward: 13.0000 episodes: 1550 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 22936.1 seconds (6.37 hours) Timestep: 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hours) Timestep: 5580000 mean reward (100 episodes): 22.4800 best mean reward: 26.2000 current episode reward: 28.0000 episodes: 1679 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24922.2 seconds (6.92 hours) Timestep: 5590000 mean reward (100 episodes): 21.8900 best mean reward: 26.2000 current episode reward: 33.0000 episodes: 1682 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 24968.2 seconds (6.94 hours) Timestep: 5600000 mean reward (100 episodes): 22.5100 best mean reward: 26.2000 current episode reward: 31.0000 episodes: 1685 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25014.5 seconds (6.95 hours) Timestep: 5610000 mean reward (100 episodes): 21.9300 best mean reward: 26.2000 current episode reward: 3.0000 episodes: 1688 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25061.0 seconds (6.96 hours) Timestep: 5620000 mean reward (100 episodes): 22.0400 best mean reward: 26.2000 current episode reward: 23.0000 episodes: 1691 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25107.5 seconds (6.97 hours) Timestep: 5630000 mean reward (100 episodes): 21.1100 best mean reward: 26.2000 current episode reward: 0.0000 episodes: 1694 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25154.5 seconds (6.99 hours) Timestep: 5640000 mean reward (100 episodes): 20.5900 best mean reward: 26.2000 current episode reward: 11.0000 episodes: 1697 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25201.1 seconds (7.00 hours) Timestep: 5650000 mean reward (100 episodes): 20.9900 best mean reward: 26.2000 current episode reward: 33.0000 episodes: 1700 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25248.2 seconds (7.01 hours) Timestep: 5660000 mean reward (100 episodes): 20.5500 best mean reward: 26.2000 current episode reward: 0.0000 episodes: 1703 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 25294.5 seconds (7.03 hours) Timestep: 5670000 mean reward (100 episodes): 20.0600 best 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hours) Timestep: 8330000 mean reward (100 episodes): 26.5600 best mean reward: 29.2100 current episode reward: 38.0000 episodes: 2507 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37686.2 seconds (10.47 hours) Timestep: 8340000 mean reward (100 episodes): 26.8200 best mean reward: 29.2100 current episode reward: 14.0000 episodes: 2510 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37732.6 seconds (10.48 hours) Timestep: 8350000 mean reward (100 episodes): 26.6700 best mean reward: 29.2100 current episode reward: 8.0000 episodes: 2513 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37779.7 seconds (10.49 hours) Timestep: 8360000 mean reward (100 episodes): 26.3300 best mean reward: 29.2100 current episode reward: 41.0000 episodes: 2516 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37825.9 seconds (10.51 hours) Timestep: 8370000 mean reward (100 episodes): 26.5500 best mean reward: 29.2100 current episode reward: 18.0000 episodes: 2519 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37872.6 seconds (10.52 hours) Timestep: 8380000 mean reward (100 episodes): 26.4700 best mean reward: 29.2100 current episode reward: 14.0000 episodes: 2522 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37918.5 seconds (10.53 hours) Timestep: 8390000 mean reward (100 episodes): 26.0300 best mean reward: 29.2100 current episode reward: 8.0000 episodes: 2525 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 37964.6 seconds (10.55 hours) Timestep: 8400000 mean reward (100 episodes): 25.6100 best mean reward: 29.2100 current episode reward: 3.0000 episodes: 2528 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38010.2 seconds (10.56 hours) Timestep: 8410000 mean reward (100 episodes): 26.2800 best mean reward: 29.2100 current episode reward: 92.0000 episodes: 2531 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38057.1 seconds (10.57 hours) Timestep: 8420000 mean reward (100 episodes): 26.6000 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seconds (10.64 hours) Timestep: 8470000 mean reward (100 episodes): 25.6800 best mean reward: 29.2100 current episode reward: 21.0000 episodes: 2549 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38334.4 seconds (10.65 hours) Timestep: 8480000 mean reward (100 episodes): 25.5000 best mean reward: 29.2100 current episode reward: 0.0000 episodes: 2552 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38381.0 seconds (10.66 hours) Timestep: 8490000 mean reward (100 episodes): 25.4700 best mean reward: 29.2100 current episode reward: 21.0000 episodes: 2555 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38426.9 seconds (10.67 hours) Timestep: 8500000 mean reward (100 episodes): 25.8700 best mean reward: 29.2100 current episode reward: 43.0000 episodes: 2558 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38474.0 seconds (10.69 hours) Timestep: 8510000 mean reward (100 episodes): 25.7800 best mean reward: 29.2100 current episode reward: 20.0000 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elapsed time: 38938.0 seconds (10.82 hours) Timestep: 8610000 mean reward (100 episodes): 25.9400 best mean reward: 29.2100 current episode reward: 25.0000 episodes: 2591 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 38984.3 seconds (10.83 hours) Timestep: 8620000 mean reward (100 episodes): 26.3800 best mean reward: 29.2100 current episode reward: 42.0000 episodes: 2594 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39030.5 seconds (10.84 hours) Timestep: 8630000 mean reward (100 episodes): 26.9200 best mean reward: 29.2100 current episode reward: 25.0000 episodes: 2597 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39076.7 seconds (10.85 hours) Timestep: 8640000 mean reward (100 episodes): 26.8300 best mean reward: 29.2100 current episode reward: 10.0000 episodes: 2600 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39122.7 seconds (10.87 hours) Timestep: 8650000 mean reward (100 episodes): 26.5200 best mean reward: 29.2100 current episode reward: 38.0000 episodes: 2603 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39169.2 seconds (10.88 hours) Timestep: 8660000 mean reward (100 episodes): 26.3500 best mean reward: 29.2100 current episode reward: 29.0000 episodes: 2606 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39215.9 seconds (10.89 hours) Timestep: 8670000 mean reward (100 episodes): 26.6600 best mean reward: 29.2100 current episode reward: 48.0000 episodes: 2609 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39262.9 seconds (10.91 hours) Timestep: 8680000 mean reward (100 episodes): 26.4500 best mean reward: 29.2100 current episode reward: 9.0000 episodes: 2612 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39309.2 seconds (10.92 hours) Timestep: 8690000 mean reward (100 episodes): 26.6400 best mean reward: 29.2100 current episode reward: 1.0000 episodes: 2615 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39355.2 seconds (10.93 hours) Timestep: 8700000 mean 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episode reward: 25.0000 episodes: 2645 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39819.0 seconds (11.06 hours) Timestep: 8800000 mean reward (100 episodes): 27.7300 best mean reward: 29.2100 current episode reward: 29.0000 episodes: 2648 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39865.2 seconds (11.07 hours) Timestep: 8810000 mean reward (100 episodes): 27.7600 best mean reward: 29.2100 current episode reward: 7.0000 episodes: 2651 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39911.1 seconds (11.09 hours) Timestep: 8820000 mean reward (100 episodes): 28.0100 best mean reward: 29.2100 current episode reward: 15.0000 episodes: 2654 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 39957.6 seconds (11.10 hours) Timestep: 8830000 mean reward (100 episodes): 27.7700 best mean reward: 29.2100 current episode reward: 26.0000 episodes: 2657 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40003.7 seconds (11.11 hours) Timestep: 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hours) Timestep: 8980000 mean reward (100 episodes): 27.3100 best mean reward: 29.2100 current episode reward: 26.0000 episodes: 2702 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40697.1 seconds (11.30 hours) Timestep: 8990000 mean reward (100 episodes): 26.9900 best mean reward: 29.2100 current episode reward: 18.0000 episodes: 2705 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40744.0 seconds (11.32 hours) Timestep: 9000000 mean reward (100 episodes): 27.0300 best mean reward: 29.2100 current episode reward: 36.0000 episodes: 2708 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40790.7 seconds (11.33 hours) Timestep: 9010000 mean reward (100 episodes): 26.8100 best mean reward: 29.2100 current episode reward: 36.0000 episodes: 2711 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 40837.2 seconds (11.34 hours) Timestep: 9020000 mean reward (100 episodes): 26.9300 best mean reward: 29.2100 current episode reward: 18.0000 episodes: 2714 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0.00005 elapsed time: 42601.0 seconds (11.83 hours) Timestep: 9400000 mean reward (100 episodes): 23.9500 best mean reward: 29.2100 current episode reward: 31.0000 episodes: 2829 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42647.3 seconds (11.85 hours) Timestep: 9410000 mean reward (100 episodes): 23.7600 best mean reward: 29.2100 current episode reward: 25.0000 episodes: 2832 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42693.4 seconds (11.86 hours) Timestep: 9420000 mean reward (100 episodes): 24.0600 best mean reward: 29.2100 current episode reward: 33.0000 episodes: 2835 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42739.8 seconds (11.87 hours) Timestep: 9430000 mean reward (100 episodes): 24.0600 best mean reward: 29.2100 current episode reward: 27.0000 episodes: 2838 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42785.9 seconds (11.88 hours) Timestep: 9440000 mean reward (100 episodes): 23.8700 best mean reward: 29.2100 current episode reward: 26.0000 episodes: 2841 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42832.5 seconds (11.90 hours) Timestep: 9450000 mean reward (100 episodes): 24.1700 best mean reward: 29.2100 current episode reward: 12.0000 episodes: 2844 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42879.2 seconds (11.91 hours) Timestep: 9460000 mean reward (100 episodes): 24.3300 best mean reward: 29.2100 current episode reward: 35.0000 episodes: 2847 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42925.7 seconds (11.92 hours) Timestep: 9470000 mean reward (100 episodes): 24.5100 best mean reward: 29.2100 current episode reward: 32.0000 episodes: 2850 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 42971.6 seconds (11.94 hours) Timestep: 9480000 mean reward (100 episodes): 24.5800 best mean reward: 29.2100 current episode reward: 23.0000 episodes: 2853 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43017.8 seconds (11.95 hours) Timestep: 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learning_rate: 0.00005 elapsed time: 43249.7 seconds (12.01 hours) Timestep: 9540000 mean reward (100 episodes): 23.8400 best mean reward: 29.2100 current episode reward: 8.0000 episodes: 2871 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43296.2 seconds (12.03 hours) Timestep: 9550000 mean reward (100 episodes): 23.8900 best mean reward: 29.2100 current episode reward: 31.0000 episodes: 2874 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43342.7 seconds (12.04 hours) Timestep: 9560000 mean reward (100 episodes): 24.3900 best mean reward: 29.2100 current episode reward: 37.0000 episodes: 2877 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43389.3 seconds (12.05 hours) Timestep: 9570000 mean reward (100 episodes): 24.5400 best mean reward: 29.2100 current episode reward: 4.0000 episodes: 2880 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43436.4 seconds (12.07 hours) Timestep: 9580000 mean reward (100 episodes): 24.6300 best mean reward: 29.2100 current episode reward: 38.0000 episodes: 2883 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43483.5 seconds (12.08 hours) Timestep: 9590000 mean reward (100 episodes): 24.6500 best mean reward: 29.2100 current episode reward: 26.0000 episodes: 2886 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43530.4 seconds (12.09 hours) Timestep: 9600000 mean reward (100 episodes): 24.9500 best mean reward: 29.2100 current episode reward: 20.0000 episodes: 2889 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43576.4 seconds (12.10 hours) Timestep: 9610000 mean reward (100 episodes): 24.5300 best mean reward: 29.2100 current episode reward: 24.0000 episodes: 2892 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43622.5 seconds (12.12 hours) Timestep: 9620000 mean reward (100 episodes): 24.4900 best mean reward: 29.2100 current episode reward: 0.0000 episodes: 2895 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43668.9 seconds (12.13 hours) Timestep: 9630000 mean reward (100 episodes): 24.5300 best mean reward: 29.2100 current episode reward: 0.0000 episodes: 2898 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43714.8 seconds (12.14 hours) Timestep: 9640000 mean reward (100 episodes): 24.4500 best mean reward: 29.2100 current episode reward: 50.0000 episodes: 2901 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43760.6 seconds (12.16 hours) Timestep: 9650000 mean reward (100 episodes): 24.4100 best mean reward: 29.2100 current episode reward: 24.0000 episodes: 2904 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43806.5 seconds (12.17 hours) Timestep: 9660000 mean reward (100 episodes): 24.3400 best mean reward: 29.2100 current episode reward: 9.0000 episodes: 2907 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43853.5 seconds (12.18 hours) Timestep: 9670000 mean reward (100 episodes): 23.9000 best mean reward: 29.2100 current episode reward: 24.0000 episodes: 2910 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43899.6 seconds (12.19 hours) Timestep: 9680000 mean reward (100 episodes): 24.4600 best mean reward: 29.2100 current episode reward: 40.0000 episodes: 2913 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43945.5 seconds (12.21 hours) Timestep: 9690000 mean reward (100 episodes): 24.8700 best mean reward: 29.2100 current episode reward: 33.0000 episodes: 2916 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 43991.8 seconds (12.22 hours) Timestep: 9700000 mean reward (100 episodes): 24.8100 best mean reward: 29.2100 current episode reward: 32.0000 episodes: 2919 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44038.1 seconds (12.23 hours) Timestep: 9710000 mean reward (100 episodes): 24.7500 best mean reward: 29.2100 current episode reward: 32.0000 episodes: 2922 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44083.9 seconds (12.25 hours) Timestep: 9720000 mean reward (100 episodes): 24.4900 best mean reward: 29.2100 current episode reward: 23.0000 episodes: 2925 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44129.5 seconds (12.26 hours) Timestep: 9730000 mean reward (100 episodes): 24.2200 best mean reward: 29.2100 current episode reward: 14.0000 episodes: 2928 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44175.9 seconds (12.27 hours) Timestep: 9740000 mean reward (100 episodes): 24.2100 best mean reward: 29.2100 current episode reward: 21.0000 episodes: 2931 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44221.5 seconds (12.28 hours) Timestep: 9750000 mean reward (100 episodes): 24.4100 best mean reward: 29.2100 current episode reward: 36.0000 episodes: 2934 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44268.3 seconds (12.30 hours) Timestep: 9760000 mean reward (100 episodes): 23.8400 best mean reward: 29.2100 current episode reward: 7.0000 episodes: 2937 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44314.1 seconds (12.31 hours) Timestep: 9770000 mean reward (100 episodes): 23.7100 best mean reward: 29.2100 current episode reward: 22.0000 episodes: 2940 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44360.9 seconds (12.32 hours) Timestep: 9780000 mean reward (100 episodes): 23.4500 best mean reward: 29.2100 current episode reward: 14.0000 episodes: 2943 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44406.8 seconds (12.34 hours) Timestep: 9790000 mean reward (100 episodes): 23.2200 best mean reward: 29.2100 current episode reward: 1.0000 episodes: 2946 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44453.3 seconds (12.35 hours) Timestep: 9800000 mean reward (100 episodes): 23.0400 best mean reward: 29.2100 current episode reward: 17.0000 episodes: 2949 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44499.6 seconds (12.36 hours) Timestep: 9810000 mean reward (100 episodes): 22.7900 best mean reward: 29.2100 current episode reward: 24.0000 episodes: 2952 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44546.5 seconds (12.37 hours) Timestep: 9820000 mean reward (100 episodes): 23.3700 best mean reward: 29.2100 current episode reward: 40.0000 episodes: 2955 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44593.2 seconds (12.39 hours) Timestep: 9830000 mean reward (100 episodes): 23.1500 best mean reward: 29.2100 current episode reward: 26.0000 episodes: 2958 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44640.2 seconds (12.40 hours) Timestep: 9840000 mean reward (100 episodes): 22.6100 best mean reward: 29.2100 current episode reward: 0.0000 episodes: 2961 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44686.0 seconds (12.41 hours) Timestep: 9850000 mean reward (100 episodes): 22.4000 best mean reward: 29.2100 current episode reward: 18.0000 episodes: 2964 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44731.4 seconds (12.43 hours) Timestep: 9860000 mean reward (100 episodes): 21.7700 best mean reward: 29.2100 current episode reward: 0.0000 episodes: 2967 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44777.0 seconds (12.44 hours) Timestep: 9870000 mean reward (100 episodes): 21.7400 best mean reward: 29.2100 current episode reward: 22.0000 episodes: 2970 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44824.0 seconds (12.45 hours) Timestep: 9880000 mean reward (100 episodes): 22.4000 best mean reward: 29.2100 current episode reward: 26.0000 episodes: 2973 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44869.9 seconds (12.46 hours) Timestep: 9890000 mean reward (100 episodes): 21.8800 best mean reward: 29.2100 current episode reward: 24.0000 episodes: 2976 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44916.2 seconds (12.48 hours) Timestep: 9900000 mean reward (100 episodes): 21.0700 best mean reward: 29.2100 current episode reward: 14.0000 episodes: 2979 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 44962.6 seconds (12.49 hours) Timestep: 9910000 mean reward (100 episodes): 21.3100 best mean reward: 29.2100 current episode reward: 18.0000 episodes: 2982 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 45008.9 seconds (12.50 hours) Timestep: 9920000 mean reward (100 episodes): 20.8500 best mean reward: 29.2100 current episode reward: 18.0000 episodes: 2985 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 45054.7 seconds (12.52 hours) Timestep: 9930000 mean reward (100 episodes): 20.4600 best mean reward: 29.2100 current episode reward: 19.0000 episodes: 2988 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 45101.0 seconds (12.53 hours) Timestep: 9940000 mean reward (100 episodes): 20.7900 best mean reward: 29.2100 current episode reward: 29.0000 episodes: 2991 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 45147.8 seconds (12.54 hours) Timestep: 9950000 mean reward (100 episodes): 20.9000 best mean reward: 29.2100 current episode reward: 36.0000 episodes: 2994 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 45193.9 seconds (12.55 hours) Timestep: 9960000 mean reward (100 episodes): 21.1000 best mean reward: 29.2100 current episode reward: 38.0000 episodes: 2997 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 45239.9 seconds (12.57 hours) Timestep: 9970000 mean reward (100 episodes): 21.4500 best mean reward: 29.2100 current episode reward: 5.0000 episodes: 3000 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 45286.7 seconds (12.58 hours) Timestep: 9980000 mean reward (100 episodes): 20.6700 best mean reward: 29.2100 current episode reward: 5.0000 episodes: 3003 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 45333.4 seconds (12.59 hours) Timestep: 9983323 mean reward (100 episodes): 20.4300 best mean reward: 29.2100 current episode reward: 0.0000 episodes: 3004 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 45348.9 seconds (12.60 hours) ================================================ FILE: dqn/logs_text/Pong_s001.text ================================================ ('AVAILABLE GPUS: ', [u'device: 0, name: TITAN X (Pascal), pci bus id: 0000:01:00.0']) task = Task Timestep: 60000 mean reward (100 episodes): -20.2615 best mean reward: -inf current episode reward: -20.0000 episodes: 65 exploration: 0.94600 learning_rate: 0.00010 elapsed time: 98.4 seconds (0.03 hours) Timestep: 70000 mean reward (100 episodes): -20.2400 best mean reward: -inf current episode reward: -21.0000 episodes: 75 exploration: 0.93700 learning_rate: 0.00010 elapsed time: 130.4 seconds (0.04 hours) Timestep: 80000 mean reward (100 episodes): -20.2558 best mean reward: -inf current episode reward: -20.0000 episodes: 86 exploration: 0.92800 learning_rate: 0.00010 elapsed time: 162.7 seconds (0.05 hours) Timestep: 90000 mean reward (100 episodes): -20.2292 best mean reward: -inf current episode reward: -20.0000 episodes: 96 exploration: 0.91900 learning_rate: 0.00010 elapsed time: 194.9 seconds (0.05 hours) Timestep: 100000 mean reward (100 episodes): -20.2200 best mean reward: -20.1900 current episode reward: -21.0000 episodes: 107 exploration: 0.91000 learning_rate: 0.00010 elapsed time: 227.1 seconds (0.06 hours) Timestep: 110000 mean reward (100 episodes): -20.2500 best mean reward: -20.1900 current episode reward: -21.0000 episodes: 118 exploration: 0.90100 learning_rate: 0.00010 elapsed time: 259.4 seconds (0.07 hours) Timestep: 120000 mean reward (100 episodes): -20.2200 best mean reward: -20.1900 current episode reward: -21.0000 episodes: 129 exploration: 0.89200 learning_rate: 0.00010 elapsed time: 294.7 seconds (0.08 hours) Timestep: 130000 mean reward (100 episodes): -20.2800 best mean reward: -20.1900 current episode reward: -20.0000 episodes: 140 exploration: 0.88300 learning_rate: 0.00010 elapsed time: 327.8 seconds (0.09 hours) Timestep: 140000 mean reward (100 episodes): -20.2700 best mean reward: -20.1900 current episode reward: -20.0000 episodes: 151 exploration: 0.87400 learning_rate: 0.00010 elapsed time: 361.2 seconds (0.10 hours) Timestep: 150000 mean reward (100 episodes): -20.2700 best mean reward: -20.1900 current episode reward: -19.0000 episodes: 161 exploration: 0.86500 learning_rate: 0.00010 elapsed time: 394.5 seconds (0.11 hours) Timestep: 160000 mean reward (100 episodes): -20.3400 best mean reward: -20.1900 current episode reward: -20.0000 episodes: 172 exploration: 0.85600 learning_rate: 0.00010 elapsed time: 427.9 seconds (0.12 hours) Timestep: 170000 mean reward (100 episodes): -20.2500 best mean reward: -20.1900 current episode reward: -20.0000 episodes: 183 exploration: 0.84700 learning_rate: 0.00010 elapsed time: 461.6 seconds (0.13 hours) Timestep: 180000 mean reward (100 episodes): -20.3000 best mean reward: -20.1900 current episode reward: -21.0000 episodes: 194 exploration: 0.83800 learning_rate: 0.00010 elapsed time: 494.9 seconds (0.14 hours) Timestep: 190000 mean reward (100 episodes): -20.2900 best mean reward: -20.1900 current episode reward: -19.0000 episodes: 204 exploration: 0.82900 learning_rate: 0.00010 elapsed time: 528.8 seconds (0.15 hours) Timestep: 200000 mean reward (100 episodes): -20.2700 best mean reward: -20.1900 current episode reward: -21.0000 episodes: 215 exploration: 0.82000 learning_rate: 0.00010 elapsed time: 562.7 seconds (0.16 hours) Timestep: 210000 mean reward (100 episodes): -20.2000 best mean reward: -20.1900 current episode reward: -21.0000 episodes: 226 exploration: 0.81100 learning_rate: 0.00010 elapsed time: 600.2 seconds (0.17 hours) Timestep: 220000 mean reward (100 episodes): -20.1800 best mean reward: -20.1800 current episode reward: -21.0000 episodes: 238 exploration: 0.80200 learning_rate: 0.00010 elapsed time: 634.5 seconds (0.18 hours) Timestep: 230000 mean reward (100 episodes): -20.1600 best mean reward: -20.1600 current episode reward: -21.0000 episodes: 248 exploration: 0.79300 learning_rate: 0.00010 elapsed time: 669.0 seconds (0.19 hours) Timestep: 240000 mean reward (100 episodes): -20.1700 best mean reward: -20.1400 current episode reward: -20.0000 episodes: 259 exploration: 0.78400 learning_rate: 0.00010 elapsed time: 703.7 seconds (0.20 hours) Timestep: 250000 mean reward (100 episodes): -20.1800 best mean reward: -20.1400 current episode reward: -21.0000 episodes: 269 exploration: 0.77500 learning_rate: 0.00010 elapsed time: 738.1 seconds (0.21 hours) Timestep: 260000 mean reward (100 episodes): -20.1700 best mean reward: -20.1400 current episode reward: -21.0000 episodes: 280 exploration: 0.76600 learning_rate: 0.00010 elapsed time: 772.9 seconds (0.21 hours) Timestep: 270000 mean reward (100 episodes): -20.2300 best mean reward: -20.1400 current episode reward: -21.0000 episodes: 291 exploration: 0.75700 learning_rate: 0.00010 elapsed time: 808.1 seconds (0.22 hours) Timestep: 280000 mean reward (100 episodes): -20.2200 best mean reward: -20.1400 current episode reward: -20.0000 episodes: 302 exploration: 0.74800 learning_rate: 0.00010 elapsed time: 842.9 seconds (0.23 hours) Timestep: 290000 mean reward (100 episodes): -20.2700 best mean reward: -20.1400 current episode reward: -21.0000 episodes: 314 exploration: 0.73900 learning_rate: 0.00010 elapsed time: 877.7 seconds (0.24 hours) Timestep: 300000 mean reward (100 episodes): -20.2800 best mean reward: -20.1400 current episode reward: -20.0000 episodes: 324 exploration: 0.73000 learning_rate: 0.00010 elapsed time: 913.2 seconds (0.25 hours) Timestep: 310000 mean reward (100 episodes): -20.2900 best mean reward: -20.1400 current episode reward: -20.0000 episodes: 335 exploration: 0.72100 learning_rate: 0.00010 elapsed time: 948.4 seconds (0.26 hours) Timestep: 320000 mean reward (100 episodes): -20.3300 best mean reward: -20.1400 current episode reward: -21.0000 episodes: 346 exploration: 0.71200 learning_rate: 0.00010 elapsed time: 986.9 seconds (0.27 hours) Timestep: 330000 mean reward (100 episodes): -20.2800 best mean reward: -20.1400 current episode reward: -17.0000 episodes: 356 exploration: 0.70300 learning_rate: 0.00010 elapsed time: 1023.1 seconds (0.28 hours) Timestep: 340000 mean reward (100 episodes): -20.3000 best mean reward: -20.1400 current episode reward: -21.0000 episodes: 368 exploration: 0.69400 learning_rate: 0.00010 elapsed time: 1059.3 seconds (0.29 hours) Timestep: 350000 mean reward (100 episodes): -20.3700 best mean reward: -20.1400 current episode reward: -21.0000 episodes: 379 exploration: 0.68500 learning_rate: 0.00010 elapsed time: 1095.2 seconds (0.30 hours) Timestep: 360000 mean reward (100 episodes): -20.3500 best mean reward: -20.1400 current episode reward: -21.0000 episodes: 390 exploration: 0.67600 learning_rate: 0.00010 elapsed time: 1131.1 seconds (0.31 hours) Timestep: 370000 mean reward (100 episodes): -20.3200 best mean reward: -20.1400 current episode reward: -20.0000 episodes: 401 exploration: 0.66700 learning_rate: 0.00010 elapsed time: 1167.6 seconds (0.32 hours) Timestep: 380000 mean reward (100 episodes): -20.3400 best mean reward: -20.1400 current episode reward: -21.0000 episodes: 412 exploration: 0.65800 learning_rate: 0.00010 elapsed time: 1203.7 seconds (0.33 hours) Timestep: 390000 mean reward (100 episodes): -20.3400 best mean reward: -20.1400 current episode reward: -19.0000 episodes: 423 exploration: 0.64900 learning_rate: 0.00010 elapsed time: 1240.2 seconds (0.34 hours) Timestep: 400000 mean reward (100 episodes): -20.3300 best mean reward: -20.1400 current episode reward: -20.0000 episodes: 433 exploration: 0.64000 learning_rate: 0.00010 elapsed time: 1276.6 seconds (0.35 hours) Timestep: 410000 mean reward (100 episodes): -20.2900 best mean reward: -20.1400 current episode reward: -20.0000 episodes: 442 exploration: 0.63100 learning_rate: 0.00010 elapsed time: 1313.2 seconds (0.36 hours) Timestep: 420000 mean reward (100 episodes): -20.2500 best mean reward: -20.1400 current episode reward: -21.0000 episodes: 451 exploration: 0.62200 learning_rate: 0.00010 elapsed time: 1349.3 seconds (0.37 hours) Timestep: 430000 mean reward (100 episodes): -20.1000 best mean reward: -20.1000 current episode reward: -16.0000 episodes: 459 exploration: 0.61300 learning_rate: 0.00010 elapsed time: 1386.0 seconds (0.38 hours) Timestep: 440000 mean reward (100 episodes): -19.9700 best mean reward: -19.9700 current episode reward: -18.0000 episodes: 468 exploration: 0.60400 learning_rate: 0.00010 elapsed time: 1423.3 seconds (0.40 hours) Timestep: 450000 mean reward (100 episodes): -19.8700 best mean reward: -19.8700 current episode reward: -20.0000 episodes: 476 exploration: 0.59500 learning_rate: 0.00010 elapsed time: 1460.4 seconds (0.41 hours) Timestep: 460000 mean reward (100 episodes): -19.6200 best mean reward: -19.6200 current episode reward: -20.0000 episodes: 484 exploration: 0.58600 learning_rate: 0.00010 elapsed time: 1497.6 seconds (0.42 hours) Timestep: 470000 mean reward (100 episodes): -19.4500 best mean reward: -19.4500 current episode reward: -18.0000 episodes: 491 exploration: 0.57700 learning_rate: 0.00010 elapsed time: 1535.5 seconds (0.43 hours) Timestep: 480000 mean reward (100 episodes): -19.3100 best mean reward: -19.3100 current episode reward: -20.0000 episodes: 498 exploration: 0.56800 learning_rate: 0.00010 elapsed time: 1572.5 seconds (0.44 hours) Timestep: 490000 mean reward (100 episodes): -19.2200 best mean reward: -19.2200 current episode reward: -18.0000 episodes: 505 exploration: 0.55900 learning_rate: 0.00010 elapsed time: 1609.6 seconds (0.45 hours) Timestep: 500000 mean reward (100 episodes): -19.0300 best mean reward: -19.0300 current episode reward: -16.0000 episodes: 512 exploration: 0.55000 learning_rate: 0.00010 elapsed time: 1650.4 seconds (0.46 hours) Timestep: 510000 mean reward (100 episodes): -18.9200 best mean reward: -18.9200 current episode reward: -16.0000 episodes: 519 exploration: 0.54100 learning_rate: 0.00010 elapsed time: 1690.2 seconds (0.47 hours) Timestep: 520000 mean reward (100 episodes): -18.7500 best mean reward: -18.7500 current episode reward: -15.0000 episodes: 526 exploration: 0.53200 learning_rate: 0.00010 elapsed time: 1728.6 seconds (0.48 hours) Timestep: 530000 mean reward (100 episodes): -18.5600 best mean reward: -18.5600 current episode reward: -18.0000 episodes: 533 exploration: 0.52300 learning_rate: 0.00010 elapsed time: 1766.9 seconds (0.49 hours) Timestep: 540000 mean reward (100 episodes): -18.4100 best mean reward: -18.4100 current episode reward: -15.0000 episodes: 540 exploration: 0.51400 learning_rate: 0.00010 elapsed time: 1805.1 seconds (0.50 hours) Timestep: 550000 mean reward (100 episodes): -18.2500 best mean reward: -18.2500 current episode reward: -18.0000 episodes: 546 exploration: 0.50500 learning_rate: 0.00010 elapsed time: 1844.0 seconds (0.51 hours) Timestep: 560000 mean reward (100 episodes): -18.0900 best mean reward: -18.0900 current episode reward: -18.0000 episodes: 553 exploration: 0.49600 learning_rate: 0.00010 elapsed time: 1882.6 seconds (0.52 hours) Timestep: 570000 mean reward (100 episodes): -18.0600 best mean reward: -18.0600 current episode reward: -15.0000 episodes: 559 exploration: 0.48700 learning_rate: 0.00010 elapsed time: 1921.0 seconds (0.53 hours) Timestep: 580000 mean reward (100 episodes): -17.9000 best mean reward: -17.9000 current episode reward: -15.0000 episodes: 565 exploration: 0.47800 learning_rate: 0.00010 elapsed time: 1960.6 seconds (0.54 hours) Timestep: 590000 mean reward (100 episodes): -17.8300 best mean reward: -17.8000 current episode reward: -20.0000 episodes: 571 exploration: 0.46900 learning_rate: 0.00010 elapsed time: 2000.0 seconds (0.56 hours) Timestep: 600000 mean reward (100 episodes): -17.6700 best mean reward: -17.6700 current episode reward: -17.0000 episodes: 577 exploration: 0.46000 learning_rate: 0.00010 elapsed time: 2039.2 seconds (0.57 hours) Timestep: 610000 mean reward (100 episodes): -17.7300 best mean reward: -17.6500 current episode reward: -21.0000 episodes: 584 exploration: 0.45100 learning_rate: 0.00010 elapsed time: 2078.7 seconds (0.58 hours) Timestep: 620000 mean reward (100 episodes): -17.7000 best mean reward: -17.6500 current episode reward: -19.0000 episodes: 590 exploration: 0.44200 learning_rate: 0.00010 elapsed time: 2118.7 seconds (0.59 hours) Timestep: 630000 mean reward (100 episodes): -17.6400 best mean reward: -17.6300 current episode reward: -19.0000 episodes: 595 exploration: 0.43300 learning_rate: 0.00010 elapsed time: 2157.7 seconds (0.60 hours) Timestep: 640000 mean reward (100 episodes): -17.5000 best mean reward: -17.5000 current episode reward: -16.0000 episodes: 600 exploration: 0.42400 learning_rate: 0.00010 elapsed time: 2197.3 seconds (0.61 hours) Timestep: 650000 mean reward (100 episodes): -17.4200 best mean reward: -17.4100 current episode reward: -19.0000 episodes: 605 exploration: 0.41500 learning_rate: 0.00010 elapsed time: 2237.1 seconds (0.62 hours) Timestep: 660000 mean reward (100 episodes): -17.3800 best mean reward: -17.3600 current episode reward: -20.0000 episodes: 611 exploration: 0.40600 learning_rate: 0.00010 elapsed time: 2276.6 seconds (0.63 hours) Timestep: 670000 mean reward (100 episodes): -17.3400 best mean reward: -17.3400 current episode reward: -18.0000 episodes: 617 exploration: 0.39700 learning_rate: 0.00010 elapsed time: 2316.7 seconds (0.64 hours) Timestep: 680000 mean reward (100 episodes): -17.2900 best mean reward: -17.2900 current episode reward: -18.0000 episodes: 623 exploration: 0.38800 learning_rate: 0.00010 elapsed time: 2356.8 seconds (0.65 hours) Timestep: 690000 mean reward (100 episodes): -17.3300 best mean reward: -17.2900 current episode reward: -17.0000 episodes: 628 exploration: 0.37900 learning_rate: 0.00010 elapsed time: 2396.9 seconds (0.67 hours) Timestep: 700000 mean reward (100 episodes): -17.2600 best mean reward: -17.2600 current episode reward: -15.0000 episodes: 634 exploration: 0.37000 learning_rate: 0.00010 elapsed time: 2437.1 seconds (0.68 hours) Timestep: 710000 mean reward (100 episodes): -17.2200 best mean reward: -17.2200 current episode reward: -19.0000 episodes: 639 exploration: 0.36100 learning_rate: 0.00010 elapsed time: 2477.0 seconds (0.69 hours) Timestep: 720000 mean reward (100 episodes): -17.1900 best mean reward: -17.1900 current episode reward: -16.0000 episodes: 643 exploration: 0.35200 learning_rate: 0.00010 elapsed time: 2518.1 seconds (0.70 hours) Timestep: 730000 mean reward (100 episodes): -17.0600 best mean reward: -17.0600 current episode reward: -14.0000 episodes: 648 exploration: 0.34300 learning_rate: 0.00010 elapsed time: 2558.4 seconds (0.71 hours) Timestep: 740000 mean reward (100 episodes): -17.0600 best mean reward: -17.0400 current episode reward: -16.0000 episodes: 654 exploration: 0.33400 learning_rate: 0.00010 elapsed time: 2598.7 seconds (0.72 hours) Timestep: 750000 mean reward (100 episodes): -17.0000 best mean reward: -16.9800 current episode reward: -14.0000 episodes: 659 exploration: 0.32500 learning_rate: 0.00010 elapsed time: 2640.3 seconds (0.73 hours) Timestep: 760000 mean reward (100 episodes): -16.9100 best mean reward: -16.9100 current episode reward: -14.0000 episodes: 663 exploration: 0.31600 learning_rate: 0.00010 elapsed time: 2682.0 seconds (0.74 hours) Timestep: 770000 mean reward (100 episodes): -16.7600 best mean reward: -16.7500 current episode reward: -19.0000 episodes: 668 exploration: 0.30700 learning_rate: 0.00010 elapsed time: 2722.9 seconds (0.76 hours) Timestep: 780000 mean reward (100 episodes): -16.6200 best mean reward: -16.6200 current episode reward: -11.0000 episodes: 673 exploration: 0.29800 learning_rate: 0.00010 elapsed time: 2764.8 seconds (0.77 hours) Timestep: 790000 mean reward (100 episodes): -16.5900 best mean reward: -16.5900 current episode reward: -13.0000 episodes: 678 exploration: 0.28900 learning_rate: 0.00010 elapsed time: 2806.1 seconds (0.78 hours) Timestep: 800000 mean reward (100 episodes): -16.4800 best mean reward: -16.4800 current episode reward: -18.0000 episodes: 682 exploration: 0.28000 learning_rate: 0.00010 elapsed time: 2847.9 seconds (0.79 hours) Timestep: 810000 mean reward (100 episodes): -16.3700 best mean reward: -16.3600 current episode reward: -17.0000 episodes: 687 exploration: 0.27100 learning_rate: 0.00010 elapsed time: 2889.8 seconds (0.80 hours) Timestep: 820000 mean reward (100 episodes): -16.3800 best mean reward: -16.3600 current episode reward: -18.0000 episodes: 692 exploration: 0.26200 learning_rate: 0.00010 elapsed time: 2931.4 seconds (0.81 hours) Timestep: 830000 mean reward (100 episodes): -16.3100 best mean reward: -16.3100 current episode reward: -14.0000 episodes: 696 exploration: 0.25300 learning_rate: 0.00010 elapsed time: 2973.4 seconds (0.83 hours) Timestep: 840000 mean reward (100 episodes): -16.1900 best mean reward: -16.1900 current episode reward: -16.0000 episodes: 700 exploration: 0.24400 learning_rate: 0.00010 elapsed time: 3014.4 seconds (0.84 hours) Timestep: 850000 mean reward (100 episodes): -16.0900 best mean reward: -16.0900 current episode reward: -15.0000 episodes: 705 exploration: 0.23500 learning_rate: 0.00010 elapsed time: 3056.5 seconds (0.85 hours) Timestep: 860000 mean reward (100 episodes): -15.9200 best mean reward: -15.9200 current episode reward: -11.0000 episodes: 708 exploration: 0.22600 learning_rate: 0.00010 elapsed time: 3098.8 seconds (0.86 hours) Timestep: 870000 mean reward (100 episodes): -15.7400 best mean reward: -15.7400 current episode reward: -13.0000 episodes: 713 exploration: 0.21700 learning_rate: 0.00010 elapsed time: 3140.5 seconds (0.87 hours) Timestep: 880000 mean reward (100 episodes): -15.6300 best mean reward: -15.6300 current episode reward: -13.0000 episodes: 716 exploration: 0.20800 learning_rate: 0.00010 elapsed time: 3182.4 seconds (0.88 hours) Timestep: 890000 mean reward (100 episodes): -15.5100 best mean reward: -15.5100 current episode reward: -18.0000 episodes: 721 exploration: 0.19900 learning_rate: 0.00010 elapsed time: 3224.9 seconds (0.90 hours) Timestep: 900000 mean reward (100 episodes): -15.3100 best mean reward: -15.3100 current episode reward: -11.0000 episodes: 724 exploration: 0.19000 learning_rate: 0.00010 elapsed time: 3266.8 seconds (0.91 hours) Timestep: 910000 mean reward (100 episodes): -15.1100 best mean reward: -15.1100 current episode reward: -12.0000 episodes: 729 exploration: 0.18100 learning_rate: 0.00010 elapsed time: 3311.2 seconds (0.92 hours) Timestep: 920000 mean reward (100 episodes): -15.0200 best mean reward: -15.0200 current episode reward: -13.0000 episodes: 732 exploration: 0.17200 learning_rate: 0.00010 elapsed time: 3359.3 seconds (0.93 hours) Timestep: 930000 mean reward (100 episodes): -14.8900 best mean reward: -14.8900 current episode reward: -15.0000 episodes: 736 exploration: 0.16300 learning_rate: 0.00010 elapsed time: 3402.5 seconds (0.95 hours) Timestep: 940000 mean reward (100 episodes): -14.5900 best mean reward: -14.5900 current episode reward: -15.0000 episodes: 739 exploration: 0.15400 learning_rate: 0.00010 elapsed time: 3445.6 seconds (0.96 hours) Timestep: 950000 mean reward (100 episodes): -14.4800 best mean reward: -14.4800 current episode reward: -14.0000 episodes: 743 exploration: 0.14500 learning_rate: 0.00010 elapsed time: 3489.1 seconds (0.97 hours) Timestep: 960000 mean reward (100 episodes): -14.4100 best mean reward: -14.4100 current episode reward: -12.0000 episodes: 746 exploration: 0.13600 learning_rate: 0.00010 elapsed time: 3533.8 seconds (0.98 hours) Timestep: 970000 mean reward (100 episodes): -14.2600 best mean reward: -14.2600 current episode reward: -7.0000 episodes: 750 exploration: 0.12700 learning_rate: 0.00010 elapsed time: 3578.1 seconds (0.99 hours) Timestep: 980000 mean reward (100 episodes): -14.1300 best mean reward: -14.1100 current episode reward: -18.0000 episodes: 754 exploration: 0.11800 learning_rate: 0.00010 elapsed time: 3622.3 seconds (1.01 hours) Timestep: 990000 mean reward (100 episodes): -13.8400 best mean reward: -13.8400 current episode reward: -6.0000 episodes: 757 exploration: 0.10900 learning_rate: 0.00010 elapsed time: 3666.4 seconds (1.02 hours) Timestep: 1000000 mean reward (100 episodes): -13.6200 best mean reward: -13.6200 current episode reward: -8.0000 episodes: 760 exploration: 0.10000 learning_rate: 0.00010 elapsed time: 3710.5 seconds (1.03 hours) Timestep: 1010000 mean reward (100 episodes): -13.5500 best mean reward: -13.5500 current episode reward: -8.0000 episodes: 764 exploration: 0.09967 learning_rate: 0.00010 elapsed time: 3754.1 seconds (1.04 hours) Timestep: 1020000 mean reward (100 episodes): -13.4800 best mean reward: -13.4100 current episode reward: -16.0000 episodes: 767 exploration: 0.09935 learning_rate: 0.00010 elapsed time: 3797.2 seconds (1.05 hours) Timestep: 1030000 mean reward (100 episodes): -13.2500 best mean reward: -13.2500 current episode reward: -13.0000 episodes: 771 exploration: 0.09902 learning_rate: 0.00010 elapsed time: 3840.5 seconds (1.07 hours) Timestep: 1040000 mean reward (100 episodes): -13.1700 best mean reward: -13.1700 current episode reward: -10.0000 episodes: 774 exploration: 0.09869 learning_rate: 0.00010 elapsed time: 3884.7 seconds (1.08 hours) Timestep: 1050000 mean reward (100 episodes): -12.8800 best mean reward: -12.8800 current episode reward: -7.0000 episodes: 777 exploration: 0.09836 learning_rate: 0.00010 elapsed time: 3929.2 seconds (1.09 hours) Timestep: 1060000 mean reward (100 episodes): -12.7500 best mean reward: -12.7500 current episode reward: -7.0000 episodes: 781 exploration: 0.09804 learning_rate: 0.00009 elapsed time: 3972.4 seconds (1.10 hours) Timestep: 1070000 mean reward (100 episodes): -12.5600 best mean reward: -12.5600 current episode reward: -10.0000 episodes: 784 exploration: 0.09771 learning_rate: 0.00009 elapsed time: 4015.6 seconds (1.12 hours) Timestep: 1080000 mean reward (100 episodes): -12.3900 best mean reward: -12.3900 current episode reward: -5.0000 episodes: 788 exploration: 0.09738 learning_rate: 0.00009 elapsed time: 4060.2 seconds (1.13 hours) Timestep: 1090000 mean reward (100 episodes): -12.1000 best mean reward: -12.1000 current episode reward: -7.0000 episodes: 790 exploration: 0.09705 learning_rate: 0.00009 elapsed time: 4104.7 seconds (1.14 hours) Timestep: 1100000 mean reward (100 episodes): -12.1100 best mean reward: -12.0200 current episode reward: -17.0000 episodes: 794 exploration: 0.09673 learning_rate: 0.00009 elapsed time: 4148.5 seconds (1.15 hours) Timestep: 1110000 mean reward (100 episodes): -11.9500 best mean reward: -11.9500 current episode reward: -15.0000 episodes: 797 exploration: 0.09640 learning_rate: 0.00009 elapsed time: 4192.9 seconds (1.16 hours) Timestep: 1120000 mean reward (100 episodes): -11.6800 best mean reward: -11.6800 current episode reward: -13.0000 episodes: 801 exploration: 0.09607 learning_rate: 0.00009 elapsed time: 4236.7 seconds (1.18 hours) Timestep: 1130000 mean reward (100 episodes): -11.5200 best mean reward: -11.5200 current episode reward: -9.0000 episodes: 804 exploration: 0.09575 learning_rate: 0.00009 elapsed time: 4281.1 seconds (1.19 hours) Timestep: 1140000 mean reward (100 episodes): -11.4200 best mean reward: -11.4100 current episode reward: -12.0000 episodes: 808 exploration: 0.09542 learning_rate: 0.00009 elapsed time: 4325.7 seconds (1.20 hours) Timestep: 1150000 mean reward (100 episodes): -11.2700 best mean reward: -11.2300 current episode reward: -13.0000 episodes: 811 exploration: 0.09509 learning_rate: 0.00009 elapsed time: 4369.6 seconds (1.21 hours) Timestep: 1160000 mean reward (100 episodes): -10.9600 best mean reward: -10.9600 current episode reward: -14.0000 episodes: 815 exploration: 0.09476 learning_rate: 0.00009 elapsed time: 4413.2 seconds (1.23 hours) Timestep: 1170000 mean reward (100 episodes): -10.7600 best mean reward: -10.7600 current episode reward: -10.0000 episodes: 818 exploration: 0.09444 learning_rate: 0.00009 elapsed time: 4457.1 seconds (1.24 hours) Timestep: 1180000 mean reward (100 episodes): -10.3600 best mean reward: -10.3600 current episode reward: -6.0000 episodes: 821 exploration: 0.09411 learning_rate: 0.00009 elapsed time: 4501.9 seconds (1.25 hours) Timestep: 1190000 mean reward (100 episodes): -10.2300 best mean reward: -10.2300 current episode reward: -9.0000 episodes: 824 exploration: 0.09378 learning_rate: 0.00009 elapsed time: 4545.4 seconds (1.26 hours) Timestep: 1200000 mean reward (100 episodes): -10.0500 best mean reward: -10.0500 current episode reward: -10.0000 episodes: 827 exploration: 0.09345 learning_rate: 0.00009 elapsed time: 4589.3 seconds (1.27 hours) Timestep: 1210000 mean reward (100 episodes): -9.6900 best mean reward: -9.6900 current episode reward: -6.0000 episodes: 830 exploration: 0.09313 learning_rate: 0.00009 elapsed time: 4633.0 seconds (1.29 hours) Timestep: 1220000 mean reward (100 episodes): -9.5100 best mean reward: -9.5100 current episode reward: -8.0000 episodes: 833 exploration: 0.09280 learning_rate: 0.00009 elapsed time: 4677.2 seconds (1.30 hours) Timestep: 1230000 mean reward (100 episodes): -9.1200 best mean reward: -9.1200 current episode reward: -4.0000 episodes: 836 exploration: 0.09247 learning_rate: 0.00009 elapsed time: 4721.1 seconds (1.31 hours) Timestep: 1240000 mean reward (100 episodes): -9.0700 best mean reward: -9.0700 current episode reward: -9.0000 episodes: 839 exploration: 0.09215 learning_rate: 0.00009 elapsed time: 4765.0 seconds (1.32 hours) Timestep: 1250000 mean reward (100 episodes): -8.8200 best mean reward: -8.8200 current episode reward: -4.0000 episodes: 842 exploration: 0.09182 learning_rate: 0.00009 elapsed time: 4809.2 seconds (1.34 hours) Timestep: 1260000 mean reward (100 episodes): -8.5100 best mean reward: -8.5100 current episode reward: -4.0000 episodes: 845 exploration: 0.09149 learning_rate: 0.00009 elapsed time: 4853.6 seconds (1.35 hours) Timestep: 1270000 mean reward (100 episodes): -8.1100 best mean reward: -8.1100 current episode reward: -4.0000 episodes: 848 exploration: 0.09116 learning_rate: 0.00009 elapsed time: 4898.0 seconds (1.36 hours) Timestep: 1280000 mean reward (100 episodes): -8.0200 best mean reward: -7.9900 current episode reward: -8.0000 episodes: 851 exploration: 0.09084 learning_rate: 0.00009 elapsed time: 4942.5 seconds (1.37 hours) Timestep: 1290000 mean reward (100 episodes): -7.6800 best mean reward: -7.6800 current episode reward: -9.0000 episodes: 855 exploration: 0.09051 learning_rate: 0.00009 elapsed time: 4986.8 seconds (1.39 hours) Timestep: 1300000 mean reward (100 episodes): -7.5000 best mean reward: -7.5000 current episode reward: -1.0000 episodes: 858 exploration: 0.09018 learning_rate: 0.00009 elapsed time: 5030.8 seconds (1.40 hours) Timestep: 1310000 mean reward (100 episodes): -7.2400 best mean reward: -7.2400 current episode reward: 2.0000 episodes: 861 exploration: 0.08985 learning_rate: 0.00009 elapsed time: 5074.7 seconds (1.41 hours) Timestep: 1320000 mean reward (100 episodes): -6.9200 best mean reward: -6.9200 current episode reward: -8.0000 episodes: 864 exploration: 0.08953 learning_rate: 0.00009 elapsed time: 5118.6 seconds (1.42 hours) Timestep: 1330000 mean reward (100 episodes): -6.6400 best mean reward: -6.6400 current episode reward: 2.0000 episodes: 866 exploration: 0.08920 learning_rate: 0.00009 elapsed time: 5162.3 seconds (1.43 hours) Timestep: 1340000 mean reward (100 episodes): -6.3500 best mean reward: -6.3500 current episode reward: 3.0000 episodes: 869 exploration: 0.08887 learning_rate: 0.00009 elapsed time: 5205.3 seconds (1.45 hours) Timestep: 1350000 mean reward (100 episodes): -5.9900 best mean reward: -5.9900 current episode reward: -6.0000 episodes: 872 exploration: 0.08855 learning_rate: 0.00009 elapsed time: 5249.1 seconds (1.46 hours) Timestep: 1360000 mean reward (100 episodes): -5.6800 best mean reward: -5.6800 current episode reward: -1.0000 episodes: 875 exploration: 0.08822 learning_rate: 0.00009 elapsed time: 5292.9 seconds (1.47 hours) Timestep: 1370000 mean reward (100 episodes): -5.5100 best mean reward: -5.5100 current episode reward: -3.0000 episodes: 878 exploration: 0.08789 learning_rate: 0.00009 elapsed time: 5336.1 seconds (1.48 hours) Timestep: 1380000 mean reward (100 episodes): -5.0900 best mean reward: -5.0900 current episode reward: 3.0000 episodes: 881 exploration: 0.08756 learning_rate: 0.00009 elapsed time: 5380.3 seconds (1.49 hours) Timestep: 1390000 mean reward (100 episodes): -4.8900 best mean reward: -4.8900 current episode reward: 2.0000 episodes: 883 exploration: 0.08724 learning_rate: 0.00009 elapsed time: 5425.3 seconds (1.51 hours) Timestep: 1400000 mean reward (100 episodes): -4.6300 best mean reward: -4.6300 current episode reward: -6.0000 episodes: 886 exploration: 0.08691 learning_rate: 0.00009 elapsed time: 5469.4 seconds (1.52 hours) Timestep: 1410000 mean reward (100 episodes): -4.4200 best mean reward: -4.4200 current episode reward: 5.0000 episodes: 889 exploration: 0.08658 learning_rate: 0.00009 elapsed time: 5513.0 seconds (1.53 hours) Timestep: 1420000 mean reward (100 episodes): -3.9400 best mean reward: -3.9400 current episode reward: 4.0000 episodes: 892 exploration: 0.08625 learning_rate: 0.00009 elapsed time: 5556.7 seconds (1.54 hours) Timestep: 1430000 mean reward (100 episodes): -3.3400 best mean reward: -3.3400 current episode reward: 2.0000 episodes: 895 exploration: 0.08593 learning_rate: 0.00009 elapsed time: 5601.0 seconds (1.56 hours) Timestep: 1440000 mean reward (100 episodes): -3.0100 best mean reward: -3.0100 current episode reward: 4.0000 episodes: 897 exploration: 0.08560 learning_rate: 0.00009 elapsed time: 5644.1 seconds (1.57 hours) Timestep: 1450000 mean reward (100 episodes): -2.9500 best mean reward: -2.9500 current episode reward: -5.0000 episodes: 900 exploration: 0.08527 learning_rate: 0.00009 elapsed time: 5687.8 seconds (1.58 hours) Timestep: 1460000 mean reward (100 episodes): -2.5100 best mean reward: -2.5100 current episode reward: 1.0000 episodes: 903 exploration: 0.08495 learning_rate: 0.00009 elapsed time: 5732.0 seconds (1.59 hours) Timestep: 1470000 mean reward (100 episodes): -2.0500 best mean reward: -2.0500 current episode reward: 2.0000 episodes: 906 exploration: 0.08462 learning_rate: 0.00009 elapsed time: 5775.9 seconds (1.60 hours) Timestep: 1480000 mean reward (100 episodes): -1.5900 best mean reward: -1.5900 current episode reward: 4.0000 episodes: 909 exploration: 0.08429 learning_rate: 0.00009 elapsed time: 5819.8 seconds (1.62 hours) Timestep: 1490000 mean reward (100 episodes): -1.1600 best mean reward: -1.1600 current episode reward: 5.0000 episodes: 912 exploration: 0.08396 learning_rate: 0.00009 elapsed time: 5863.7 seconds (1.63 hours) Timestep: 1500000 mean reward (100 episodes): -0.7200 best mean reward: -0.7200 current episode reward: 8.0000 episodes: 915 exploration: 0.08364 learning_rate: 0.00009 elapsed time: 5907.2 seconds (1.64 hours) Timestep: 1510000 mean reward (100 episodes): -0.1000 best mean reward: -0.1000 current episode reward: 8.0000 episodes: 919 exploration: 0.08331 learning_rate: 0.00009 elapsed time: 5951.1 seconds (1.65 hours) Timestep: 1520000 mean reward (100 episodes): 0.3800 best mean reward: 0.3800 current episode reward: 8.0000 episodes: 922 exploration: 0.08298 learning_rate: 0.00009 elapsed time: 5995.1 seconds (1.67 hours) Timestep: 1530000 mean reward (100 episodes): 0.8000 best mean reward: 0.8000 current episode reward: 9.0000 episodes: 926 exploration: 0.08265 learning_rate: 0.00009 elapsed time: 6038.7 seconds (1.68 hours) Timestep: 1540000 mean reward (100 episodes): 1.0400 best mean reward: 1.0400 current episode reward: 8.0000 episodes: 929 exploration: 0.08233 learning_rate: 0.00009 elapsed time: 6082.0 seconds (1.69 hours) Timestep: 1550000 mean reward (100 episodes): 1.3500 best mean reward: 1.3500 current episode reward: 6.0000 episodes: 932 exploration: 0.08200 learning_rate: 0.00009 elapsed time: 6125.9 seconds (1.70 hours) Timestep: 1560000 mean reward (100 episodes): 1.6700 best mean reward: 1.6700 current episode reward: 8.0000 episodes: 936 exploration: 0.08167 learning_rate: 0.00009 elapsed time: 6170.6 seconds (1.71 hours) Timestep: 1570000 mean reward (100 episodes): 2.1200 best mean reward: 2.1200 current episode reward: 9.0000 episodes: 939 exploration: 0.08135 learning_rate: 0.00009 elapsed time: 6213.6 seconds (1.73 hours) Timestep: 1580000 mean reward (100 episodes): 2.6800 best mean reward: 2.6800 current episode reward: 9.0000 episodes: 943 exploration: 0.08102 learning_rate: 0.00009 elapsed time: 6256.8 seconds (1.74 hours) Timestep: 1590000 mean reward (100 episodes): 3.1300 best mean reward: 3.1300 current episode reward: 12.0000 episodes: 946 exploration: 0.08069 learning_rate: 0.00009 elapsed time: 6300.1 seconds (1.75 hours) Timestep: 1600000 mean reward (100 episodes): 3.7400 best mean reward: 3.7400 current episode reward: 15.0000 episodes: 950 exploration: 0.08036 learning_rate: 0.00009 elapsed time: 6343.8 seconds (1.76 hours) Timestep: 1610000 mean reward (100 episodes): 4.5100 best mean reward: 4.5100 current episode reward: 18.0000 episodes: 954 exploration: 0.08004 learning_rate: 0.00009 elapsed time: 6387.9 seconds (1.77 hours) Timestep: 1620000 mean reward (100 episodes): 5.1300 best mean reward: 5.1300 current episode reward: 19.0000 episodes: 958 exploration: 0.07971 learning_rate: 0.00009 elapsed time: 6432.3 seconds (1.79 hours) Timestep: 1630000 mean reward (100 episodes): 5.6500 best mean reward: 5.6500 current episode reward: 12.0000 episodes: 962 exploration: 0.07938 learning_rate: 0.00009 elapsed time: 6476.4 seconds (1.80 hours) Timestep: 1640000 mean reward (100 episodes): 6.0700 best mean reward: 6.0700 current episode reward: 13.0000 episodes: 966 exploration: 0.07905 learning_rate: 0.00009 elapsed time: 6519.6 seconds (1.81 hours) Timestep: 1650000 mean reward (100 episodes): 6.6500 best mean reward: 6.6500 current episode reward: 13.0000 episodes: 970 exploration: 0.07873 learning_rate: 0.00009 elapsed time: 6563.3 seconds (1.82 hours) Timestep: 1660000 mean reward (100 episodes): 7.2700 best mean reward: 7.2700 current episode reward: 14.0000 episodes: 974 exploration: 0.07840 learning_rate: 0.00008 elapsed time: 6607.0 seconds (1.84 hours) Timestep: 1670000 mean reward (100 episodes): 8.2200 best mean reward: 8.2200 current episode reward: 19.0000 episodes: 979 exploration: 0.07807 learning_rate: 0.00008 elapsed time: 6650.6 seconds (1.85 hours) Timestep: 1680000 mean reward (100 episodes): 8.7300 best mean reward: 8.7300 current episode reward: 16.0000 episodes: 983 exploration: 0.07775 learning_rate: 0.00008 elapsed time: 6694.5 seconds (1.86 hours) Timestep: 1690000 mean reward (100 episodes): 9.3600 best mean reward: 9.3600 current episode reward: 15.0000 episodes: 987 exploration: 0.07742 learning_rate: 0.00008 elapsed time: 6738.8 seconds (1.87 hours) Timestep: 1700000 mean reward (100 episodes): 9.9300 best mean reward: 9.9300 current episode reward: 17.0000 episodes: 991 exploration: 0.07709 learning_rate: 0.00008 elapsed time: 6782.7 seconds (1.88 hours) Timestep: 1710000 mean reward (100 episodes): 10.4600 best mean reward: 10.4600 current episode reward: 16.0000 episodes: 996 exploration: 0.07676 learning_rate: 0.00008 elapsed time: 6827.5 seconds (1.90 hours) Timestep: 1720000 mean reward (100 episodes): 11.0900 best mean reward: 11.0900 current episode reward: 16.0000 episodes: 1000 exploration: 0.07644 learning_rate: 0.00008 elapsed time: 6875.3 seconds (1.91 hours) Timestep: 1730000 mean reward (100 episodes): 11.7200 best mean reward: 11.7200 current episode reward: 21.0000 episodes: 1005 exploration: 0.07611 learning_rate: 0.00008 elapsed time: 6921.5 seconds (1.92 hours) Timestep: 1740000 mean reward (100 episodes): 12.2400 best mean reward: 12.2400 current episode reward: 18.0000 episodes: 1010 exploration: 0.07578 learning_rate: 0.00008 elapsed time: 6964.8 seconds (1.93 hours) Timestep: 1750000 mean reward (100 episodes): 12.6400 best mean reward: 12.6400 current episode reward: 17.0000 episodes: 1014 exploration: 0.07545 learning_rate: 0.00008 elapsed time: 7008.9 seconds (1.95 hours) Timestep: 1760000 mean reward (100 episodes): 13.0700 best mean reward: 13.0700 current episode reward: 18.0000 episodes: 1019 exploration: 0.07513 learning_rate: 0.00008 elapsed time: 7052.6 seconds (1.96 hours) Timestep: 1770000 mean reward (100 episodes): 13.4000 best mean reward: 13.4000 current episode reward: 20.0000 episodes: 1024 exploration: 0.07480 learning_rate: 0.00008 elapsed time: 7097.0 seconds (1.97 hours) Timestep: 1780000 mean reward (100 episodes): 14.0200 best mean reward: 14.0200 current episode reward: 17.0000 episodes: 1029 exploration: 0.07447 learning_rate: 0.00008 elapsed time: 7141.5 seconds (1.98 hours) Timestep: 1790000 mean reward (100 episodes): 14.4700 best mean reward: 14.4700 current episode reward: 17.0000 episodes: 1033 exploration: 0.07415 learning_rate: 0.00008 elapsed time: 7185.3 seconds (2.00 hours) Timestep: 1800000 mean reward (100 episodes): 14.9800 best mean reward: 14.9800 current episode reward: 17.0000 episodes: 1037 exploration: 0.07382 learning_rate: 0.00008 elapsed time: 7229.3 seconds (2.01 hours) Timestep: 1810000 mean reward (100 episodes): 15.1800 best mean reward: 15.1800 current episode reward: 18.0000 episodes: 1042 exploration: 0.07349 learning_rate: 0.00008 elapsed time: 7273.8 seconds (2.02 hours) Timestep: 1820000 mean reward (100 episodes): 15.5700 best mean reward: 15.5700 current episode reward: 19.0000 episodes: 1047 exploration: 0.07316 learning_rate: 0.00008 elapsed time: 7317.2 seconds (2.03 hours) Timestep: 1830000 mean reward (100 episodes): 15.7400 best mean reward: 15.7400 current episode reward: 19.0000 episodes: 1052 exploration: 0.07284 learning_rate: 0.00008 elapsed time: 7360.9 seconds (2.04 hours) Timestep: 1840000 mean reward (100 episodes): 15.9700 best mean reward: 15.9700 current episode reward: 14.0000 episodes: 1057 exploration: 0.07251 learning_rate: 0.00008 elapsed time: 7404.4 seconds (2.06 hours) Timestep: 1850000 mean reward (100 episodes): 16.1900 best mean reward: 16.1900 current episode reward: 18.0000 episodes: 1062 exploration: 0.07218 learning_rate: 0.00008 elapsed time: 7448.8 seconds (2.07 hours) Timestep: 1860000 mean reward (100 episodes): 16.4000 best mean reward: 16.4000 current episode reward: 18.0000 episodes: 1067 exploration: 0.07185 learning_rate: 0.00008 elapsed time: 7492.2 seconds (2.08 hours) Timestep: 1870000 mean reward (100 episodes): 16.5000 best mean reward: 16.5300 current episode reward: 14.0000 episodes: 1072 exploration: 0.07153 learning_rate: 0.00008 elapsed time: 7535.8 seconds (2.09 hours) Timestep: 1880000 mean reward (100 episodes): 16.4900 best mean reward: 16.5300 current episode reward: 17.0000 episodes: 1076 exploration: 0.07120 learning_rate: 0.00008 elapsed time: 7579.6 seconds (2.11 hours) Timestep: 1890000 mean reward (100 episodes): 16.4700 best mean reward: 16.5300 current episode reward: 19.0000 episodes: 1081 exploration: 0.07087 learning_rate: 0.00008 elapsed time: 7623.7 seconds (2.12 hours) Timestep: 1900000 mean reward (100 episodes): 16.5900 best mean reward: 16.5900 current episode reward: 19.0000 episodes: 1086 exploration: 0.07055 learning_rate: 0.00008 elapsed time: 7667.5 seconds (2.13 hours) Timestep: 1910000 mean reward (100 episodes): 16.6700 best mean reward: 16.6700 current episode reward: 15.0000 episodes: 1090 exploration: 0.07022 learning_rate: 0.00008 elapsed time: 7711.2 seconds (2.14 hours) Timestep: 1920000 mean reward (100 episodes): 16.6300 best mean reward: 16.6700 current episode reward: 16.0000 episodes: 1095 exploration: 0.06989 learning_rate: 0.00008 elapsed time: 7754.2 seconds (2.15 hours) Timestep: 1930000 mean reward (100 episodes): 16.6500 best mean reward: 16.7400 current episode reward: 18.0000 episodes: 1100 exploration: 0.06956 learning_rate: 0.00008 elapsed time: 7798.1 seconds (2.17 hours) Timestep: 1940000 mean reward (100 episodes): 16.0500 best mean reward: 16.7400 current episode reward: -9.0000 episodes: 1105 exploration: 0.06924 learning_rate: 0.00008 elapsed time: 7842.2 seconds (2.18 hours) Timestep: 1950000 mean reward (100 episodes): 16.1000 best mean reward: 16.7400 current episode reward: 17.0000 episodes: 1110 exploration: 0.06891 learning_rate: 0.00008 elapsed time: 7886.2 seconds (2.19 hours) Timestep: 1960000 mean reward (100 episodes): 16.1100 best mean reward: 16.7400 current episode reward: 14.0000 episodes: 1115 exploration: 0.06858 learning_rate: 0.00008 elapsed time: 7930.1 seconds (2.20 hours) Timestep: 1970000 mean reward (100 episodes): 16.1500 best mean reward: 16.7400 current episode reward: 19.0000 episodes: 1119 exploration: 0.06825 learning_rate: 0.00008 elapsed time: 7974.4 seconds (2.22 hours) Timestep: 1980000 mean reward (100 episodes): 16.0600 best mean reward: 16.7400 current episode reward: 17.0000 episodes: 1124 exploration: 0.06793 learning_rate: 0.00008 elapsed time: 8019.4 seconds (2.23 hours) Timestep: 1990000 mean reward (100 episodes): 16.2200 best mean reward: 16.7400 current episode reward: 19.0000 episodes: 1129 exploration: 0.06760 learning_rate: 0.00008 elapsed time: 8063.1 seconds (2.24 hours) Timestep: 2000000 mean reward (100 episodes): 16.3100 best mean reward: 16.7400 current episode reward: 18.0000 episodes: 1134 exploration: 0.06727 learning_rate: 0.00008 elapsed time: 8107.2 seconds (2.25 hours) Timestep: 2010000 mean reward (100 episodes): 16.4100 best mean reward: 16.7400 current episode reward: 18.0000 episodes: 1139 exploration: 0.06695 learning_rate: 0.00008 elapsed time: 8151.3 seconds (2.26 hours) Timestep: 2020000 mean reward (100 episodes): 16.4500 best mean reward: 16.7400 current episode reward: 18.0000 episodes: 1144 exploration: 0.06662 learning_rate: 0.00008 elapsed time: 8195.9 seconds (2.28 hours) Timestep: 2030000 mean reward (100 episodes): 16.3600 best mean reward: 16.7400 current episode reward: 11.0000 episodes: 1149 exploration: 0.06629 learning_rate: 0.00008 elapsed time: 8239.9 seconds (2.29 hours) Timestep: 2040000 mean reward (100 episodes): 16.4600 best mean reward: 16.7400 current episode reward: 20.0000 episodes: 1154 exploration: 0.06596 learning_rate: 0.00008 elapsed time: 8283.5 seconds (2.30 hours) Timestep: 2050000 mean reward (100 episodes): 16.4700 best mean reward: 16.7400 current episode reward: 16.0000 episodes: 1158 exploration: 0.06564 learning_rate: 0.00008 elapsed time: 8327.2 seconds (2.31 hours) Timestep: 2060000 mean reward (100 episodes): 16.4300 best mean reward: 16.7400 current episode reward: 15.0000 episodes: 1163 exploration: 0.06531 learning_rate: 0.00008 elapsed time: 8371.8 seconds (2.33 hours) Timestep: 2070000 mean reward (100 episodes): 16.3900 best mean reward: 16.7400 current episode reward: 19.0000 episodes: 1168 exploration: 0.06498 learning_rate: 0.00008 elapsed time: 8415.3 seconds (2.34 hours) Timestep: 2080000 mean reward (100 episodes): 16.4500 best mean reward: 16.7400 current episode reward: 17.0000 episodes: 1173 exploration: 0.06465 learning_rate: 0.00008 elapsed time: 8459.2 seconds (2.35 hours) Timestep: 2090000 mean reward (100 episodes): 15.6600 best mean reward: 16.7400 current episode reward: 20.0000 episodes: 1178 exploration: 0.06433 learning_rate: 0.00008 elapsed time: 8503.1 seconds (2.36 hours) Timestep: 2100000 mean reward (100 episodes): 15.7600 best mean reward: 16.7400 current episode reward: 18.0000 episodes: 1184 exploration: 0.06400 learning_rate: 0.00008 elapsed time: 8546.9 seconds (2.37 hours) Timestep: 2110000 mean reward (100 episodes): 15.8100 best mean reward: 16.7400 current episode reward: 20.0000 episodes: 1189 exploration: 0.06367 learning_rate: 0.00008 elapsed time: 8590.7 seconds (2.39 hours) Timestep: 2120000 mean reward (100 episodes): 15.8800 best mean reward: 16.7400 current episode reward: 18.0000 episodes: 1193 exploration: 0.06335 learning_rate: 0.00008 elapsed time: 8634.8 seconds (2.40 hours) Timestep: 2130000 mean reward (100 episodes): 15.8000 best mean reward: 16.7400 current episode reward: 20.0000 episodes: 1198 exploration: 0.06302 learning_rate: 0.00008 elapsed time: 8678.6 seconds (2.41 hours) Timestep: 2140000 mean reward (100 episodes): 16.1800 best mean reward: 16.7400 current episode reward: 19.0000 episodes: 1203 exploration: 0.06269 learning_rate: 0.00008 elapsed time: 8722.8 seconds (2.42 hours) Timestep: 2150000 mean reward (100 episodes): 16.5200 best mean reward: 16.7400 current episode reward: 18.0000 episodes: 1207 exploration: 0.06236 learning_rate: 0.00008 elapsed time: 8766.5 seconds (2.44 hours) Timestep: 2160000 mean reward (100 episodes): 16.4800 best mean reward: 16.7400 current episode reward: 17.0000 episodes: 1212 exploration: 0.06204 learning_rate: 0.00008 elapsed time: 8810.3 seconds (2.45 hours) Timestep: 2170000 mean reward (100 episodes): 16.5300 best mean reward: 16.7400 current episode reward: 16.0000 episodes: 1217 exploration: 0.06171 learning_rate: 0.00008 elapsed time: 8854.2 seconds (2.46 hours) Timestep: 2180000 mean reward (100 episodes): 16.5800 best mean reward: 16.7400 current episode reward: 18.0000 episodes: 1222 exploration: 0.06138 learning_rate: 0.00008 elapsed time: 8898.3 seconds (2.47 hours) Timestep: 2190000 mean reward (100 episodes): 16.4700 best mean reward: 16.7400 current episode reward: 14.0000 episodes: 1227 exploration: 0.06105 learning_rate: 0.00008 elapsed time: 8942.1 seconds (2.48 hours) Timestep: 2200000 mean reward (100 episodes): 16.3500 best mean reward: 16.7400 current episode reward: 5.0000 episodes: 1231 exploration: 0.06073 learning_rate: 0.00008 elapsed time: 8986.0 seconds (2.50 hours) Timestep: 2210000 mean reward (100 episodes): 16.3300 best mean reward: 16.7400 current episode reward: 20.0000 episodes: 1236 exploration: 0.06040 learning_rate: 0.00008 elapsed time: 9029.8 seconds (2.51 hours) Timestep: 2220000 mean reward (100 episodes): 16.3200 best mean reward: 16.7400 current episode reward: 18.0000 episodes: 1241 exploration: 0.06007 learning_rate: 0.00008 elapsed time: 9073.3 seconds (2.52 hours) Timestep: 2230000 mean reward (100 episodes): 16.3700 best mean reward: 16.7400 current episode reward: 17.0000 episodes: 1245 exploration: 0.05975 learning_rate: 0.00008 elapsed time: 9117.2 seconds (2.53 hours) Timestep: 2240000 mean reward (100 episodes): 16.3900 best mean reward: 16.7400 current episode reward: 17.0000 episodes: 1250 exploration: 0.05942 learning_rate: 0.00008 elapsed time: 9161.0 seconds (2.54 hours) Timestep: 2250000 mean reward (100 episodes): 16.3700 best mean reward: 16.7400 current episode reward: 19.0000 episodes: 1255 exploration: 0.05909 learning_rate: 0.00008 elapsed time: 9205.4 seconds (2.56 hours) Timestep: 2260000 mean reward (100 episodes): 16.4300 best mean reward: 16.7400 current episode reward: 18.0000 episodes: 1260 exploration: 0.05876 learning_rate: 0.00007 elapsed time: 9249.3 seconds (2.57 hours) Timestep: 2270000 mean reward (100 episodes): 16.5400 best mean reward: 16.7400 current episode reward: 18.0000 episodes: 1265 exploration: 0.05844 learning_rate: 0.00007 elapsed time: 9293.2 seconds (2.58 hours) Timestep: 2280000 mean reward (100 episodes): 16.5300 best mean reward: 16.7400 current episode reward: 17.0000 episodes: 1270 exploration: 0.05811 learning_rate: 0.00007 elapsed time: 9336.7 seconds (2.59 hours) Timestep: 2290000 mean reward (100 episodes): 16.8100 best mean reward: 16.8100 current episode reward: 18.0000 episodes: 1275 exploration: 0.05778 learning_rate: 0.00007 elapsed time: 9380.9 seconds (2.61 hours) Timestep: 2300000 mean reward (100 episodes): 17.3800 best mean reward: 17.4500 current episode reward: 13.0000 episodes: 1279 exploration: 0.05745 learning_rate: 0.00007 elapsed time: 9425.0 seconds (2.62 hours) Timestep: 2310000 mean reward (100 episodes): 17.3100 best mean reward: 17.4500 current episode reward: 19.0000 episodes: 1284 exploration: 0.05713 learning_rate: 0.00007 elapsed time: 9470.1 seconds (2.63 hours) Timestep: 2320000 mean reward (100 episodes): 17.3000 best mean reward: 17.4500 current episode reward: 18.0000 episodes: 1289 exploration: 0.05680 learning_rate: 0.00007 elapsed time: 9514.3 seconds (2.64 hours) Timestep: 2330000 mean reward (100 episodes): 17.3400 best mean reward: 17.4500 current episode reward: 18.0000 episodes: 1294 exploration: 0.05647 learning_rate: 0.00007 elapsed time: 9558.5 seconds (2.66 hours) Timestep: 2340000 mean reward (100 episodes): 17.3700 best mean reward: 17.4500 current episode reward: 18.0000 episodes: 1299 exploration: 0.05615 learning_rate: 0.00007 elapsed time: 9602.5 seconds (2.67 hours) Timestep: 2350000 mean reward (100 episodes): 17.3900 best mean reward: 17.4500 current episode reward: 18.0000 episodes: 1304 exploration: 0.05582 learning_rate: 0.00007 elapsed time: 9646.1 seconds (2.68 hours) Timestep: 2360000 mean reward (100 episodes): 17.4900 best mean reward: 17.4900 current episode reward: 19.0000 episodes: 1309 exploration: 0.05549 learning_rate: 0.00007 elapsed time: 9690.9 seconds (2.69 hours) Timestep: 2370000 mean reward (100 episodes): 17.5700 best mean reward: 17.5700 current episode reward: 20.0000 episodes: 1314 exploration: 0.05516 learning_rate: 0.00007 elapsed time: 9735.3 seconds (2.70 hours) Timestep: 2380000 mean reward (100 episodes): 17.6900 best mean reward: 17.6900 current episode reward: 18.0000 episodes: 1320 exploration: 0.05484 learning_rate: 0.00007 elapsed time: 9779.2 seconds (2.72 hours) Timestep: 2390000 mean reward (100 episodes): 17.7500 best mean reward: 17.8000 current episode reward: 16.0000 episodes: 1324 exploration: 0.05451 learning_rate: 0.00007 elapsed time: 9823.7 seconds (2.73 hours) Timestep: 2400000 mean reward (100 episodes): 17.7700 best mean reward: 17.8000 current episode reward: 18.0000 episodes: 1329 exploration: 0.05418 learning_rate: 0.00007 elapsed time: 9867.8 seconds (2.74 hours) Timestep: 2410000 mean reward (100 episodes): 17.9200 best mean reward: 17.9200 current episode reward: 19.0000 episodes: 1334 exploration: 0.05385 learning_rate: 0.00007 elapsed time: 9912.1 seconds (2.75 hours) Timestep: 2420000 mean reward (100 episodes): 17.8200 best mean reward: 17.9200 current episode reward: 15.0000 episodes: 1339 exploration: 0.05353 learning_rate: 0.00007 elapsed time: 9955.9 seconds (2.77 hours) Timestep: 2430000 mean reward (100 episodes): 17.9000 best mean reward: 17.9200 current episode reward: 21.0000 episodes: 1344 exploration: 0.05320 learning_rate: 0.00007 elapsed time: 9999.8 seconds (2.78 hours) Timestep: 2440000 mean reward (100 episodes): 17.9200 best mean reward: 17.9400 current episode reward: 17.0000 episodes: 1348 exploration: 0.05287 learning_rate: 0.00007 elapsed time: 10043.7 seconds (2.79 hours) Timestep: 2450000 mean reward (100 episodes): 17.8700 best mean reward: 17.9400 current episode reward: 20.0000 episodes: 1353 exploration: 0.05255 learning_rate: 0.00007 elapsed time: 10087.6 seconds (2.80 hours) Timestep: 2460000 mean reward (100 episodes): 17.8600 best mean reward: 17.9400 current episode reward: 18.0000 episodes: 1358 exploration: 0.05222 learning_rate: 0.00007 elapsed time: 10131.9 seconds (2.81 hours) Timestep: 2470000 mean reward (100 episodes): 17.9100 best mean reward: 17.9400 current episode reward: 18.0000 episodes: 1363 exploration: 0.05189 learning_rate: 0.00007 elapsed time: 10176.0 seconds (2.83 hours) Timestep: 2480000 mean reward (100 episodes): 17.9800 best mean reward: 17.9800 current episode reward: 20.0000 episodes: 1368 exploration: 0.05156 learning_rate: 0.00007 elapsed time: 10220.0 seconds (2.84 hours) Timestep: 2490000 mean reward (100 episodes): 17.9700 best mean reward: 18.0000 current episode reward: 18.0000 episodes: 1373 exploration: 0.05124 learning_rate: 0.00007 elapsed time: 10265.0 seconds (2.85 hours) Timestep: 2500000 mean reward (100 episodes): 17.9500 best mean reward: 18.0000 current episode reward: 18.0000 episodes: 1378 exploration: 0.05091 learning_rate: 0.00007 elapsed time: 10309.2 seconds (2.86 hours) Timestep: 2510000 mean reward (100 episodes): 18.0600 best mean reward: 18.1200 current episode reward: 17.0000 episodes: 1383 exploration: 0.05058 learning_rate: 0.00007 elapsed time: 10353.5 seconds (2.88 hours) Timestep: 2520000 mean reward (100 episodes): 18.0300 best mean reward: 18.1200 current episode reward: 20.0000 episodes: 1388 exploration: 0.05025 learning_rate: 0.00007 elapsed time: 10397.8 seconds (2.89 hours) Timestep: 2530000 mean reward (100 episodes): 18.0700 best mean reward: 18.1200 current episode reward: 20.0000 episodes: 1393 exploration: 0.04993 learning_rate: 0.00007 elapsed time: 10441.3 seconds (2.90 hours) Timestep: 2540000 mean reward (100 episodes): 17.7100 best mean reward: 18.1200 current episode reward: 19.0000 episodes: 1398 exploration: 0.04960 learning_rate: 0.00007 elapsed time: 10485.3 seconds (2.91 hours) Timestep: 2550000 mean reward (100 episodes): 17.3600 best mean reward: 18.1200 current episode reward: -18.0000 episodes: 1404 exploration: 0.04927 learning_rate: 0.00007 elapsed time: 10529.0 seconds (2.92 hours) Timestep: 2560000 mean reward (100 episodes): 17.1000 best mean reward: 18.1200 current episode reward: 20.0000 episodes: 1409 exploration: 0.04895 learning_rate: 0.00007 elapsed time: 10572.4 seconds (2.94 hours) Timestep: 2570000 mean reward (100 episodes): 17.0600 best mean reward: 18.1200 current episode reward: 19.0000 episodes: 1413 exploration: 0.04862 learning_rate: 0.00007 elapsed time: 10616.1 seconds (2.95 hours) Timestep: 2580000 mean reward (100 episodes): 16.9500 best mean reward: 18.1200 current episode reward: 16.0000 episodes: 1418 exploration: 0.04829 learning_rate: 0.00007 elapsed time: 10660.0 seconds (2.96 hours) Timestep: 2590000 mean reward (100 episodes): 16.9700 best mean reward: 18.1200 current episode reward: 19.0000 episodes: 1423 exploration: 0.04796 learning_rate: 0.00007 elapsed time: 10704.1 seconds (2.97 hours) Timestep: 2600000 mean reward (100 episodes): 17.0300 best mean reward: 18.1200 current episode reward: 14.0000 episodes: 1428 exploration: 0.04764 learning_rate: 0.00007 elapsed time: 10749.0 seconds (2.99 hours) Timestep: 2610000 mean reward (100 episodes): 17.0700 best mean reward: 18.1200 current episode reward: 19.0000 episodes: 1433 exploration: 0.04731 learning_rate: 0.00007 elapsed time: 10793.7 seconds (3.00 hours) Timestep: 2620000 mean reward (100 episodes): 17.1900 best mean reward: 18.1200 current episode reward: 20.0000 episodes: 1439 exploration: 0.04698 learning_rate: 0.00007 elapsed time: 10837.1 seconds (3.01 hours) Timestep: 2630000 mean reward (100 episodes): 17.1500 best mean reward: 18.1200 current episode reward: 18.0000 episodes: 1444 exploration: 0.04665 learning_rate: 0.00007 elapsed time: 10881.7 seconds (3.02 hours) Timestep: 2640000 mean reward (100 episodes): 17.1600 best mean reward: 18.1200 current episode reward: 17.0000 episodes: 1448 exploration: 0.04633 learning_rate: 0.00007 elapsed time: 10926.3 seconds (3.04 hours) Timestep: 2650000 mean reward (100 episodes): 17.1400 best mean reward: 18.1200 current episode reward: 17.0000 episodes: 1453 exploration: 0.04600 learning_rate: 0.00007 elapsed time: 10970.6 seconds (3.05 hours) Timestep: 2660000 mean reward (100 episodes): 17.1400 best mean reward: 18.1200 current episode reward: 19.0000 episodes: 1459 exploration: 0.04567 learning_rate: 0.00007 elapsed time: 11014.3 seconds (3.06 hours) Timestep: 2670000 mean reward (100 episodes): 17.1200 best mean reward: 18.1200 current episode reward: 21.0000 episodes: 1464 exploration: 0.04535 learning_rate: 0.00007 elapsed time: 11057.9 seconds (3.07 hours) Timestep: 2680000 mean reward (100 episodes): 17.0300 best mean reward: 18.1200 current episode reward: 21.0000 episodes: 1469 exploration: 0.04502 learning_rate: 0.00007 elapsed time: 11102.8 seconds (3.08 hours) Timestep: 2690000 mean reward (100 episodes): 17.0400 best mean reward: 18.1200 current episode reward: 20.0000 episodes: 1474 exploration: 0.04469 learning_rate: 0.00007 elapsed time: 11146.9 seconds (3.10 hours) Timestep: 2700000 mean reward (100 episodes): 17.1300 best mean reward: 18.1200 current episode reward: 19.0000 episodes: 1479 exploration: 0.04436 learning_rate: 0.00007 elapsed time: 11190.9 seconds (3.11 hours) Timestep: 2710000 mean reward (100 episodes): 17.1800 best mean reward: 18.1200 current episode reward: 18.0000 episodes: 1484 exploration: 0.04404 learning_rate: 0.00007 elapsed time: 11235.2 seconds (3.12 hours) Timestep: 2720000 mean reward (100 episodes): 17.2000 best mean reward: 18.1200 current episode reward: 20.0000 episodes: 1489 exploration: 0.04371 learning_rate: 0.00007 elapsed time: 11279.3 seconds (3.13 hours) Timestep: 2730000 mean reward (100 episodes): 17.3200 best mean reward: 18.1200 current episode reward: 20.0000 episodes: 1495 exploration: 0.04338 learning_rate: 0.00007 elapsed time: 11323.9 seconds (3.15 hours) Timestep: 2740000 mean reward (100 episodes): 17.7100 best mean reward: 18.1200 current episode reward: 18.0000 episodes: 1499 exploration: 0.04305 learning_rate: 0.00007 elapsed time: 11368.2 seconds (3.16 hours) Timestep: 2750000 mean reward (100 episodes): 18.0800 best mean reward: 18.1200 current episode reward: 18.0000 episodes: 1504 exploration: 0.04273 learning_rate: 0.00007 elapsed time: 11412.8 seconds (3.17 hours) Timestep: 2760000 mean reward (100 episodes): 18.3500 best mean reward: 18.3800 current episode reward: 19.0000 episodes: 1509 exploration: 0.04240 learning_rate: 0.00007 elapsed time: 11457.3 seconds (3.18 hours) Timestep: 2770000 mean reward (100 episodes): 18.4200 best mean reward: 18.4200 current episode reward: 20.0000 episodes: 1514 exploration: 0.04207 learning_rate: 0.00007 elapsed time: 11501.4 seconds (3.19 hours) Timestep: 2780000 mean reward (100 episodes): 18.4200 best mean reward: 18.4300 current episode reward: 17.0000 episodes: 1519 exploration: 0.04175 learning_rate: 0.00007 elapsed time: 11545.5 seconds (3.21 hours) Timestep: 2790000 mean reward (100 episodes): 18.3800 best mean reward: 18.4300 current episode reward: 19.0000 episodes: 1524 exploration: 0.04142 learning_rate: 0.00007 elapsed time: 11590.0 seconds (3.22 hours) Timestep: 2800000 mean reward (100 episodes): 18.4000 best mean reward: 18.4400 current episode reward: 17.0000 episodes: 1529 exploration: 0.04109 learning_rate: 0.00007 elapsed time: 11633.7 seconds (3.23 hours) Timestep: 2810000 mean reward (100 episodes): 18.4500 best mean reward: 18.4700 current episode reward: 18.0000 episodes: 1535 exploration: 0.04076 learning_rate: 0.00007 elapsed time: 11677.6 seconds (3.24 hours) Timestep: 2820000 mean reward (100 episodes): 18.4500 best mean reward: 18.4700 current episode reward: 19.0000 episodes: 1540 exploration: 0.04044 learning_rate: 0.00007 elapsed time: 11722.1 seconds (3.26 hours) Timestep: 2830000 mean reward (100 episodes): 18.4800 best mean reward: 18.4800 current episode reward: 20.0000 episodes: 1545 exploration: 0.04011 learning_rate: 0.00007 elapsed time: 11765.7 seconds (3.27 hours) Timestep: 2840000 mean reward (100 episodes): 18.6200 best mean reward: 18.6300 current episode reward: 18.0000 episodes: 1550 exploration: 0.03978 learning_rate: 0.00007 elapsed time: 11809.6 seconds (3.28 hours) Timestep: 2850000 mean reward (100 episodes): 18.6700 best mean reward: 18.6900 current episode reward: 17.0000 episodes: 1556 exploration: 0.03945 learning_rate: 0.00007 elapsed time: 11853.7 seconds (3.29 hours) Timestep: 2860000 mean reward (100 episodes): 18.6400 best mean reward: 18.6900 current episode reward: 19.0000 episodes: 1561 exploration: 0.03913 learning_rate: 0.00006 elapsed time: 11897.6 seconds (3.30 hours) Timestep: 2870000 mean reward (100 episodes): 18.6500 best mean reward: 18.6900 current episode reward: 15.0000 episodes: 1566 exploration: 0.03880 learning_rate: 0.00006 elapsed time: 11941.6 seconds (3.32 hours) Timestep: 2880000 mean reward (100 episodes): 18.7000 best mean reward: 18.7300 current episode reward: 16.0000 episodes: 1571 exploration: 0.03847 learning_rate: 0.00006 elapsed time: 11986.0 seconds (3.33 hours) Timestep: 2890000 mean reward (100 episodes): 18.7300 best mean reward: 18.7300 current episode reward: 19.0000 episodes: 1575 exploration: 0.03815 learning_rate: 0.00006 elapsed time: 12030.3 seconds (3.34 hours) Timestep: 2900000 mean reward (100 episodes): 18.7200 best mean reward: 18.7300 current episode reward: 19.0000 episodes: 1581 exploration: 0.03782 learning_rate: 0.00006 elapsed time: 12074.7 seconds (3.35 hours) Timestep: 2910000 mean reward (100 episodes): 18.7400 best mean reward: 18.7500 current episode reward: 18.0000 episodes: 1586 exploration: 0.03749 learning_rate: 0.00006 elapsed time: 12119.5 seconds (3.37 hours) Timestep: 2920000 mean reward (100 episodes): 18.6500 best mean reward: 18.7500 current episode reward: 21.0000 episodes: 1591 exploration: 0.03716 learning_rate: 0.00006 elapsed time: 12163.8 seconds (3.38 hours) Timestep: 2930000 mean reward (100 episodes): 18.6000 best mean reward: 18.7500 current episode reward: 19.0000 episodes: 1596 exploration: 0.03684 learning_rate: 0.00006 elapsed time: 12208.1 seconds (3.39 hours) Timestep: 2940000 mean reward (100 episodes): 18.6700 best mean reward: 18.7500 current episode reward: 19.0000 episodes: 1602 exploration: 0.03651 learning_rate: 0.00006 elapsed time: 12251.6 seconds (3.40 hours) Timestep: 2950000 mean reward (100 episodes): 18.6300 best mean reward: 18.7500 current episode reward: 18.0000 episodes: 1606 exploration: 0.03618 learning_rate: 0.00006 elapsed time: 12295.4 seconds (3.42 hours) Timestep: 2960000 mean reward (100 episodes): 18.7000 best mean reward: 18.7500 current episode reward: 20.0000 episodes: 1612 exploration: 0.03585 learning_rate: 0.00006 elapsed time: 12339.5 seconds (3.43 hours) Timestep: 2970000 mean reward (100 episodes): 18.7100 best mean reward: 18.7500 current episode reward: 21.0000 episodes: 1617 exploration: 0.03553 learning_rate: 0.00006 elapsed time: 12383.7 seconds (3.44 hours) Timestep: 2980000 mean reward (100 episodes): 18.7400 best mean reward: 18.7500 current episode reward: 18.0000 episodes: 1622 exploration: 0.03520 learning_rate: 0.00006 elapsed time: 12427.8 seconds (3.45 hours) Timestep: 2990000 mean reward (100 episodes): 18.7700 best mean reward: 18.7800 current episode reward: 20.0000 episodes: 1628 exploration: 0.03487 learning_rate: 0.00006 elapsed time: 12472.3 seconds (3.46 hours) Timestep: 3000000 mean reward (100 episodes): 18.7300 best mean reward: 18.8100 current episode reward: 19.0000 episodes: 1633 exploration: 0.03455 learning_rate: 0.00006 elapsed time: 12516.2 seconds (3.48 hours) Timestep: 3010000 mean reward (100 episodes): 18.7100 best mean reward: 18.8100 current episode reward: 21.0000 episodes: 1638 exploration: 0.03422 learning_rate: 0.00006 elapsed time: 12561.1 seconds (3.49 hours) Timestep: 3020000 mean reward (100 episodes): 18.7400 best mean reward: 18.8100 current episode reward: 19.0000 episodes: 1644 exploration: 0.03389 learning_rate: 0.00006 elapsed time: 12605.2 seconds (3.50 hours) Timestep: 3030000 mean reward (100 episodes): 18.6700 best mean reward: 18.8100 current episode reward: 19.0000 episodes: 1649 exploration: 0.03356 learning_rate: 0.00006 elapsed time: 12649.5 seconds (3.51 hours) Timestep: 3040000 mean reward (100 episodes): 18.7500 best mean reward: 18.8100 current episode reward: 20.0000 episodes: 1654 exploration: 0.03324 learning_rate: 0.00006 elapsed time: 12694.4 seconds (3.53 hours) Timestep: 3050000 mean reward (100 episodes): 18.7400 best mean reward: 18.8100 current episode reward: 16.0000 episodes: 1659 exploration: 0.03291 learning_rate: 0.00006 elapsed time: 12738.4 seconds (3.54 hours) Timestep: 3060000 mean reward (100 episodes): 18.7600 best mean reward: 18.8100 current episode reward: 20.0000 episodes: 1664 exploration: 0.03258 learning_rate: 0.00006 elapsed time: 12782.6 seconds (3.55 hours) Timestep: 3070000 mean reward (100 episodes): 18.8900 best mean reward: 18.8900 current episode reward: 21.0000 episodes: 1670 exploration: 0.03225 learning_rate: 0.00006 elapsed time: 12827.3 seconds (3.56 hours) Timestep: 3080000 mean reward (100 episodes): 18.9600 best mean reward: 18.9600 current episode reward: 20.0000 episodes: 1675 exploration: 0.03193 learning_rate: 0.00006 elapsed time: 12870.0 seconds (3.58 hours) Timestep: 3090000 mean reward (100 episodes): 18.9700 best mean reward: 19.0000 current episode reward: 20.0000 episodes: 1680 exploration: 0.03160 learning_rate: 0.00006 elapsed time: 12914.4 seconds (3.59 hours) Timestep: 3100000 mean reward (100 episodes): 19.0000 best mean reward: 19.0000 current episode reward: 18.0000 episodes: 1686 exploration: 0.03127 learning_rate: 0.00006 elapsed time: 12958.3 seconds (3.60 hours) Timestep: 3110000 mean reward (100 episodes): 19.0700 best mean reward: 19.1200 current episode reward: 21.0000 episodes: 1691 exploration: 0.03095 learning_rate: 0.00006 elapsed time: 13002.8 seconds (3.61 hours) Timestep: 3120000 mean reward (100 episodes): 18.9600 best mean reward: 19.1200 current episode reward: 4.0000 episodes: 1696 exploration: 0.03062 learning_rate: 0.00006 elapsed time: 13047.2 seconds (3.62 hours) Timestep: 3130000 mean reward (100 episodes): 18.9900 best mean reward: 19.1200 current episode reward: 17.0000 episodes: 1702 exploration: 0.03029 learning_rate: 0.00006 elapsed time: 13090.2 seconds (3.64 hours) Timestep: 3140000 mean reward (100 episodes): 18.9800 best mean reward: 19.1200 current episode reward: 18.0000 episodes: 1707 exploration: 0.02996 learning_rate: 0.00006 elapsed time: 13134.6 seconds (3.65 hours) Timestep: 3150000 mean reward (100 episodes): 18.9900 best mean reward: 19.1200 current episode reward: 19.0000 episodes: 1712 exploration: 0.02964 learning_rate: 0.00006 elapsed time: 13178.8 seconds (3.66 hours) Timestep: 3160000 mean reward (100 episodes): 19.0700 best mean reward: 19.1200 current episode reward: 20.0000 episodes: 1718 exploration: 0.02931 learning_rate: 0.00006 elapsed time: 13224.1 seconds (3.67 hours) Timestep: 3170000 mean reward (100 episodes): 19.1100 best mean reward: 19.1200 current episode reward: 20.0000 episodes: 1723 exploration: 0.02898 learning_rate: 0.00006 elapsed time: 13267.8 seconds (3.69 hours) Timestep: 3180000 mean reward (100 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episodes): 20.7800 best mean reward: 20.8700 current episode reward: 20.0000 episodes: 3689 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28257.4 seconds (7.85 hours) Timestep: 6550000 mean reward (100 episodes): 20.8000 best mean reward: 20.8700 current episode reward: 21.0000 episodes: 3695 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28302.2 seconds (7.86 hours) Timestep: 6560000 mean reward (100 episodes): 20.7900 best mean reward: 20.8700 current episode reward: 21.0000 episodes: 3701 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28346.2 seconds (7.87 hours) Timestep: 6570000 mean reward (100 episodes): 20.7800 best mean reward: 20.8700 current episode reward: 21.0000 episodes: 3707 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28390.1 seconds (7.89 hours) Timestep: 6580000 mean reward (100 episodes): 20.7900 best mean reward: 20.8700 current episode reward: 21.0000 episodes: 3713 exploration: 0.01000 learning_rate: 0.00005 elapsed 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episodes): 20.8000 best mean reward: 20.8700 current episode reward: 20.0000 episodes: 3772 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28880.1 seconds (8.02 hours) Timestep: 6690000 mean reward (100 episodes): 20.7700 best mean reward: 20.8700 current episode reward: 20.0000 episodes: 3778 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28924.9 seconds (8.03 hours) Timestep: 6700000 mean reward (100 episodes): 20.7800 best mean reward: 20.8700 current episode reward: 21.0000 episodes: 3784 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 28969.0 seconds (8.05 hours) Timestep: 6710000 mean reward (100 episodes): 20.7900 best mean reward: 20.8700 current episode reward: 21.0000 episodes: 3790 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 29012.3 seconds (8.06 hours) Timestep: 6720000 mean reward (100 episodes): 20.7800 best mean reward: 20.8700 current episode reward: 20.0000 episodes: 3796 exploration: 0.01000 learning_rate: 0.00005 elapsed 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time: 32168.9 seconds (8.94 hours) Timestep: 7430000 mean reward (100 episodes): 20.8400 best mean reward: 20.8700 current episode reward: 21.0000 episodes: 4218 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32213.5 seconds (8.95 hours) Timestep: 7440000 mean reward (100 episodes): 20.8500 best mean reward: 20.8700 current episode reward: 21.0000 episodes: 4225 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32257.8 seconds (8.96 hours) Timestep: 7450000 mean reward (100 episodes): 20.8400 best mean reward: 20.8700 current episode reward: 21.0000 episodes: 4230 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32302.0 seconds (8.97 hours) Timestep: 7460000 mean reward (100 episodes): 20.8200 best mean reward: 20.8700 current episode reward: 21.0000 episodes: 4236 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32346.4 seconds (8.99 hours) Timestep: 7470000 mean reward (100 episodes): 20.8000 best mean reward: 20.8700 current episode reward: 21.0000 episodes: 4242 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32390.3 seconds (9.00 hours) Timestep: 7476493 mean reward (100 episodes): 20.7700 best mean reward: 20.8700 current episode reward: 21.0000 episodes: 4246 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32419.2 seconds (9.01 hours) ================================================ FILE: dqn/logs_text/Pong_s002.text ================================================ ('AVAILABLE GPUS: ', [u'device: 0, name: TITAN X (Pascal), pci bus id: 0000:01:00.0']) task = Task Timestep: 60000 mean reward (100 episodes): -20.2698 best mean reward: -inf current episode reward: -19.0000 episodes: 63 exploration: 0.94600 learning_rate: 0.00010 elapsed time: 94.9 seconds (0.03 hours) Timestep: 70000 mean reward (100 episodes): -20.3200 best mean reward: -inf current episode reward: -20.0000 episodes: 75 exploration: 0.93700 learning_rate: 0.00010 elapsed time: 129.6 seconds (0.04 hours) Timestep: 80000 mean reward (100 episodes): -20.3372 best mean reward: -inf current episode reward: -20.0000 episodes: 86 exploration: 0.92800 learning_rate: 0.00010 elapsed time: 161.8 seconds (0.04 hours) Timestep: 90000 mean reward (100 episodes): -20.2917 best mean reward: -inf current episode reward: -19.0000 episodes: 96 exploration: 0.91900 learning_rate: 0.00010 elapsed time: 193.8 seconds (0.05 hours) Timestep: 100000 mean reward (100 episodes): -20.2700 best mean reward: -20.2500 current episode reward: -21.0000 episodes: 107 exploration: 0.91000 learning_rate: 0.00010 elapsed time: 226.2 seconds (0.06 hours) Timestep: 110000 mean reward (100 episodes): -20.2300 best mean reward: -20.2300 current episode reward: -21.0000 episodes: 118 exploration: 0.90100 learning_rate: 0.00010 elapsed time: 258.8 seconds (0.07 hours) Timestep: 120000 mean reward (100 episodes): -20.2600 best mean reward: -20.2300 current episode reward: -19.0000 episodes: 129 exploration: 0.89200 learning_rate: 0.00010 elapsed time: 293.9 seconds (0.08 hours) Timestep: 130000 mean reward (100 episodes): -20.2300 best mean reward: -20.2300 current episode reward: -19.0000 episodes: 139 exploration: 0.88300 learning_rate: 0.00010 elapsed time: 327.3 seconds (0.09 hours) Timestep: 140000 mean reward (100 episodes): -20.2600 best mean reward: -20.2200 current episode reward: -21.0000 episodes: 150 exploration: 0.87400 learning_rate: 0.00010 elapsed time: 360.2 seconds (0.10 hours) Timestep: 150000 mean reward (100 episodes): -20.2800 best mean reward: -20.2200 current episode reward: -21.0000 episodes: 160 exploration: 0.86500 learning_rate: 0.00010 elapsed time: 393.3 seconds (0.11 hours) Timestep: 160000 mean reward (100 episodes): -20.3200 best mean reward: -20.2200 current episode reward: -20.0000 episodes: 170 exploration: 0.85600 learning_rate: 0.00010 elapsed time: 426.0 seconds (0.12 hours) Timestep: 170000 mean reward (100 episodes): -20.2500 best mean reward: -20.2200 current episode reward: -20.0000 episodes: 181 exploration: 0.84700 learning_rate: 0.00010 elapsed time: 459.4 seconds (0.13 hours) Timestep: 180000 mean reward (100 episodes): -20.2900 best mean reward: -20.2200 current episode reward: -21.0000 episodes: 192 exploration: 0.83800 learning_rate: 0.00010 elapsed time: 492.5 seconds (0.14 hours) Timestep: 190000 mean reward (100 episodes): -20.3400 best mean reward: -20.2200 current episode reward: -21.0000 episodes: 203 exploration: 0.82900 learning_rate: 0.00010 elapsed time: 526.1 seconds (0.15 hours) Timestep: 200000 mean reward (100 episodes): -20.3200 best mean reward: -20.2200 current episode reward: -21.0000 episodes: 214 exploration: 0.82000 learning_rate: 0.00010 elapsed time: 559.4 seconds (0.16 hours) Timestep: 210000 mean reward (100 episodes): -20.3300 best mean reward: -20.2200 current episode reward: -19.0000 episodes: 225 exploration: 0.81100 learning_rate: 0.00010 elapsed time: 596.1 seconds (0.17 hours) Timestep: 220000 mean reward (100 episodes): -20.3400 best mean reward: -20.2200 current episode reward: -20.0000 episodes: 235 exploration: 0.80200 learning_rate: 0.00010 elapsed time: 630.7 seconds (0.18 hours) Timestep: 230000 mean reward (100 episodes): -20.3300 best mean reward: -20.2200 current episode reward: -20.0000 episodes: 247 exploration: 0.79300 learning_rate: 0.00010 elapsed time: 664.8 seconds (0.18 hours) Timestep: 240000 mean reward (100 episodes): -20.3100 best mean reward: -20.2200 current episode reward: -20.0000 episodes: 257 exploration: 0.78400 learning_rate: 0.00010 elapsed time: 698.7 seconds (0.19 hours) Timestep: 250000 mean reward (100 episodes): -20.2600 best mean reward: -20.2200 current episode reward: -18.0000 episodes: 268 exploration: 0.77500 learning_rate: 0.00010 elapsed time: 733.0 seconds (0.20 hours) Timestep: 260000 mean reward (100 episodes): -20.2700 best mean reward: -20.2200 current episode reward: -21.0000 episodes: 278 exploration: 0.76600 learning_rate: 0.00010 elapsed time: 767.2 seconds (0.21 hours) Timestep: 270000 mean reward (100 episodes): -20.2400 best mean reward: -20.2200 current episode reward: -20.0000 episodes: 289 exploration: 0.75700 learning_rate: 0.00010 elapsed time: 802.2 seconds (0.22 hours) Timestep: 280000 mean reward (100 episodes): -20.2500 best mean reward: -20.2200 current episode reward: -20.0000 episodes: 300 exploration: 0.74800 learning_rate: 0.00010 elapsed time: 836.9 seconds (0.23 hours) Timestep: 290000 mean reward (100 episodes): -20.2100 best mean reward: -20.2000 current episode reward: -20.0000 episodes: 310 exploration: 0.73900 learning_rate: 0.00010 elapsed time: 871.8 seconds (0.24 hours) Timestep: 300000 mean reward (100 episodes): -20.1600 best mean reward: -20.1600 current episode reward: -17.0000 episodes: 321 exploration: 0.73000 learning_rate: 0.00010 elapsed time: 907.2 seconds (0.25 hours) Timestep: 310000 mean reward (100 episodes): -20.2200 best mean reward: -20.1600 current episode reward: -21.0000 episodes: 332 exploration: 0.72100 learning_rate: 0.00010 elapsed time: 942.4 seconds (0.26 hours) Timestep: 320000 mean reward (100 episodes): -20.1900 best mean reward: -20.1600 current episode reward: -20.0000 episodes: 343 exploration: 0.71200 learning_rate: 0.00010 elapsed time: 979.5 seconds (0.27 hours) Timestep: 330000 mean reward (100 episodes): -20.2600 best mean reward: -20.1600 current episode reward: -20.0000 episodes: 354 exploration: 0.70300 learning_rate: 0.00010 elapsed time: 1016.4 seconds (0.28 hours) Timestep: 340000 mean reward (100 episodes): -20.2100 best mean reward: -20.1600 current episode reward: -21.0000 episodes: 364 exploration: 0.69400 learning_rate: 0.00010 elapsed time: 1052.1 seconds (0.29 hours) Timestep: 350000 mean reward (100 episodes): -20.2600 best mean reward: -20.1600 current episode reward: -21.0000 episodes: 373 exploration: 0.68500 learning_rate: 0.00010 elapsed time: 1088.3 seconds (0.30 hours) Timestep: 360000 mean reward (100 episodes): -20.1700 best mean reward: -20.1600 current episode reward: -19.0000 episodes: 383 exploration: 0.67600 learning_rate: 0.00010 elapsed time: 1124.8 seconds (0.31 hours) Timestep: 370000 mean reward (100 episodes): -20.0700 best mean reward: -20.0700 current episode reward: -19.0000 episodes: 392 exploration: 0.66700 learning_rate: 0.00010 elapsed time: 1160.2 seconds (0.32 hours) Timestep: 380000 mean reward (100 episodes): -20.0500 best mean reward: -20.0400 current episode reward: -19.0000 episodes: 402 exploration: 0.65800 learning_rate: 0.00010 elapsed time: 1196.5 seconds (0.33 hours) Timestep: 390000 mean reward (100 episodes): -20.0100 best mean reward: -20.0100 current episode reward: -19.0000 episodes: 411 exploration: 0.64900 learning_rate: 0.00010 elapsed time: 1232.8 seconds (0.34 hours) Timestep: 400000 mean reward (100 episodes): -19.9100 best mean reward: -19.9100 current episode reward: -18.0000 episodes: 419 exploration: 0.64000 learning_rate: 0.00010 elapsed time: 1268.4 seconds (0.35 hours) Timestep: 410000 mean reward (100 episodes): -19.8200 best mean reward: -19.8200 current episode reward: -19.0000 episodes: 427 exploration: 0.63100 learning_rate: 0.00010 elapsed time: 1305.2 seconds (0.36 hours) Timestep: 420000 mean reward (100 episodes): -19.6600 best mean reward: -19.6600 current episode reward: -20.0000 episodes: 435 exploration: 0.62200 learning_rate: 0.00010 elapsed time: 1342.3 seconds (0.37 hours) Timestep: 430000 mean reward (100 episodes): -19.5600 best mean reward: -19.5600 current episode reward: -15.0000 episodes: 442 exploration: 0.61300 learning_rate: 0.00010 elapsed time: 1379.1 seconds (0.38 hours) Timestep: 440000 mean reward (100 episodes): -19.3700 best mean reward: -19.3700 current episode reward: -17.0000 episodes: 450 exploration: 0.60400 learning_rate: 0.00010 elapsed time: 1416.0 seconds (0.39 hours) Timestep: 450000 mean reward (100 episodes): -19.1800 best mean reward: -19.1800 current episode reward: -19.0000 episodes: 457 exploration: 0.59500 learning_rate: 0.00010 elapsed time: 1452.7 seconds (0.40 hours) Timestep: 460000 mean reward (100 episodes): -19.0900 best mean reward: -19.0900 current episode reward: -19.0000 episodes: 464 exploration: 0.58600 learning_rate: 0.00010 elapsed time: 1489.6 seconds (0.41 hours) Timestep: 470000 mean reward (100 episodes): -18.9000 best mean reward: -18.8900 current episode reward: -20.0000 episodes: 471 exploration: 0.57700 learning_rate: 0.00010 elapsed time: 1527.1 seconds (0.42 hours) Timestep: 480000 mean reward (100 episodes): -18.7800 best mean reward: -18.7800 current episode reward: -18.0000 episodes: 478 exploration: 0.56800 learning_rate: 0.00010 elapsed time: 1564.7 seconds (0.43 hours) Timestep: 490000 mean reward (100 episodes): -18.7200 best mean reward: -18.7100 current episode reward: -19.0000 episodes: 484 exploration: 0.55900 learning_rate: 0.00010 elapsed time: 1602.2 seconds (0.45 hours) Timestep: 500000 mean reward (100 episodes): -18.5100 best mean reward: -18.5100 current episode reward: -17.0000 episodes: 491 exploration: 0.55000 learning_rate: 0.00010 elapsed time: 1639.9 seconds (0.46 hours) Timestep: 510000 mean reward (100 episodes): -18.4400 best mean reward: -18.4400 current episode reward: -21.0000 episodes: 497 exploration: 0.54100 learning_rate: 0.00010 elapsed time: 1677.4 seconds (0.47 hours) Timestep: 520000 mean reward (100 episodes): -18.1800 best mean reward: -18.1800 current episode reward: -14.0000 episodes: 504 exploration: 0.53200 learning_rate: 0.00010 elapsed time: 1715.6 seconds (0.48 hours) Timestep: 530000 mean reward (100 episodes): -18.0500 best mean reward: -18.0500 current episode reward: -19.0000 episodes: 510 exploration: 0.52300 learning_rate: 0.00010 elapsed time: 1753.7 seconds (0.49 hours) Timestep: 540000 mean reward (100 episodes): -17.9400 best mean reward: -17.9400 current episode reward: -18.0000 episodes: 516 exploration: 0.51400 learning_rate: 0.00010 elapsed time: 1796.4 seconds (0.50 hours) Timestep: 550000 mean reward (100 episodes): -17.8800 best mean reward: -17.8800 current episode reward: -17.0000 episodes: 523 exploration: 0.50500 learning_rate: 0.00010 elapsed time: 1834.9 seconds (0.51 hours) Timestep: 560000 mean reward (100 episodes): -17.8700 best mean reward: -17.8600 current episode reward: -18.0000 episodes: 529 exploration: 0.49600 learning_rate: 0.00010 elapsed time: 1874.4 seconds (0.52 hours) Timestep: 570000 mean reward (100 episodes): -17.7800 best mean reward: -17.7800 current episode reward: -16.0000 episodes: 535 exploration: 0.48700 learning_rate: 0.00010 elapsed time: 1913.1 seconds (0.53 hours) Timestep: 580000 mean reward (100 episodes): -17.6300 best mean reward: -17.6300 current episode reward: -18.0000 episodes: 540 exploration: 0.47800 learning_rate: 0.00010 elapsed time: 1952.6 seconds (0.54 hours) Timestep: 590000 mean reward (100 episodes): -17.7300 best mean reward: -17.6300 current episode reward: -18.0000 episodes: 546 exploration: 0.46900 learning_rate: 0.00010 elapsed time: 1992.0 seconds (0.55 hours) Timestep: 600000 mean reward (100 episodes): -17.6600 best mean reward: -17.6300 current episode reward: -17.0000 episodes: 553 exploration: 0.46000 learning_rate: 0.00010 elapsed time: 2030.8 seconds (0.56 hours) Timestep: 610000 mean reward (100 episodes): -17.5900 best mean reward: -17.5900 current episode reward: -16.0000 episodes: 559 exploration: 0.45100 learning_rate: 0.00010 elapsed time: 2070.4 seconds (0.58 hours) Timestep: 620000 mean reward (100 episodes): -17.4900 best mean reward: -17.4900 current episode reward: -15.0000 episodes: 565 exploration: 0.44200 learning_rate: 0.00010 elapsed time: 2110.1 seconds (0.59 hours) Timestep: 630000 mean reward (100 episodes): -17.4700 best mean reward: -17.4300 current episode reward: -17.0000 episodes: 570 exploration: 0.43300 learning_rate: 0.00010 elapsed time: 2150.2 seconds (0.60 hours) Timestep: 640000 mean reward (100 episodes): -17.3500 best mean reward: -17.3500 current episode reward: -16.0000 episodes: 576 exploration: 0.42400 learning_rate: 0.00010 elapsed time: 2189.7 seconds (0.61 hours) Timestep: 650000 mean reward (100 episodes): -17.2800 best mean reward: -17.2800 current episode reward: -16.0000 episodes: 582 exploration: 0.41500 learning_rate: 0.00010 elapsed time: 2229.2 seconds (0.62 hours) Timestep: 660000 mean reward (100 episodes): -17.1800 best mean reward: -17.1400 current episode reward: -15.0000 episodes: 588 exploration: 0.40600 learning_rate: 0.00010 elapsed time: 2269.4 seconds (0.63 hours) Timestep: 670000 mean reward (100 episodes): -17.1100 best mean reward: -17.1100 current episode reward: -18.0000 episodes: 593 exploration: 0.39700 learning_rate: 0.00010 elapsed time: 2309.9 seconds (0.64 hours) Timestep: 680000 mean reward (100 episodes): -16.9900 best mean reward: -16.9900 current episode reward: -16.0000 episodes: 598 exploration: 0.38800 learning_rate: 0.00010 elapsed time: 2350.7 seconds (0.65 hours) Timestep: 690000 mean reward (100 episodes): -17.0600 best mean reward: -16.9900 current episode reward: -18.0000 episodes: 604 exploration: 0.37900 learning_rate: 0.00010 elapsed time: 2391.0 seconds (0.66 hours) Timestep: 700000 mean reward (100 episodes): -16.9400 best mean reward: -16.9400 current episode reward: -15.0000 episodes: 609 exploration: 0.37000 learning_rate: 0.00010 elapsed time: 2431.2 seconds (0.68 hours) Timestep: 710000 mean reward (100 episodes): -16.8400 best mean reward: -16.8400 current episode reward: -14.0000 episodes: 615 exploration: 0.36100 learning_rate: 0.00010 elapsed time: 2472.0 seconds (0.69 hours) Timestep: 720000 mean reward (100 episodes): -16.7800 best mean reward: -16.7800 current episode reward: -14.0000 episodes: 620 exploration: 0.35200 learning_rate: 0.00010 elapsed time: 2512.1 seconds (0.70 hours) Timestep: 730000 mean reward (100 episodes): -16.7400 best mean reward: -16.7200 current episode reward: -19.0000 episodes: 625 exploration: 0.34300 learning_rate: 0.00010 elapsed time: 2552.9 seconds (0.71 hours) Timestep: 740000 mean reward (100 episodes): -16.6500 best mean reward: -16.6500 current episode reward: -15.0000 episodes: 630 exploration: 0.33400 learning_rate: 0.00010 elapsed time: 2594.3 seconds (0.72 hours) Timestep: 750000 mean reward (100 episodes): -16.6100 best mean reward: -16.6100 current episode reward: -14.0000 episodes: 635 exploration: 0.32500 learning_rate: 0.00010 elapsed time: 2634.8 seconds (0.73 hours) Timestep: 760000 mean reward (100 episodes): -16.5100 best mean reward: -16.5100 current episode reward: -15.0000 episodes: 641 exploration: 0.31600 learning_rate: 0.00010 elapsed time: 2675.7 seconds (0.74 hours) Timestep: 770000 mean reward (100 episodes): -16.3300 best mean reward: -16.3000 current episode reward: -20.0000 episodes: 645 exploration: 0.30700 learning_rate: 0.00010 elapsed time: 2716.1 seconds (0.75 hours) Timestep: 780000 mean reward (100 episodes): -16.3700 best mean reward: -16.3000 current episode reward: -17.0000 episodes: 651 exploration: 0.29800 learning_rate: 0.00010 elapsed time: 2757.8 seconds (0.77 hours) Timestep: 790000 mean reward (100 episodes): -16.3100 best mean reward: -16.3000 current episode reward: -12.0000 episodes: 656 exploration: 0.28900 learning_rate: 0.00010 elapsed time: 2799.1 seconds (0.78 hours) Timestep: 800000 mean reward (100 episodes): -16.1700 best mean reward: -16.1700 current episode reward: -14.0000 episodes: 660 exploration: 0.28000 learning_rate: 0.00010 elapsed time: 2840.1 seconds (0.79 hours) Timestep: 810000 mean reward (100 episodes): -16.0200 best mean reward: -16.0200 current episode reward: -10.0000 episodes: 665 exploration: 0.27100 learning_rate: 0.00010 elapsed time: 2881.3 seconds (0.80 hours) Timestep: 820000 mean reward (100 episodes): -16.0000 best mean reward: -15.9300 current episode reward: -19.0000 episodes: 670 exploration: 0.26200 learning_rate: 0.00010 elapsed time: 2921.7 seconds (0.81 hours) Timestep: 830000 mean reward (100 episodes): -15.8600 best mean reward: -15.8600 current episode reward: -7.0000 episodes: 674 exploration: 0.25300 learning_rate: 0.00010 elapsed time: 2964.6 seconds (0.82 hours) Timestep: 840000 mean reward (100 episodes): -15.7700 best mean reward: -15.7700 current episode reward: -17.0000 episodes: 679 exploration: 0.24400 learning_rate: 0.00010 elapsed time: 3006.9 seconds (0.84 hours) Timestep: 850000 mean reward (100 episodes): -15.6100 best mean reward: -15.5700 current episode reward: -17.0000 episodes: 684 exploration: 0.23500 learning_rate: 0.00010 elapsed time: 3049.9 seconds (0.85 hours) Timestep: 860000 mean reward (100 episodes): -15.5700 best mean reward: -15.5500 current episode reward: -17.0000 episodes: 688 exploration: 0.22600 learning_rate: 0.00010 elapsed time: 3092.4 seconds (0.86 hours) Timestep: 870000 mean reward (100 episodes): -15.4300 best mean reward: -15.4300 current episode reward: -18.0000 episodes: 693 exploration: 0.21700 learning_rate: 0.00010 elapsed time: 3135.1 seconds (0.87 hours) Timestep: 880000 mean reward (100 episodes): -15.3800 best mean reward: -15.3800 current episode reward: -16.0000 episodes: 698 exploration: 0.20800 learning_rate: 0.00010 elapsed time: 3177.3 seconds (0.88 hours) Timestep: 890000 mean reward (100 episodes): -15.0500 best mean reward: -15.0500 current episode reward: -11.0000 episodes: 702 exploration: 0.19900 learning_rate: 0.00010 elapsed time: 3220.1 seconds (0.89 hours) Timestep: 900000 mean reward (100 episodes): -14.9600 best mean reward: -14.9600 current episode reward: -11.0000 episodes: 706 exploration: 0.19000 learning_rate: 0.00010 elapsed time: 3262.6 seconds (0.91 hours) Timestep: 910000 mean reward (100 episodes): -14.8600 best mean reward: -14.8600 current episode reward: -15.0000 episodes: 710 exploration: 0.18100 learning_rate: 0.00010 elapsed time: 3305.2 seconds (0.92 hours) Timestep: 920000 mean reward (100 episodes): -14.6700 best mean reward: -14.6700 current episode reward: -12.0000 episodes: 714 exploration: 0.17200 learning_rate: 0.00010 elapsed time: 3347.7 seconds (0.93 hours) Timestep: 930000 mean reward (100 episodes): -14.4400 best mean reward: -14.4200 current episode reward: -17.0000 episodes: 718 exploration: 0.16300 learning_rate: 0.00010 elapsed time: 3390.1 seconds (0.94 hours) Timestep: 940000 mean reward (100 episodes): -14.2700 best mean reward: -14.2700 current episode reward: -17.0000 episodes: 723 exploration: 0.15400 learning_rate: 0.00010 elapsed time: 3432.8 seconds (0.95 hours) Timestep: 950000 mean reward (100 episodes): -14.0000 best mean reward: -14.0000 current episode reward: -7.0000 episodes: 726 exploration: 0.14500 learning_rate: 0.00010 elapsed time: 3475.0 seconds (0.97 hours) Timestep: 960000 mean reward (100 episodes): -13.8200 best mean reward: -13.8200 current episode reward: -11.0000 episodes: 729 exploration: 0.13600 learning_rate: 0.00010 elapsed time: 3525.6 seconds (0.98 hours) Timestep: 970000 mean reward (100 episodes): -13.4900 best mean reward: -13.4900 current episode reward: -8.0000 episodes: 732 exploration: 0.12700 learning_rate: 0.00010 elapsed time: 3569.4 seconds (0.99 hours) Timestep: 980000 mean reward (100 episodes): -13.0900 best mean reward: -13.0900 current episode reward: -9.0000 episodes: 736 exploration: 0.11800 learning_rate: 0.00010 elapsed time: 3613.5 seconds (1.00 hours) Timestep: 990000 mean reward (100 episodes): -12.8700 best mean reward: -12.8700 current episode reward: -6.0000 episodes: 739 exploration: 0.10900 learning_rate: 0.00010 elapsed time: 3657.3 seconds (1.02 hours) Timestep: 1000000 mean reward (100 episodes): -12.6400 best mean reward: -12.6400 current episode reward: -13.0000 episodes: 742 exploration: 0.10000 learning_rate: 0.00010 elapsed time: 3701.7 seconds (1.03 hours) Timestep: 1010000 mean reward (100 episodes): -12.3800 best mean reward: -12.3800 current episode reward: -11.0000 episodes: 745 exploration: 0.09967 learning_rate: 0.00010 elapsed time: 3746.4 seconds (1.04 hours) Timestep: 1020000 mean reward (100 episodes): -12.1000 best mean reward: -12.1000 current episode reward: -1.0000 episodes: 748 exploration: 0.09935 learning_rate: 0.00010 elapsed time: 3791.4 seconds (1.05 hours) Timestep: 1030000 mean reward (100 episodes): -11.7200 best mean reward: -11.7200 current episode reward: 5.0000 episodes: 752 exploration: 0.09902 learning_rate: 0.00010 elapsed time: 3835.4 seconds (1.07 hours) Timestep: 1040000 mean reward (100 episodes): -11.4400 best mean reward: -11.4400 current episode reward: -8.0000 episodes: 755 exploration: 0.09869 learning_rate: 0.00010 elapsed time: 3879.8 seconds (1.08 hours) Timestep: 1050000 mean reward (100 episodes): -11.2000 best mean reward: -11.2000 current episode reward: 2.0000 episodes: 757 exploration: 0.09836 learning_rate: 0.00010 elapsed time: 3922.8 seconds (1.09 hours) Timestep: 1060000 mean reward (100 episodes): -11.0700 best mean reward: -11.0700 current episode reward: -8.0000 episodes: 761 exploration: 0.09804 learning_rate: 0.00009 elapsed time: 3966.6 seconds (1.10 hours) Timestep: 1070000 mean reward (100 episodes): -10.8300 best mean reward: -10.8300 current episode reward: -11.0000 episodes: 764 exploration: 0.09771 learning_rate: 0.00009 elapsed time: 4010.2 seconds (1.11 hours) Timestep: 1080000 mean reward (100 episodes): -10.6500 best mean reward: -10.6500 current episode reward: -2.0000 episodes: 766 exploration: 0.09738 learning_rate: 0.00009 elapsed time: 4053.1 seconds (1.13 hours) Timestep: 1090000 mean reward (100 episodes): -10.1300 best mean reward: -10.1300 current episode reward: -5.0000 episodes: 769 exploration: 0.09705 learning_rate: 0.00009 elapsed time: 4096.3 seconds (1.14 hours) Timestep: 1100000 mean reward (100 episodes): -9.7500 best mean reward: -9.7500 current episode reward: -5.0000 episodes: 772 exploration: 0.09673 learning_rate: 0.00009 elapsed time: 4139.3 seconds (1.15 hours) Timestep: 1110000 mean reward (100 episodes): -9.6100 best mean reward: -9.6100 current episode reward: 4.0000 episodes: 774 exploration: 0.09640 learning_rate: 0.00009 elapsed time: 4181.9 seconds (1.16 hours) Timestep: 1120000 mean reward (100 episodes): -9.2900 best mean reward: -9.2900 current episode reward: -9.0000 episodes: 778 exploration: 0.09607 learning_rate: 0.00009 elapsed time: 4225.3 seconds (1.17 hours) Timestep: 1130000 mean reward (100 episodes): -9.2400 best mean reward: -9.2400 current episode reward: -2.0000 episodes: 781 exploration: 0.09575 learning_rate: 0.00009 elapsed time: 4268.0 seconds (1.19 hours) Timestep: 1140000 mean reward (100 episodes): -9.1700 best mean reward: -9.1700 current episode reward: -10.0000 episodes: 785 exploration: 0.09542 learning_rate: 0.00009 elapsed time: 4311.9 seconds (1.20 hours) Timestep: 1150000 mean reward (100 episodes): -8.7800 best mean reward: -8.7800 current episode reward: 4.0000 episodes: 788 exploration: 0.09509 learning_rate: 0.00009 elapsed time: 4355.9 seconds (1.21 hours) Timestep: 1160000 mean reward (100 episodes): -8.5600 best mean reward: -8.5600 current episode reward: -12.0000 episodes: 791 exploration: 0.09476 learning_rate: 0.00009 elapsed time: 4399.1 seconds (1.22 hours) Timestep: 1170000 mean reward (100 episodes): -8.1000 best mean reward: -8.1000 current episode reward: 2.0000 episodes: 794 exploration: 0.09444 learning_rate: 0.00009 elapsed time: 4442.8 seconds (1.23 hours) Timestep: 1180000 mean reward (100 episodes): -7.6700 best mean reward: -7.6700 current episode reward: 7.0000 episodes: 796 exploration: 0.09411 learning_rate: 0.00009 elapsed time: 4486.1 seconds (1.25 hours) Timestep: 1190000 mean reward (100 episodes): -7.3200 best mean reward: -7.3200 current episode reward: 3.0000 episodes: 800 exploration: 0.09378 learning_rate: 0.00009 elapsed time: 4529.9 seconds (1.26 hours) Timestep: 1200000 mean reward (100 episodes): -7.1200 best mean reward: -7.1200 current episode reward: 4.0000 episodes: 802 exploration: 0.09345 learning_rate: 0.00009 elapsed time: 4573.0 seconds (1.27 hours) Timestep: 1210000 mean reward (100 episodes): -6.6100 best mean reward: -6.6100 current episode reward: 5.0000 episodes: 805 exploration: 0.09313 learning_rate: 0.00009 elapsed time: 4616.7 seconds (1.28 hours) Timestep: 1220000 mean reward (100 episodes): -6.3200 best mean reward: -6.3200 current episode reward: -6.0000 episodes: 808 exploration: 0.09280 learning_rate: 0.00009 elapsed time: 4660.3 seconds (1.29 hours) Timestep: 1230000 mean reward (100 episodes): -6.0000 best mean reward: -6.0000 current episode reward: 8.0000 episodes: 810 exploration: 0.09247 learning_rate: 0.00009 elapsed time: 4704.4 seconds (1.31 hours) Timestep: 1240000 mean reward (100 episodes): -5.5100 best mean reward: -5.5100 current episode reward: 1.0000 episodes: 813 exploration: 0.09215 learning_rate: 0.00009 elapsed time: 4747.3 seconds (1.32 hours) Timestep: 1250000 mean reward (100 episodes): -5.2700 best mean reward: -5.2700 current episode reward: -3.0000 episodes: 816 exploration: 0.09182 learning_rate: 0.00009 elapsed time: 4790.4 seconds (1.33 hours) Timestep: 1260000 mean reward (100 episodes): -4.9800 best mean reward: -4.9800 current episode reward: 1.0000 episodes: 819 exploration: 0.09149 learning_rate: 0.00009 elapsed time: 4834.1 seconds (1.34 hours) Timestep: 1270000 mean reward (100 episodes): -4.5700 best mean reward: -4.5700 current episode reward: -7.0000 episodes: 822 exploration: 0.09116 learning_rate: 0.00009 elapsed time: 4877.7 seconds (1.35 hours) Timestep: 1280000 mean reward (100 episodes): -4.2700 best mean reward: -4.2700 current episode reward: 1.0000 episodes: 825 exploration: 0.09084 learning_rate: 0.00009 elapsed time: 4922.1 seconds (1.37 hours) Timestep: 1290000 mean reward (100 episodes): -4.1400 best mean reward: -4.1300 current episode reward: -10.0000 episodes: 828 exploration: 0.09051 learning_rate: 0.00009 elapsed time: 4966.1 seconds (1.38 hours) Timestep: 1300000 mean reward (100 episodes): -3.8700 best mean reward: -3.8700 current episode reward: 9.0000 episodes: 830 exploration: 0.09018 learning_rate: 0.00009 elapsed time: 5010.1 seconds (1.39 hours) Timestep: 1310000 mean reward (100 episodes): -3.6800 best mean reward: -3.6800 current episode reward: 4.0000 episodes: 833 exploration: 0.08985 learning_rate: 0.00009 elapsed time: 5054.3 seconds (1.40 hours) Timestep: 1320000 mean reward (100 episodes): -3.3900 best mean reward: -3.3900 current episode reward: 4.0000 episodes: 835 exploration: 0.08953 learning_rate: 0.00009 elapsed time: 5097.5 seconds (1.42 hours) Timestep: 1330000 mean reward (100 episodes): -3.1100 best mean reward: -3.1100 current episode reward: -5.0000 episodes: 838 exploration: 0.08920 learning_rate: 0.00009 elapsed time: 5140.8 seconds (1.43 hours) Timestep: 1340000 mean reward (100 episodes): -3.0100 best mean reward: -2.9700 current episode reward: -6.0000 episodes: 841 exploration: 0.08887 learning_rate: 0.00009 elapsed time: 5184.4 seconds (1.44 hours) Timestep: 1350000 mean reward (100 episodes): -2.7600 best mean reward: -2.7600 current episode reward: 7.0000 episodes: 843 exploration: 0.08855 learning_rate: 0.00009 elapsed time: 5228.5 seconds (1.45 hours) Timestep: 1360000 mean reward (100 episodes): -2.4400 best mean reward: -2.4400 current episode reward: 7.0000 episodes: 846 exploration: 0.08822 learning_rate: 0.00009 elapsed time: 5272.0 seconds (1.46 hours) Timestep: 1370000 mean reward (100 episodes): -2.0000 best mean reward: -2.0000 current episode reward: 7.0000 episodes: 849 exploration: 0.08789 learning_rate: 0.00009 elapsed time: 5315.2 seconds (1.48 hours) Timestep: 1380000 mean reward (100 episodes): -1.7700 best mean reward: -1.7700 current episode reward: 3.0000 episodes: 851 exploration: 0.08756 learning_rate: 0.00009 elapsed time: 5359.0 seconds (1.49 hours) Timestep: 1390000 mean reward (100 episodes): -1.4500 best mean reward: -1.4500 current episode reward: 6.0000 episodes: 854 exploration: 0.08724 learning_rate: 0.00009 elapsed time: 5402.7 seconds (1.50 hours) Timestep: 1400000 mean reward (100 episodes): -1.3300 best mean reward: -1.3300 current episode reward: -2.0000 episodes: 856 exploration: 0.08691 learning_rate: 0.00009 elapsed time: 5446.7 seconds (1.51 hours) Timestep: 1410000 mean reward (100 episodes): -1.1500 best mean reward: -1.1500 current episode reward: 5.0000 episodes: 858 exploration: 0.08658 learning_rate: 0.00009 elapsed time: 5489.5 seconds (1.52 hours) Timestep: 1420000 mean reward (100 episodes): -0.7600 best mean reward: -0.7600 current episode reward: 2.0000 episodes: 861 exploration: 0.08625 learning_rate: 0.00009 elapsed time: 5533.9 seconds (1.54 hours) Timestep: 1430000 mean reward (100 episodes): -0.6400 best mean reward: -0.6400 current episode reward: 3.0000 episodes: 863 exploration: 0.08593 learning_rate: 0.00009 elapsed time: 5576.9 seconds (1.55 hours) Timestep: 1440000 mean reward (100 episodes): -0.4000 best mean reward: -0.4000 current episode reward: 9.0000 episodes: 865 exploration: 0.08560 learning_rate: 0.00009 elapsed time: 5620.3 seconds (1.56 hours) Timestep: 1450000 mean reward (100 episodes): -0.4100 best mean reward: -0.3400 current episode reward: -4.0000 episodes: 868 exploration: 0.08527 learning_rate: 0.00009 elapsed time: 5664.4 seconds (1.57 hours) Timestep: 1460000 mean reward (100 episodes): -0.1000 best mean reward: -0.1000 current episode reward: 5.0000 episodes: 871 exploration: 0.08495 learning_rate: 0.00009 elapsed time: 5708.1 seconds (1.59 hours) Timestep: 1470000 mean reward (100 episodes): 0.2000 best mean reward: 0.2000 current episode reward: 5.0000 episodes: 874 exploration: 0.08462 learning_rate: 0.00009 elapsed time: 5751.8 seconds (1.60 hours) Timestep: 1480000 mean reward (100 episodes): 0.4100 best mean reward: 0.4100 current episode reward: 6.0000 episodes: 877 exploration: 0.08429 learning_rate: 0.00009 elapsed time: 5795.8 seconds (1.61 hours) Timestep: 1490000 mean reward (100 episodes): 1.1200 best mean reward: 1.1200 current episode reward: 12.0000 episodes: 880 exploration: 0.08396 learning_rate: 0.00009 elapsed time: 5839.3 seconds (1.62 hours) Timestep: 1500000 mean reward (100 episodes): 1.5100 best mean reward: 1.5100 current episode reward: -5.0000 episodes: 883 exploration: 0.08364 learning_rate: 0.00009 elapsed time: 5882.8 seconds (1.63 hours) Timestep: 1510000 mean reward (100 episodes): 1.9000 best mean reward: 1.9000 current episode reward: -2.0000 episodes: 885 exploration: 0.08331 learning_rate: 0.00009 elapsed time: 5926.4 seconds (1.65 hours) Timestep: 1520000 mean reward (100 episodes): 2.2300 best mean reward: 2.2500 current episode reward: 2.0000 episodes: 888 exploration: 0.08298 learning_rate: 0.00009 elapsed time: 5970.3 seconds (1.66 hours) Timestep: 1530000 mean reward (100 episodes): 2.5600 best mean reward: 2.5600 current episode reward: 5.0000 episodes: 891 exploration: 0.08265 learning_rate: 0.00009 elapsed time: 6014.2 seconds (1.67 hours) Timestep: 1540000 mean reward (100 episodes): 2.7700 best mean reward: 2.7700 current episode reward: 10.0000 episodes: 893 exploration: 0.08233 learning_rate: 0.00009 elapsed time: 6057.9 seconds (1.68 hours) Timestep: 1550000 mean reward (100 episodes): 3.1100 best mean reward: 3.1100 current episode reward: 16.0000 episodes: 897 exploration: 0.08200 learning_rate: 0.00009 elapsed time: 6100.9 seconds (1.69 hours) Timestep: 1560000 mean reward (100 episodes): 3.5800 best mean reward: 3.5800 current episode reward: 6.0000 episodes: 900 exploration: 0.08167 learning_rate: 0.00009 elapsed time: 6145.0 seconds (1.71 hours) Timestep: 1570000 mean reward (100 episodes): 3.8500 best mean reward: 3.8500 current episode reward: 14.0000 episodes: 904 exploration: 0.08135 learning_rate: 0.00009 elapsed time: 6189.0 seconds (1.72 hours) Timestep: 1580000 mean reward (100 episodes): 4.2200 best mean reward: 4.2200 current episode reward: 9.0000 episodes: 907 exploration: 0.08102 learning_rate: 0.00009 elapsed time: 6231.7 seconds (1.73 hours) Timestep: 1590000 mean reward (100 episodes): 4.7500 best mean reward: 4.7500 current episode reward: 12.0000 episodes: 911 exploration: 0.08069 learning_rate: 0.00009 elapsed time: 6275.7 seconds (1.74 hours) Timestep: 1600000 mean reward (100 episodes): 5.0700 best mean reward: 5.0700 current episode reward: 12.0000 episodes: 915 exploration: 0.08036 learning_rate: 0.00009 elapsed time: 6320.2 seconds (1.76 hours) Timestep: 1610000 mean reward (100 episodes): 5.7100 best mean reward: 5.7100 current episode reward: 14.0000 episodes: 919 exploration: 0.08004 learning_rate: 0.00009 elapsed time: 6363.9 seconds (1.77 hours) Timestep: 1620000 mean reward (100 episodes): 5.9700 best mean reward: 5.9700 current episode reward: 10.0000 episodes: 922 exploration: 0.07971 learning_rate: 0.00009 elapsed time: 6408.1 seconds (1.78 hours) Timestep: 1630000 mean reward (100 episodes): 6.3400 best mean reward: 6.3400 current episode reward: 10.0000 episodes: 925 exploration: 0.07938 learning_rate: 0.00009 elapsed time: 6451.4 seconds (1.79 hours) Timestep: 1640000 mean reward (100 episodes): 7.0100 best mean reward: 7.0100 current episode reward: 15.0000 episodes: 929 exploration: 0.07905 learning_rate: 0.00009 elapsed time: 6495.3 seconds (1.80 hours) Timestep: 1650000 mean reward (100 episodes): 7.5900 best mean reward: 7.5900 current episode reward: 17.0000 episodes: 934 exploration: 0.07873 learning_rate: 0.00009 elapsed time: 6539.8 seconds (1.82 hours) Timestep: 1660000 mean reward (100 episodes): 8.1900 best mean reward: 8.1900 current episode reward: 17.0000 episodes: 938 exploration: 0.07840 learning_rate: 0.00008 elapsed time: 6584.0 seconds (1.83 hours) Timestep: 1670000 mean reward (100 episodes): 8.9500 best mean reward: 8.9500 current episode reward: 14.0000 episodes: 943 exploration: 0.07807 learning_rate: 0.00008 elapsed time: 6628.5 seconds (1.84 hours) Timestep: 1680000 mean reward (100 episodes): 9.3600 best mean reward: 9.3600 current episode reward: 19.0000 episodes: 947 exploration: 0.07775 learning_rate: 0.00008 elapsed time: 6673.0 seconds (1.85 hours) Timestep: 1690000 mean reward (100 episodes): 9.9400 best mean reward: 9.9400 current episode reward: 14.0000 episodes: 952 exploration: 0.07742 learning_rate: 0.00008 elapsed time: 6717.4 seconds (1.87 hours) Timestep: 1700000 mean reward (100 episodes): 10.4700 best mean reward: 10.4700 current episode reward: 17.0000 episodes: 956 exploration: 0.07709 learning_rate: 0.00008 elapsed time: 6761.8 seconds (1.88 hours) Timestep: 1710000 mean reward (100 episodes): 11.1400 best mean reward: 11.1400 current episode reward: 20.0000 episodes: 961 exploration: 0.07676 learning_rate: 0.00008 elapsed time: 6805.4 seconds (1.89 hours) Timestep: 1720000 mean reward (100 episodes): 11.5800 best mean reward: 11.5800 current episode reward: 16.0000 episodes: 965 exploration: 0.07644 learning_rate: 0.00008 elapsed time: 6848.8 seconds (1.90 hours) Timestep: 1730000 mean reward (100 episodes): 12.1700 best mean reward: 12.1700 current episode reward: 16.0000 episodes: 970 exploration: 0.07611 learning_rate: 0.00008 elapsed time: 6893.3 seconds (1.91 hours) Timestep: 1740000 mean reward (100 episodes): 12.5800 best mean reward: 12.5800 current episode reward: 16.0000 episodes: 974 exploration: 0.07578 learning_rate: 0.00008 elapsed time: 6937.3 seconds (1.93 hours) Timestep: 1750000 mean reward (100 episodes): 13.1500 best mean reward: 13.1500 current episode reward: 18.0000 episodes: 979 exploration: 0.07545 learning_rate: 0.00008 elapsed time: 6981.6 seconds (1.94 hours) Timestep: 1760000 mean reward (100 episodes): 13.6000 best mean reward: 13.6000 current episode reward: 18.0000 episodes: 984 exploration: 0.07513 learning_rate: 0.00008 elapsed time: 7025.8 seconds (1.95 hours) Timestep: 1770000 mean reward (100 episodes): 14.2200 best mean reward: 14.2200 current episode reward: 14.0000 episodes: 989 exploration: 0.07480 learning_rate: 0.00008 elapsed time: 7069.5 seconds (1.96 hours) Timestep: 1780000 mean reward (100 episodes): 14.5800 best mean reward: 14.5800 current episode reward: 17.0000 episodes: 993 exploration: 0.07447 learning_rate: 0.00008 elapsed time: 7113.4 seconds (1.98 hours) Timestep: 1790000 mean reward (100 episodes): 14.7100 best mean reward: 14.7200 current episode reward: 15.0000 episodes: 997 exploration: 0.07415 learning_rate: 0.00008 elapsed time: 7157.2 seconds (1.99 hours) Timestep: 1800000 mean reward (100 episodes): 15.1400 best mean reward: 15.1400 current episode reward: 18.0000 episodes: 1002 exploration: 0.07382 learning_rate: 0.00008 elapsed time: 7207.2 seconds (2.00 hours) Timestep: 1810000 mean reward (100 episodes): 15.1900 best mean reward: 15.1900 current episode reward: 20.0000 episodes: 1006 exploration: 0.07349 learning_rate: 0.00008 elapsed time: 7251.2 seconds (2.01 hours) Timestep: 1820000 mean reward (100 episodes): 15.3700 best mean reward: 15.3700 current episode reward: 17.0000 episodes: 1011 exploration: 0.07316 learning_rate: 0.00008 elapsed time: 7294.8 seconds (2.03 hours) Timestep: 1830000 mean reward (100 episodes): 15.6600 best mean reward: 15.6700 current episode reward: 13.0000 episodes: 1016 exploration: 0.07284 learning_rate: 0.00008 elapsed time: 7338.2 seconds (2.04 hours) Timestep: 1840000 mean reward (100 episodes): 15.7900 best mean reward: 15.7900 current episode reward: 15.0000 episodes: 1020 exploration: 0.07251 learning_rate: 0.00008 elapsed time: 7381.3 seconds (2.05 hours) Timestep: 1850000 mean reward (100 episodes): 16.0400 best mean reward: 16.0400 current episode reward: 18.0000 episodes: 1025 exploration: 0.07218 learning_rate: 0.00008 elapsed time: 7425.2 seconds (2.06 hours) Timestep: 1860000 mean reward (100 episodes): 16.2400 best mean reward: 16.2400 current episode reward: 16.0000 episodes: 1030 exploration: 0.07185 learning_rate: 0.00008 elapsed time: 7469.4 seconds (2.07 hours) Timestep: 1870000 mean reward (100 episodes): 16.1700 best mean reward: 16.2400 current episode reward: 15.0000 episodes: 1034 exploration: 0.07153 learning_rate: 0.00008 elapsed time: 7512.7 seconds (2.09 hours) Timestep: 1880000 mean reward (100 episodes): 16.2000 best mean reward: 16.2500 current episode reward: 12.0000 episodes: 1038 exploration: 0.07120 learning_rate: 0.00008 elapsed time: 7557.7 seconds (2.10 hours) Timestep: 1890000 mean reward (100 episodes): 16.2400 best mean reward: 16.2500 current episode reward: 16.0000 episodes: 1043 exploration: 0.07087 learning_rate: 0.00008 elapsed time: 7602.0 seconds (2.11 hours) Timestep: 1900000 mean reward (100 episodes): 16.2900 best mean reward: 16.3500 current episode reward: 13.0000 episodes: 1047 exploration: 0.07055 learning_rate: 0.00008 elapsed time: 7646.1 seconds (2.12 hours) Timestep: 1910000 mean reward (100 episodes): 16.3000 best mean reward: 16.3500 current episode reward: 12.0000 episodes: 1051 exploration: 0.07022 learning_rate: 0.00008 elapsed time: 7689.9 seconds (2.14 hours) Timestep: 1920000 mean reward (100 episodes): 16.2500 best mean reward: 16.3500 current episode reward: 14.0000 episodes: 1056 exploration: 0.06989 learning_rate: 0.00008 elapsed time: 7733.6 seconds (2.15 hours) Timestep: 1930000 mean reward (100 episodes): 16.3000 best mean reward: 16.3500 current episode reward: 19.0000 episodes: 1061 exploration: 0.06956 learning_rate: 0.00008 elapsed time: 7777.3 seconds (2.16 hours) Timestep: 1940000 mean reward (100 episodes): 16.4000 best mean reward: 16.4000 current episode reward: 18.0000 episodes: 1065 exploration: 0.06924 learning_rate: 0.00008 elapsed time: 7821.6 seconds (2.17 hours) Timestep: 1950000 mean reward (100 episodes): 16.4200 best mean reward: 16.4200 current episode reward: 19.0000 episodes: 1070 exploration: 0.06891 learning_rate: 0.00008 elapsed time: 7866.0 seconds (2.19 hours) Timestep: 1960000 mean reward (100 episodes): 16.5000 best mean reward: 16.5000 current episode reward: 18.0000 episodes: 1075 exploration: 0.06858 learning_rate: 0.00008 elapsed time: 7909.7 seconds (2.20 hours) Timestep: 1970000 mean reward (100 episodes): 16.4700 best mean reward: 16.5000 current episode reward: 18.0000 episodes: 1079 exploration: 0.06825 learning_rate: 0.00008 elapsed time: 7953.1 seconds (2.21 hours) Timestep: 1980000 mean reward (100 episodes): 16.4600 best mean reward: 16.5000 current episode reward: 20.0000 episodes: 1084 exploration: 0.06793 learning_rate: 0.00008 elapsed time: 7996.2 seconds (2.22 hours) Timestep: 1990000 mean reward (100 episodes): 16.4500 best mean reward: 16.5000 current episode reward: 20.0000 episodes: 1089 exploration: 0.06760 learning_rate: 0.00008 elapsed time: 8040.0 seconds (2.23 hours) Timestep: 2000000 mean reward (100 episodes): 16.4600 best mean reward: 16.5000 current episode reward: 14.0000 episodes: 1094 exploration: 0.06727 learning_rate: 0.00008 elapsed time: 8083.6 seconds (2.25 hours) Timestep: 2010000 mean reward (100 episodes): 16.5100 best mean reward: 16.5100 current episode reward: 17.0000 episodes: 1099 exploration: 0.06695 learning_rate: 0.00008 elapsed time: 8126.0 seconds (2.26 hours) Timestep: 2020000 mean reward (100 episodes): 16.5600 best mean reward: 16.5700 current episode reward: 16.0000 episodes: 1104 exploration: 0.06662 learning_rate: 0.00008 elapsed time: 8170.2 seconds (2.27 hours) Timestep: 2030000 mean reward (100 episodes): 16.6200 best mean reward: 16.6300 current episode reward: 17.0000 episodes: 1109 exploration: 0.06629 learning_rate: 0.00008 elapsed time: 8214.3 seconds (2.28 hours) Timestep: 2040000 mean reward (100 episodes): 16.6400 best mean reward: 16.6600 current episode reward: 20.0000 episodes: 1113 exploration: 0.06596 learning_rate: 0.00008 elapsed time: 8257.9 seconds (2.29 hours) Timestep: 2050000 mean reward (100 episodes): 16.6800 best mean reward: 16.6800 current episode reward: 17.0000 episodes: 1118 exploration: 0.06564 learning_rate: 0.00008 elapsed time: 8302.0 seconds (2.31 hours) Timestep: 2060000 mean reward (100 episodes): 16.7700 best mean reward: 16.7700 current episode reward: 18.0000 episodes: 1122 exploration: 0.06531 learning_rate: 0.00008 elapsed time: 8345.1 seconds (2.32 hours) Timestep: 2070000 mean reward (100 episodes): 16.7600 best mean reward: 16.8100 current episode reward: 17.0000 episodes: 1127 exploration: 0.06498 learning_rate: 0.00008 elapsed time: 8388.8 seconds (2.33 hours) Timestep: 2080000 mean reward (100 episodes): 16.7700 best mean reward: 16.8100 current episode reward: 17.0000 episodes: 1132 exploration: 0.06465 learning_rate: 0.00008 elapsed time: 8433.1 seconds (2.34 hours) Timestep: 2090000 mean reward (100 episodes): 16.7900 best mean reward: 16.8300 current episode reward: 20.0000 episodes: 1137 exploration: 0.06433 learning_rate: 0.00008 elapsed time: 8476.5 seconds (2.35 hours) Timestep: 2100000 mean reward (100 episodes): 16.9300 best mean reward: 16.9300 current episode reward: 18.0000 episodes: 1141 exploration: 0.06400 learning_rate: 0.00008 elapsed time: 8519.9 seconds (2.37 hours) Timestep: 2110000 mean reward (100 episodes): 16.9500 best mean reward: 16.9600 current episode reward: 16.0000 episodes: 1146 exploration: 0.06367 learning_rate: 0.00008 elapsed time: 8563.4 seconds (2.38 hours) Timestep: 2120000 mean reward (100 episodes): 16.9900 best mean reward: 16.9900 current episode reward: 17.0000 episodes: 1150 exploration: 0.06335 learning_rate: 0.00008 elapsed time: 8607.4 seconds (2.39 hours) Timestep: 2130000 mean reward (100 episodes): 17.0900 best mean reward: 17.0900 current episode reward: 18.0000 episodes: 1155 exploration: 0.06302 learning_rate: 0.00008 elapsed time: 8650.4 seconds (2.40 hours) Timestep: 2140000 mean reward (100 episodes): 17.2300 best mean reward: 17.2300 current episode reward: 18.0000 episodes: 1160 exploration: 0.06269 learning_rate: 0.00008 elapsed time: 8694.4 seconds (2.42 hours) Timestep: 2150000 mean reward (100 episodes): 17.1300 best mean reward: 17.2300 current episode reward: 18.0000 episodes: 1165 exploration: 0.06236 learning_rate: 0.00008 elapsed time: 8738.3 seconds (2.43 hours) Timestep: 2160000 mean reward (100 episodes): 17.1900 best mean reward: 17.2300 current episode reward: 16.0000 episodes: 1170 exploration: 0.06204 learning_rate: 0.00008 elapsed time: 8781.6 seconds (2.44 hours) Timestep: 2170000 mean reward (100 episodes): 17.2100 best mean reward: 17.2300 current episode reward: 20.0000 episodes: 1175 exploration: 0.06171 learning_rate: 0.00008 elapsed time: 8825.3 seconds (2.45 hours) Timestep: 2180000 mean reward (100 episodes): 17.2400 best mean reward: 17.2700 current episode reward: 13.0000 episodes: 1180 exploration: 0.06138 learning_rate: 0.00008 elapsed time: 8868.6 seconds (2.46 hours) Timestep: 2190000 mean reward (100 episodes): 17.2800 best mean reward: 17.3000 current episode reward: 19.0000 episodes: 1185 exploration: 0.06105 learning_rate: 0.00008 elapsed time: 8912.4 seconds (2.48 hours) Timestep: 2200000 mean reward (100 episodes): 17.3100 best mean reward: 17.3300 current episode reward: 16.0000 episodes: 1190 exploration: 0.06073 learning_rate: 0.00008 elapsed time: 8955.9 seconds (2.49 hours) Timestep: 2210000 mean reward (100 episodes): 17.3600 best mean reward: 17.3600 current episode reward: 19.0000 episodes: 1195 exploration: 0.06040 learning_rate: 0.00008 elapsed time: 8999.7 seconds (2.50 hours) Timestep: 2220000 mean reward (100 episodes): 17.3900 best mean reward: 17.4100 current episode reward: 17.0000 episodes: 1200 exploration: 0.06007 learning_rate: 0.00008 elapsed time: 9042.8 seconds (2.51 hours) Timestep: 2230000 mean reward (100 episodes): 17.4100 best mean reward: 17.4100 current episode reward: 19.0000 episodes: 1205 exploration: 0.05975 learning_rate: 0.00008 elapsed time: 9086.5 seconds (2.52 hours) Timestep: 2240000 mean reward (100 episodes): 17.2500 best mean reward: 17.4100 current episode reward: 16.0000 episodes: 1210 exploration: 0.05942 learning_rate: 0.00008 elapsed time: 9130.2 seconds (2.54 hours) Timestep: 2250000 mean reward (100 episodes): 17.3000 best mean reward: 17.4100 current episode reward: 20.0000 episodes: 1214 exploration: 0.05909 learning_rate: 0.00008 elapsed time: 9173.2 seconds (2.55 hours) Timestep: 2260000 mean reward (100 episodes): 17.3000 best mean reward: 17.4100 current episode reward: 18.0000 episodes: 1219 exploration: 0.05876 learning_rate: 0.00007 elapsed time: 9216.8 seconds (2.56 hours) Timestep: 2270000 mean reward (100 episodes): 17.3400 best mean reward: 17.4100 current episode reward: 14.0000 episodes: 1224 exploration: 0.05844 learning_rate: 0.00007 elapsed time: 9260.5 seconds (2.57 hours) Timestep: 2280000 mean reward (100 episodes): 17.3600 best mean reward: 17.4100 current episode reward: 19.0000 episodes: 1229 exploration: 0.05811 learning_rate: 0.00007 elapsed time: 9304.2 seconds (2.58 hours) Timestep: 2290000 mean reward (100 episodes): 17.4000 best mean reward: 17.4100 current episode reward: 20.0000 episodes: 1234 exploration: 0.05778 learning_rate: 0.00007 elapsed time: 9348.3 seconds (2.60 hours) Timestep: 2300000 mean reward (100 episodes): 17.3800 best mean reward: 17.4700 current episode reward: 18.0000 episodes: 1239 exploration: 0.05745 learning_rate: 0.00007 elapsed time: 9392.4 seconds (2.61 hours) Timestep: 2310000 mean reward (100 episodes): 17.4200 best mean reward: 17.4700 current episode reward: 16.0000 episodes: 1243 exploration: 0.05713 learning_rate: 0.00007 elapsed time: 9435.7 seconds (2.62 hours) Timestep: 2320000 mean reward (100 episodes): 17.5200 best mean reward: 17.5200 current episode reward: 20.0000 episodes: 1249 exploration: 0.05680 learning_rate: 0.00007 elapsed time: 9480.0 seconds (2.63 hours) Timestep: 2330000 mean reward (100 episodes): 17.6200 best mean reward: 17.6200 current episode reward: 19.0000 episodes: 1254 exploration: 0.05647 learning_rate: 0.00007 elapsed time: 9524.6 seconds (2.65 hours) Timestep: 2340000 mean reward (100 episodes): 17.6100 best mean reward: 17.6300 current episode reward: 21.0000 episodes: 1259 exploration: 0.05615 learning_rate: 0.00007 elapsed time: 9568.9 seconds (2.66 hours) Timestep: 2350000 mean reward (100 episodes): 17.7300 best mean reward: 17.7300 current episode reward: 19.0000 episodes: 1264 exploration: 0.05582 learning_rate: 0.00007 elapsed time: 9613.2 seconds (2.67 hours) Timestep: 2360000 mean reward (100 episodes): 17.7700 best mean reward: 17.7700 current episode reward: 20.0000 episodes: 1269 exploration: 0.05549 learning_rate: 0.00007 elapsed time: 9656.4 seconds (2.68 hours) Timestep: 2370000 mean reward (100 episodes): 17.8200 best mean reward: 17.8200 current episode reward: 18.0000 episodes: 1274 exploration: 0.05516 learning_rate: 0.00007 elapsed time: 9701.5 seconds (2.69 hours) Timestep: 2380000 mean reward (100 episodes): 17.7100 best mean reward: 17.8200 current episode reward: 11.0000 episodes: 1279 exploration: 0.05484 learning_rate: 0.00007 elapsed time: 9746.1 seconds (2.71 hours) Timestep: 2390000 mean reward (100 episodes): 17.7600 best mean reward: 17.8200 current episode reward: 19.0000 episodes: 1283 exploration: 0.05451 learning_rate: 0.00007 elapsed time: 9790.7 seconds (2.72 hours) Timestep: 2400000 mean reward (100 episodes): 17.6600 best mean reward: 17.8200 current episode reward: 19.0000 episodes: 1288 exploration: 0.05418 learning_rate: 0.00007 elapsed time: 9835.1 seconds (2.73 hours) Timestep: 2410000 mean reward (100 episodes): 17.7000 best mean reward: 17.8200 current episode reward: 17.0000 episodes: 1293 exploration: 0.05385 learning_rate: 0.00007 elapsed time: 9878.7 seconds (2.74 hours) Timestep: 2420000 mean reward (100 episodes): 17.7300 best mean reward: 17.8200 current episode reward: 17.0000 episodes: 1298 exploration: 0.05353 learning_rate: 0.00007 elapsed time: 9922.2 seconds (2.76 hours) Timestep: 2430000 mean reward (100 episodes): 17.8000 best mean reward: 17.8200 current episode reward: 20.0000 episodes: 1303 exploration: 0.05320 learning_rate: 0.00007 elapsed time: 9966.4 seconds (2.77 hours) Timestep: 2440000 mean reward (100 episodes): 17.8600 best mean reward: 17.8600 current episode reward: 20.0000 episodes: 1308 exploration: 0.05287 learning_rate: 0.00007 elapsed time: 10010.1 seconds (2.78 hours) Timestep: 2450000 mean reward (100 episodes): 18.0100 best mean reward: 18.0100 current episode reward: 21.0000 episodes: 1313 exploration: 0.05255 learning_rate: 0.00007 elapsed time: 10053.5 seconds (2.79 hours) Timestep: 2460000 mean reward (100 episodes): 17.9800 best mean reward: 18.0100 current episode reward: 19.0000 episodes: 1317 exploration: 0.05222 learning_rate: 0.00007 elapsed time: 10097.3 seconds (2.80 hours) Timestep: 2470000 mean reward (100 episodes): 17.9300 best mean reward: 18.0100 current episode reward: 18.0000 episodes: 1323 exploration: 0.05189 learning_rate: 0.00007 elapsed time: 10141.0 seconds (2.82 hours) Timestep: 2480000 mean reward (100 episodes): 18.0700 best mean reward: 18.0700 current episode reward: 19.0000 episodes: 1328 exploration: 0.05156 learning_rate: 0.00007 elapsed time: 10184.1 seconds (2.83 hours) Timestep: 2490000 mean reward (100 episodes): 18.0600 best mean reward: 18.0700 current episode reward: 19.0000 episodes: 1333 exploration: 0.05124 learning_rate: 0.00007 elapsed time: 10228.5 seconds (2.84 hours) Timestep: 2500000 mean reward (100 episodes): 18.0900 best mean reward: 18.0900 current episode reward: 19.0000 episodes: 1338 exploration: 0.05091 learning_rate: 0.00007 elapsed time: 10272.1 seconds (2.85 hours) Timestep: 2510000 mean reward (100 episodes): 18.1200 best mean reward: 18.1200 current episode reward: 18.0000 episodes: 1343 exploration: 0.05058 learning_rate: 0.00007 elapsed time: 10315.8 seconds (2.87 hours) Timestep: 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learning_rate: 0.00007 elapsed time: 10536.9 seconds (2.93 hours) Timestep: 2570000 mean reward (100 episodes): 17.9600 best mean reward: 18.1400 current episode reward: 19.0000 episodes: 1373 exploration: 0.04862 learning_rate: 0.00007 elapsed time: 10581.1 seconds (2.94 hours) Timestep: 2580000 mean reward (100 episodes): 17.9100 best mean reward: 18.1400 current episode reward: 17.0000 episodes: 1378 exploration: 0.04829 learning_rate: 0.00007 elapsed time: 10625.3 seconds (2.95 hours) Timestep: 2590000 mean reward (100 episodes): 17.9700 best mean reward: 18.1400 current episode reward: 19.0000 episodes: 1382 exploration: 0.04796 learning_rate: 0.00007 elapsed time: 10669.1 seconds (2.96 hours) Timestep: 2600000 mean reward (100 episodes): 18.0200 best mean reward: 18.1400 current episode reward: 19.0000 episodes: 1387 exploration: 0.04764 learning_rate: 0.00007 elapsed time: 10713.1 seconds (2.98 hours) Timestep: 2610000 mean reward (100 episodes): 17.9800 best mean reward: 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Timestep: 2660000 mean reward (100 episodes): 18.0700 best mean reward: 18.1400 current episode reward: 18.0000 episodes: 1418 exploration: 0.04567 learning_rate: 0.00007 elapsed time: 10979.8 seconds (3.05 hours) Timestep: 2670000 mean reward (100 episodes): 18.0900 best mean reward: 18.1400 current episode reward: 20.0000 episodes: 1423 exploration: 0.04535 learning_rate: 0.00007 elapsed time: 11023.8 seconds (3.06 hours) Timestep: 2680000 mean reward (100 episodes): 18.1200 best mean reward: 18.1400 current episode reward: 19.0000 episodes: 1428 exploration: 0.04502 learning_rate: 0.00007 elapsed time: 11067.5 seconds (3.07 hours) Timestep: 2690000 mean reward (100 episodes): 18.2700 best mean reward: 18.2700 current episode reward: 21.0000 episodes: 1434 exploration: 0.04469 learning_rate: 0.00007 elapsed time: 11111.5 seconds (3.09 hours) Timestep: 2700000 mean reward (100 episodes): 18.3000 best mean reward: 18.3000 current episode reward: 20.0000 episodes: 1439 exploration: 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hours) Timestep: 2800000 mean reward (100 episodes): 18.5800 best mean reward: 18.6000 current episode reward: 19.0000 episodes: 1490 exploration: 0.04109 learning_rate: 0.00007 elapsed time: 11596.1 seconds (3.22 hours) Timestep: 2810000 mean reward (100 episodes): 18.6800 best mean reward: 18.6800 current episode reward: 19.0000 episodes: 1495 exploration: 0.04076 learning_rate: 0.00007 elapsed time: 11639.9 seconds (3.23 hours) Timestep: 2820000 mean reward (100 episodes): 18.6800 best mean reward: 18.6800 current episode reward: 19.0000 episodes: 1500 exploration: 0.04044 learning_rate: 0.00007 elapsed time: 11683.5 seconds (3.25 hours) Timestep: 2830000 mean reward (100 episodes): 18.7700 best mean reward: 18.7700 current episode reward: 20.0000 episodes: 1505 exploration: 0.04011 learning_rate: 0.00007 elapsed time: 11727.8 seconds (3.26 hours) Timestep: 2840000 mean reward (100 episodes): 18.7600 best mean reward: 18.8400 current episode reward: 19.0000 episodes: 1511 exploration: 0.03978 learning_rate: 0.00007 elapsed time: 11771.5 seconds (3.27 hours) Timestep: 2850000 mean reward (100 episodes): 18.7900 best mean reward: 18.8400 current episode reward: 19.0000 episodes: 1516 exploration: 0.03945 learning_rate: 0.00007 elapsed time: 11815.6 seconds (3.28 hours) Timestep: 2860000 mean reward (100 episodes): 18.8800 best mean reward: 18.8800 current episode reward: 19.0000 episodes: 1522 exploration: 0.03913 learning_rate: 0.00006 elapsed time: 11859.8 seconds (3.29 hours) Timestep: 2870000 mean reward (100 episodes): 18.8300 best mean reward: 18.8800 current episode reward: 16.0000 episodes: 1527 exploration: 0.03880 learning_rate: 0.00006 elapsed time: 11903.9 seconds (3.31 hours) Timestep: 2880000 mean reward (100 episodes): 18.7900 best mean reward: 18.8800 current episode reward: 18.0000 episodes: 1532 exploration: 0.03847 learning_rate: 0.00006 elapsed time: 11948.5 seconds (3.32 hours) Timestep: 2890000 mean reward (100 episodes): 18.6800 best mean reward: 18.8800 current episode reward: 17.0000 episodes: 1537 exploration: 0.03815 learning_rate: 0.00006 elapsed time: 11992.7 seconds (3.33 hours) Timestep: 2900000 mean reward (100 episodes): 18.7200 best mean reward: 18.8800 current episode reward: 19.0000 episodes: 1542 exploration: 0.03782 learning_rate: 0.00006 elapsed time: 12036.8 seconds (3.34 hours) Timestep: 2910000 mean reward (100 episodes): 18.7800 best mean reward: 18.8800 current episode reward: 20.0000 episodes: 1548 exploration: 0.03749 learning_rate: 0.00006 elapsed time: 12081.0 seconds (3.36 hours) Timestep: 2920000 mean reward (100 episodes): 18.7800 best mean reward: 18.8800 current episode reward: 19.0000 episodes: 1553 exploration: 0.03716 learning_rate: 0.00006 elapsed time: 12124.6 seconds (3.37 hours) Timestep: 2930000 mean reward (100 episodes): 18.8300 best mean reward: 18.8800 current episode reward: 21.0000 episodes: 1558 exploration: 0.03684 learning_rate: 0.00006 elapsed time: 12168.0 seconds (3.38 hours) Timestep: 2940000 mean reward (100 episodes): 18.9100 best mean reward: 18.9400 current episode reward: 17.0000 episodes: 1564 exploration: 0.03651 learning_rate: 0.00006 elapsed time: 12212.5 seconds (3.39 hours) Timestep: 2950000 mean reward (100 episodes): 18.9900 best mean reward: 19.0000 current episode reward: 19.0000 episodes: 1569 exploration: 0.03618 learning_rate: 0.00006 elapsed time: 12257.1 seconds (3.40 hours) Timestep: 2960000 mean reward (100 episodes): 19.0800 best mean reward: 19.0800 current episode reward: 20.0000 episodes: 1575 exploration: 0.03585 learning_rate: 0.00006 elapsed time: 12301.6 seconds (3.42 hours) Timestep: 2970000 mean reward (100 episodes): 19.1200 best mean reward: 19.1200 current episode reward: 20.0000 episodes: 1580 exploration: 0.03553 learning_rate: 0.00006 elapsed time: 12345.4 seconds (3.43 hours) Timestep: 2980000 mean reward (100 episodes): 19.1000 best mean reward: 19.1400 current episode reward: 15.0000 episodes: 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episodes): 20.6800 best mean reward: 20.8500 current episode reward: 19.0000 episodes: 4069 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31236.1 seconds (8.68 hours) Timestep: 7240000 mean reward (100 episodes): 20.7000 best mean reward: 20.8500 current episode reward: 21.0000 episodes: 4076 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31280.0 seconds (8.69 hours) Timestep: 7250000 mean reward (100 episodes): 20.7000 best mean reward: 20.8500 current episode reward: 21.0000 episodes: 4082 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31324.0 seconds (8.70 hours) Timestep: 7260000 mean reward (100 episodes): 20.7400 best mean reward: 20.8500 current episode reward: 21.0000 episodes: 4088 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31368.2 seconds (8.71 hours) Timestep: 7270000 mean reward (100 episodes): 20.7300 best mean reward: 20.8500 current episode reward: 19.0000 episodes: 4093 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31412.4 seconds (8.73 hours) Timestep: 7280000 mean reward (100 episodes): 20.7600 best mean reward: 20.8500 current episode reward: 21.0000 episodes: 4100 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31456.7 seconds (8.74 hours) Timestep: 7290000 mean reward (100 episodes): 20.7600 best mean reward: 20.8500 current episode reward: 21.0000 episodes: 4106 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31501.3 seconds (8.75 hours) Timestep: 7300000 mean reward (100 episodes): 20.7600 best mean reward: 20.8500 current episode reward: 21.0000 episodes: 4112 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31546.4 seconds (8.76 hours) Timestep: 7310000 mean reward (100 episodes): 20.7400 best mean reward: 20.8500 current episode reward: 21.0000 episodes: 4118 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31591.2 seconds (8.78 hours) Timestep: 7320000 mean reward (100 episodes): 20.7500 best mean reward: 20.8500 current episode reward: 21.0000 episodes: 4124 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31635.9 seconds (8.79 hours) Timestep: 7330000 mean reward (100 episodes): 20.7500 best mean reward: 20.8500 current episode reward: 20.0000 episodes: 4130 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31680.0 seconds (8.80 hours) Timestep: 7340000 mean reward (100 episodes): 20.7700 best mean reward: 20.8500 current episode reward: 20.0000 episodes: 4136 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31724.3 seconds (8.81 hours) Timestep: 7350000 mean reward (100 episodes): 20.7900 best mean reward: 20.8500 current episode reward: 21.0000 episodes: 4142 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31769.7 seconds (8.82 hours) Timestep: 7360000 mean reward (100 episodes): 20.7900 best mean reward: 20.8500 current episode reward: 21.0000 episodes: 4148 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31814.1 seconds (8.84 hours) Timestep: 7370000 mean reward (100 episodes): 20.8000 best mean reward: 20.8500 current episode reward: 21.0000 episodes: 4154 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31858.0 seconds (8.85 hours) Timestep: 7380000 mean reward (100 episodes): 20.8000 best mean reward: 20.8500 current episode reward: 21.0000 episodes: 4160 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31902.0 seconds (8.86 hours) Timestep: 7390000 mean reward (100 episodes): 20.8100 best mean reward: 20.8500 current episode reward: 21.0000 episodes: 4166 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31946.6 seconds (8.87 hours) Timestep: 7400000 mean reward (100 episodes): 20.8200 best mean reward: 20.8500 current episode reward: 21.0000 episodes: 4172 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 31990.4 seconds (8.89 hours) Timestep: 7410000 mean reward (100 episodes): 20.8100 best mean reward: 20.8500 current episode reward: 21.0000 episodes: 4178 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32034.5 seconds (8.90 hours) Timestep: 7420000 mean reward (100 episodes): 20.8100 best mean reward: 20.8500 current episode reward: 21.0000 episodes: 4184 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32078.9 seconds (8.91 hours) Timestep: 7430000 mean reward (100 episodes): 20.7900 best mean reward: 20.8500 current episode reward: 21.0000 episodes: 4190 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32123.5 seconds (8.92 hours) Timestep: 7440000 mean reward (100 episodes): 20.8000 best mean reward: 20.8500 current episode reward: 21.0000 episodes: 4196 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32167.6 seconds (8.94 hours) Timestep: 7450000 mean reward (100 episodes): 20.7700 best mean reward: 20.8500 current episode reward: 21.0000 episodes: 4202 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32212.1 seconds (8.95 hours) Timestep: 7460000 mean reward (100 episodes): 20.7800 best mean reward: 20.8500 current episode reward: 21.0000 episodes: 4208 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32256.3 seconds (8.96 hours) Timestep: 7470000 mean reward (100 episodes): 20.7900 best mean reward: 20.8500 current episode reward: 21.0000 episodes: 4214 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32300.5 seconds (8.97 hours) Timestep: 7476547 mean reward (100 episodes): 20.8000 best mean reward: 20.8500 current episode reward: 21.0000 episodes: 4218 exploration: 0.01000 learning_rate: 0.00005 elapsed time: 32329.7 seconds (8.98 hours) ================================================ FILE: dqn/plot_dqn.py ================================================ import matplotlib.pyplot as plt import numpy as np import pickle import seaborn as sns plt.style.use('seaborn-darkgrid') np.set_printoptions(edgeitems=100, linewidth=100, suppress=True) # Some default settings. LOGDIR = 'logs_pkls/' FIGDIR = 'figures/' title_size = 22 axis_size = 20 tick_size = 18 legend_size = 20 lw = 3 def smoothed_block(x, n): """ Smoothed average in a block (size n) on array x. """ assert (len(x.shape) == 1) and (x.shape[0] > n) length = x.shape[0]-n+1 sm = np.zeros((length,)) for i in range(length): sm[i] = np.mean(x[i:i+n]) return sm ######## # Pong # ######## name = "Pong" fig, axarr = plt.subplots(2,2, figsize=(15,12)) pong_data = [] pong_eps = [] pong_t = [] pong_mean = [] pong_best_mean = [] pong_ep = [] pong_eps_sm = [] pong_colors = ['red','black'] pong_labels = ['seed=1','seed=2'] for i in range(0,2): index_str = str(i+1) with open(LOGDIR+'Pong_s00'+index_str+'.pkl', 'rb') as f: pong_data.append( np.array(pickle.load(f)) ) pong_eps.append( np.array(pickle.load(f)) ) pong_data[i] = np.maximum(pong_data[i], -21) pong_t.append((pong_data[i][:,0]) / 1000000.0) pong_mean.append(pong_data[i][:,1]) pong_best_mean.append(pong_data[i][:,2]) pong_ep.append(pong_data[i][:,3]) pong_eps_sm.append(smoothed_block(pong_eps[i], 100)) axarr[0,0].set_title(name+ " Scores at Timesteps", fontsize=title_size) axarr[0,1].set_title(name+ " Scores at Timesteps (Block 100)", fontsize=title_size) axarr[0,0].plot(pong_t[i], pong_ep[i], c=pong_colors[i], lw=lw, label=pong_labels[i]) axarr[0,1].plot(pong_t[i], pong_mean[i], c=pong_colors[i], lw=lw, label=pong_labels[i]) axarr[0,0].set_xlabel("Training Steps (in Millions)", fontsize=axis_size) axarr[0,1].set_xlabel("Training Steps (in Millions)", fontsize=axis_size) axarr[1,0].set_title(name+ " Scores per Episode", fontsize=title_size) axarr[1,1].set_title(name+ " Scores per Episode (Block 100)", fontsize=title_size) axarr[1,0].plot(pong_eps[i], c=pong_colors[i], lw=lw, label=pong_labels[i]) axarr[1,1].plot(pong_eps_sm[i], c=pong_colors[i], lw=lw, label=pong_labels[i]) axarr[1,0].set_xlabel("Number of Episodes", fontsize=axis_size) axarr[1,1].set_xlabel("Number of Episodes", fontsize=axis_size) for i in range(2): for j in range(2): axarr[i,j].set_ylabel("Rewards", fontsize=axis_size) axarr[i,j].tick_params(axis='x', labelsize=tick_size) axarr[i,j].tick_params(axis='y', labelsize=tick_size) axarr[i,j].legend(loc='lower right', prop={'size':legend_size}) axarr[i,j].set_ylim([-23,23]) plt.tight_layout() plt.savefig(FIGDIR+name+".png") ############ # Breakout # ############ name = "Breakout" fig, axarr = plt.subplots(2,2, figsize=(15,12)) breakout_data = [] breakout_eps = [] breakout_t = [] breakout_mean = [] breakout_best_mean = [] breakout_ep = [] breakout_eps_sm = [] breakout_colors = ['blue','yellow'] breakout_labels = ['seed=1','seed=2'] for i in range(0,2): index_str = str(i+1) with open(LOGDIR+'Breakout_s00'+index_str+'.pkl', 'rb') as f: breakout_data.append( np.array(pickle.load(f)) ) breakout_eps.append( np.array(pickle.load(f)) ) breakout_data[i] = np.maximum(breakout_data[i], -21) breakout_t.append((breakout_data[i][:,0]) / 1000000.0) breakout_mean.append(breakout_data[i][:,1]) breakout_best_mean.append(breakout_data[i][:,2]) breakout_ep.append(breakout_data[i][:,3]) breakout_eps_sm.append(smoothed_block(breakout_eps[i], 100)) axarr[0,0].set_title(name+ " Scores at Timesteps", fontsize=title_size) axarr[0,1].set_title(name+ " Scores at Timesteps (Block 100)", fontsize=title_size) axarr[0,0].plot(breakout_t[i], breakout_ep[i], c=breakout_colors[i], lw=lw, label=breakout_labels[i]) axarr[0,1].plot(breakout_t[i], breakout_mean[i], c=breakout_colors[i], lw=lw, label=breakout_labels[i]) axarr[0,0].set_xlabel("Training Steps (in Millions)", fontsize=axis_size) axarr[0,1].set_xlabel("Training Steps (in Millions)", fontsize=axis_size) axarr[1,0].set_title(name+ " Scores per Episode", fontsize=title_size) axarr[1,1].set_title(name+ " Scores per Episode (Block 100)", fontsize=title_size) axarr[1,0].plot(breakout_eps[i], c=breakout_colors[i], lw=lw, label=breakout_labels[i]) axarr[1,1].plot(breakout_eps_sm[i], c=breakout_colors[i], lw=lw, label=breakout_labels[i]) axarr[1,0].set_xlabel("Number of Episodes", fontsize=axis_size) axarr[1,1].set_xlabel("Number of Episodes", fontsize=axis_size) for i in range(2): for j in range(2): axarr[i,j].set_ylabel("Rewards", fontsize=axis_size) axarr[i,j].tick_params(axis='x', labelsize=tick_size) axarr[i,j].tick_params(axis='y', labelsize=tick_size) axarr[i,j].legend(loc='upper left', prop={'size':legend_size}) plt.tight_layout() plt.savefig(FIGDIR+name+".png") ############# # BeamRider # ############# name = "BeamRider" fig, axarr = plt.subplots(2,2, figsize=(15,12)) beamrider_data = [] beamrider_eps = [] beamrider_t = [] beamrider_mean = [] beamrider_best_mean = [] beamrider_ep = [] beamrider_eps_sm = [] beamrider_colors = ['orange','purple'] beamrider_labels = ['seed=1','seed=2'] for i in range(0,2): index_str = str(i+1) with open(LOGDIR+'BeamRider_s00'+index_str+'.pkl', 'rb') as f: beamrider_data.append( np.array(pickle.load(f)) ) beamrider_eps.append( np.array(pickle.load(f)) ) beamrider_data[i] = np.maximum(beamrider_data[i], -21) beamrider_t.append((beamrider_data[i][:,0]) / 1000000.0) beamrider_mean.append(beamrider_data[i][:,1]) beamrider_best_mean.append(beamrider_data[i][:,2]) beamrider_ep.append(beamrider_data[i][:,3]) beamrider_eps_sm.append(smoothed_block(beamrider_eps[i], 100)) axarr[0,0].set_title(name+ " Scores at Timesteps", fontsize=title_size) axarr[0,1].set_title(name+ " Scores at Timesteps (Block 100)", fontsize=title_size) axarr[0,0].plot(beamrider_t[i], beamrider_ep[i], c=beamrider_colors[i], lw=lw, label=beamrider_labels[i]) axarr[0,1].plot(beamrider_t[i], beamrider_mean[i], c=beamrider_colors[i], lw=lw, label=beamrider_labels[i]) axarr[0,0].set_xlabel("Training Steps (in Millions)", fontsize=axis_size) axarr[0,1].set_xlabel("Training Steps (in Millions)", fontsize=axis_size) axarr[1,0].set_title(name+ " Scores per Episode", fontsize=title_size) axarr[1,1].set_title(name+ " Scores per Episode (Block 100)", fontsize=title_size) axarr[1,0].plot(beamrider_eps[i], c=beamrider_colors[i], lw=lw, label=beamrider_labels[i]) axarr[1,1].plot(beamrider_eps_sm[i], c=beamrider_colors[i], lw=lw, label=beamrider_labels[i]) axarr[1,0].set_xlabel("Number of Episodes", fontsize=axis_size) axarr[1,1].set_xlabel("Number of Episodes", fontsize=axis_size) for i in range(2): for j in range(2): axarr[i,j].set_ylabel("Rewards", fontsize=axis_size) axarr[i,j].tick_params(axis='x', labelsize=tick_size) axarr[i,j].tick_params(axis='y', labelsize=tick_size) axarr[i,j].legend(loc='upper left', prop={'size':legend_size}) plt.tight_layout() plt.savefig(FIGDIR+name+".png") ########## # Enduro # ########## name = "Enduro" fig, axarr = plt.subplots(2,2, figsize=(15,12)) enduro_data = [] enduro_eps = [] enduro_t = [] enduro_mean = [] enduro_best_mean = [] enduro_ep = [] enduro_eps_sm = [] enduro_colors = ['gold','midnightblue'] enduro_labels = ['seed=1','seed=2'] for i in range(0,2): index_str = str(i+1) with open(LOGDIR+'Enduro_s00'+index_str+'.pkl', 'rb') as f: enduro_data.append( np.array(pickle.load(f)) ) enduro_eps.append( np.array(pickle.load(f)) ) enduro_data[i] = np.maximum(enduro_data[i], -21) enduro_t.append((enduro_data[i][:,0]) / 1000000.0) enduro_mean.append(enduro_data[i][:,1]) enduro_best_mean.append(enduro_data[i][:,2]) enduro_ep.append(enduro_data[i][:,3]) enduro_eps_sm.append(smoothed_block(enduro_eps[i], 100)) axarr[0,0].set_title(name+ " Scores at Timesteps", fontsize=title_size) axarr[0,1].set_title(name+ " Scores at Timesteps (Block 100)", fontsize=title_size) axarr[0,0].plot(enduro_t[i], enduro_ep[i], c=enduro_colors[i], lw=lw, label=enduro_labels[i]) axarr[0,1].plot(enduro_t[i], enduro_mean[i], c=enduro_colors[i], lw=lw, label=enduro_labels[i]) axarr[0,0].set_xlabel("Training Steps (in Millions)", fontsize=axis_size) axarr[0,1].set_xlabel("Training Steps (in Millions)", fontsize=axis_size) axarr[1,0].set_title(name+ " Scores per Episode", fontsize=title_size) axarr[1,1].set_title(name+ " Scores per Episode (Block 100)", fontsize=title_size) axarr[1,0].plot(enduro_eps[i], c=enduro_colors[i], lw=lw, label=enduro_labels[i]) axarr[1,1].plot(enduro_eps_sm[i], c=enduro_colors[i], lw=lw, label=enduro_labels[i]) axarr[1,0].set_xlabel("Number of Episodes", fontsize=axis_size) axarr[1,1].set_xlabel("Number of Episodes", fontsize=axis_size) for i in range(2): for j in range(2): axarr[i,j].set_ylabel("Rewards", fontsize=axis_size) axarr[i,j].tick_params(axis='x', labelsize=tick_size) axarr[i,j].tick_params(axis='y', labelsize=tick_size) axarr[i,j].legend(loc='upper left', prop={'size':legend_size}) plt.tight_layout() plt.savefig(FIGDIR+name+".png") ================================================ FILE: dqn/run_dqn_atari.py ================================================ import argparse import gym from gym import wrappers import os.path as osp import random import numpy as np import tensorflow as tf import tensorflow.contrib.layers as layers import argparse import sys import dqn from dqn_utils import * from atari_wrappers import * def atari_model(img_in, num_actions, scope, reuse=False): # as described in https://storage.googleapis.com/deepmind-data/assets/papers/DeepMindNature14236Paper.pdf with tf.variable_scope(scope, reuse=reuse): out = img_in with tf.variable_scope("convnet"): # original architecture out = layers.convolution2d(out, num_outputs=32, kernel_size=8, stride=4, activation_fn=tf.nn.relu) out = layers.convolution2d(out, num_outputs=64, kernel_size=4, stride=2, activation_fn=tf.nn.relu) out = layers.convolution2d(out, num_outputs=64, kernel_size=3, stride=1, activation_fn=tf.nn.relu) out = layers.flatten(out) with tf.variable_scope("action_value"): out = layers.fully_connected(out, num_outputs=512, activation_fn=tf.nn.relu) out = layers.fully_connected(out, num_outputs=num_actions, activation_fn=None) return out def atari_learn(env, session, num_timesteps, log_file = './logs_pkls/rewards.pkl'): # This is just a rough estimate num_iterations = float(num_timesteps) / 4.0 lr_multiplier = 1.0 lr_schedule = PiecewiseSchedule([ (0, 1e-4 * lr_multiplier), (num_iterations / 10, 1e-4 * lr_multiplier), (num_iterations / 2, 5e-5 * lr_multiplier), ], outside_value=5e-5 * lr_multiplier) optimizer = dqn.OptimizerSpec( constructor=tf.train.AdamOptimizer, kwargs=dict(epsilon=1e-4), lr_schedule=lr_schedule ) def stopping_criterion(env, t): # notice that here t is the number of steps of the wrapped env, # which is different from the number of steps in the underlying env return get_wrapper_by_name(env, "Monitor").get_total_steps() >= num_timesteps exploration_schedule = PiecewiseSchedule( [ (0, 1.0), (1e6, 0.1), (num_iterations / 2, 0.01), ], outside_value=0.01 ) dqn.learn( env, q_func=atari_model, optimizer_spec=optimizer, session=session, exploration=exploration_schedule, stopping_criterion=stopping_criterion, replay_buffer_size=1000000, batch_size=32, gamma=0.99, learning_starts=50000, learning_freq=4, frame_history_len=4, target_update_freq=10000, grad_norm_clipping=10, log_file=log_file ) env.close() def get_available_gpus(): from tensorflow.python.client import device_lib local_device_protos = device_lib.list_local_devices() return [x.physical_device_desc for x in local_device_protos if x.device_type == 'GPU'] def set_global_seeds(i): try: import tensorflow as tf except ImportError: pass else: tf.set_random_seed(i) np.random.seed(i) random.seed(i) def get_session(): tf.reset_default_graph() tf_config = tf.ConfigProto( inter_op_parallelism_threads=1, intra_op_parallelism_threads=1) # This was the default provided in the starter code. #session = tf.Session(config=tf_config) # Use this if I want to see what is on the GPU. #session = tf.Session(config=tf.ConfigProto(log_device_placement=True)) # Use this for limiting memory allocated for the GPU. gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5) session = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) print("AVAILABLE GPUS: ", get_available_gpus()) return session def get_env(task, seed): env_id = task.env_id env = gym.make(env_id) set_global_seeds(seed) env.seed(seed) expt_dir = '/tmp/hw3_vid_dir2/' env = wrappers.Monitor(env, osp.join(expt_dir, "gym"), force=True) env = wrap_deepmind(env) return env def main(): # Games that we'll be testing. game_to_ID = {'BeamRider':0, 'Breakout':1, 'Enduro':2, 'Pong':3, 'Qbert':4} # Get some arguments here. Note: num_timesteps default uses tasks default. parser = argparse.ArgumentParser() parser.add_argument('--game', type=str, default='Pong') parser.add_argument('--seed', type=int, default=0) parser.add_argument('--num_timesteps', type=int, default=40000000) args = parser.parse_args() # Choose the game to play and set log file. benchmark = gym.benchmark_spec('Atari40M') task = benchmark.tasks[game_to_ID[args.game]] log_name = args.game+"_s"+str(args.seed).zfill(3)+".pkl" # Run training. Should change the seed if possible! # Also, the actual # of iterations run is _roughly_ num_timesteps/4. seed = args.seed env = get_env(task, seed) session = get_session() print("task = {}".format(task)) atari_learn(env, session, num_timesteps=args.num_timesteps, log_file=log_name) if __name__ == "__main__": main() ================================================ FILE: dqn/run_dqn_ram.py ================================================ import argparse import gym from gym import wrappers import os.path as osp import random import numpy as np import tensorflow as tf import tensorflow.contrib.layers as layers import dqn from dqn_utils import * from atari_wrappers import * def atari_model(ram_in, num_actions, scope, reuse=False): with tf.variable_scope(scope, reuse=reuse): out = ram_in #out = tf.concat(1,(ram_in[:,4:5],ram_in[:,8:9],ram_in[:,11:13],ram_in[:,21:22],ram_in[:,50:51], ram_in[:,60:61],ram_in[:,64:65])) with tf.variable_scope("action_value"): out = layers.fully_connected(out, num_outputs=256, activation_fn=tf.nn.relu) out = layers.fully_connected(out, num_outputs=128, activation_fn=tf.nn.relu) out = layers.fully_connected(out, num_outputs=64, activation_fn=tf.nn.relu) out = layers.fully_connected(out, num_outputs=num_actions, activation_fn=None) return out def atari_learn(env, session, num_timesteps): # This is just a rough estimate num_iterations = float(num_timesteps) / 4.0 lr_multiplier = 1.0 lr_schedule = PiecewiseSchedule([ (0, 1e-4 * lr_multiplier), (num_iterations / 10, 1e-4 * lr_multiplier), (num_iterations / 2, 5e-5 * lr_multiplier), ], outside_value=5e-5 * lr_multiplier) optimizer = dqn.OptimizerSpec( constructor=tf.train.AdamOptimizer, kwargs=dict(epsilon=1e-4), lr_schedule=lr_schedule ) def stopping_criterion(env, t): # notice that here t is the number of steps of the wrapped env, # which is different from the number of steps in the underlying env return get_wrapper_by_name(env, "Monitor").get_total_steps() >= num_timesteps exploration_schedule = PiecewiseSchedule( [ (0, 0.2), (1e6, 0.1), (num_iterations / 2, 0.01), ], outside_value=0.01 ) dqn.learn( env, q_func=atari_model, optimizer_spec=optimizer, session=session, exploration=exploration_schedule, stopping_criterion=stopping_criterion, replay_buffer_size=1000000, batch_size=32, gamma=0.99, learning_starts=50000, learning_freq=4, frame_history_len=1, target_update_freq=10000, grad_norm_clipping=10 ) env.close() def get_available_gpus(): from tensorflow.python.client import device_lib local_device_protos = device_lib.list_local_devices() return [x.physical_device_desc for x in local_device_protos if x.device_type == 'GPU'] def set_global_seeds(i): try: import tensorflow as tf except ImportError: pass else: tf.set_random_seed(i) np.random.seed(i) random.seed(i) def get_session(): tf.reset_default_graph() tf_config = tf.ConfigProto( inter_op_parallelism_threads=1, intra_op_parallelism_threads=1) session = tf.Session(config=tf_config) print("AVAILABLE GPUS: ", get_available_gpus()) return session def get_env(seed): env = gym.make('Pong-ram-v0') set_global_seeds(seed) env.seed(seed) expt_dir = '/tmp/hw3_vid_dir/' env = wrappers.Monitor(env, osp.join(expt_dir, "gym"), force=True) env = wrap_deepmind_ram(env) return env def main(): # Run training seed = 0 # Use a seed of zero (you may want to randomize the seed!) env = get_env(seed) session = get_session() atari_learn(env, session, num_timesteps=int(4e7)) if __name__ == "__main__": main() ================================================ FILE: es/README.md ================================================ # Evolution Strategies Inspired by [recent work from OpenAI][1]. # Code Usage See the bash scripts. The ES code I am using includes the following tricks: - Mirrored sampling - Ranking transformation I do not use the trick of instantiating a large block of Gaussian noise for each worker, because this code is designed to run sequentially. Note: as of 05/18/2017, `npop` INCLUDES the mirroring so it must be divisible by two. It uses TensorFlow but maybe that's not even needed for our purposes? Because there are no gradients to update a network. (We have to do gradient ascent, but that's done explicitly here and I don't think autodiff is necessary.) Tensorflow and the GPU are mostly useful for the *forward* pass in RL, which is not even the most critical step. # Results ## Inverted Pendulum I originally ran this for 800 iterations, but it seems like 700 is also a safe upper bound on the number of iterations. Args (this one is with `npop` **not** counting the mirroring): ``` Namespace(do_not_save=False, envname='InvertedPendulum-v1', es_iters=800, log_every_t_iter=2, lrate_es=0.005, npop=100, render=False, seed=4, sigma=0.1, snapshot_every_t_iter=50, test_trajs=10, verbose=True) ``` and (with revised `npop` and also with 700 iterations): ``` Namespace(do_not_save=False, envname='InvertedPendulum-v1', es_iters=700, log_every_t_iter=2, lrate_es=0.005, npop=200, render=False, seed=5, sigma=0.1, snapshot_every_t_iter=50, test_trajs=10, verbose=True) ``` The results look good! The results reach the perfect score *faster* than with the Gaussian sampling of actions afterwards. ![InvertedPendulum01](figures/InvertedPendulum-v1_log.png?raw=true) ![InvertedPendulum02](figures/InvertedPendulum-v1_rewards_std.png?raw=true) ## Inverted Pendulum (+Gaussian Sampling) This uses the Gaussian sampling, which we should *not* be doing. Note: I ran this twice with normalized features (seeds 0 and 1), twice with ranking transformation (seeds 2 and 3). Run with (for one seed): ``` rm -r outputs/InvertedPendulum-v1/seed0000 clear python main.py InvertedPendulum-v1 \ --es_iters 1000 \ --log_every_t_iter 1 \ --npop 200 \ --seed 0 \ --sigma 0.1 \ --test_trajs 10 \ --verbose ``` And then do the same thing, but with seed 0001 instead. I then did seeds 2 and 3, which has the actual reward rank transformation. Seed 3 uses the following args (note: this time, `npop` gets doubled ... so they are equivalent ... sorry for the confusion): ``` Namespace(do_not_save=False, envname='InvertedPendulum-v1', es_iters=1000, log_every_t_iter=1, lrate_es=0.005, npop=100, render=False, seed=3, sigma=0.1, snapshot_every_t_iter=50, test_trajs=10, verbose=True) ``` Yowza! It works! Here's the figure log, followed by the rewards and standard deviations. They all reach the maximum score of 1000. I stopped seed 3 after 800 iterations, so **if I need to use expert trajectories, use seed 2** because that one ran for the full 1000 iterations. The seed 2 trial took about 8 and 1/3 hours. The plots named "Final"-something contain statistics related to the 10 rollouts the agent made each iteration, *after* it made the evolution strategies weight update. The plots named "Scores"-something contain statistics related to the 200 rollouts the agent made each iteration, where the 200 rollouts are each with some weight perturbation. (This is the `npop` parameter I have.) ![InvertedPendulum01](figures/InvertedPendulum-v1-old_log.png?raw=true) ![InvertedPendulum02](figures/InvertedPendulum-v1-old_rewards_std.png?raw=true) ## Half Cheetah-v1 (+Gaussian Sampling) Run with: ``` rm -r outputs/HalfCheetah-v1/seed0000 clear python main.py HalfCheetah-v1 \ --es_iters 1000 \ --log_every_t_iter 1 \ --npop 200 \ --seed 0 \ --sigma 0.1 \ --test_trajs 10 \ --verbose ``` Actually, I had to terminate this one after about 690 iterations because it was taking too long. This took maybe 16 hours to generate! But at least it is learning *something*. TRPO on HalfCheetah-v1 gets roughly 2400 so this has a long way to go. ![HalfCheetah01](figures/HalfCheetah-v1_log.png?raw=true) ![HalfCheetah02](figures/HalfCheetah-v1_rewards_std.png?raw=true) [1]:https://blog.openai.com/evolution-strategies/ ================================================ FILE: es/bash_scripts/InvertedPendulum-v1.sh ================================================ #!/bin/bash clear python main.py InvertedPendulum-v1 \ --es_iters 700 \ --lrate_es 0.005 \ --log_every_t_iter 2 \ --npop 200 \ --seed 5 \ --sigma 0.1 \ --snapshot_every_t_iter 50 \ --test_trajs 10 \ --verbose ================================================ FILE: es/es.py ================================================ """ This is Natural Evolution Strategies, designed to run on one computer and not a cluster. (c) May 2017 by Daniel Seita, though obviously based on OpenAI's work/idea. """ import gym import logz import numpy as np import os import pickle import sys import tensorflow as tf import tensorflow.contrib.layers as layers import time import utils from collections import defaultdict from gym import wrappers np.set_printoptions(edgeitems=100, linewidth=100, suppress=True, precision=5) class ESAgent: def __init__(self, session, args, log_dir=None, continuous=True): """ An Evolution Strategies agent. It uses the same network architecture from OpenAI's paper, and I think OpenAI didn't sample the actions from a Gaussian afterwards. The agent has functionality for obtaining and updating weights in vector form to make ES addition easier. Args: session: A Tensorflow session. args: The argparse from the user. log_dir: The log directory for the logging, if any. continuous: Whether the agent acts in a continuous or discrete action space. (Right now only continuous is supported.) """ assert continuous == True, "Error: only continuous==True is supported." tf.set_random_seed(args.seed) self.sess = session self.args = args self.log_dir = log_dir self.env = gym.make(args.envname) ob_dim = self.env.observation_space.shape[0] ac_dim = self.env.action_space.shape[0] self.ob_no = tf.placeholder(shape=[None, ob_dim], dtype=tf.float32) # Build the final network layer, which is our action (no sampling!). self.sampled_ac = self._make_network(data_in=self.ob_no, out_dim=ac_dim)[0] # To *extract* weight values, run a session on `self.weights_v`. self.weights = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='ESAgent') self.weights_v = tf.concat([tf.reshape(w, [-1]) for w in self.weights], axis=0) self.shapes = [w.get_shape().as_list() for w in self.weights] self.num_ws = np.sum([np.prod(sh) for sh in self.shapes]) # To *update* weights, run `self.set_params_op` w/feed `self.new_weights_v`. self.new_weights_v = tf.placeholder(tf.float32, shape=[self.num_ws]) updates = [] start = 0 for (i,w) in enumerate(self.weights): shape = self.shapes[i] size = np.prod(shape) updates.append( tf.assign(w, tf.reshape(self.new_weights_v[start:start+size], shape)) ) start += size self.set_params_op = tf.group(*updates) if args.verbose: self._print_summary() self.sess.run(tf.global_variables_initializer()) def _make_network(self, data_in, out_dim): """ Build the network with the same architecture following OpenAI's paper. Returns the final *layer* of the network, which corresponds to our chosen action. There is no non-linearity for the last layer because different envs have different action ranges. """ with tf.variable_scope("ESAgent", reuse=False): out = data_in out = layers.fully_connected(out, num_outputs=64, weights_initializer = layers.xavier_initializer(uniform=True), #weights_initializer = utils.normc_initializer(0.5), activation_fn = tf.nn.tanh) out = layers.fully_connected(out, num_outputs=64, weights_initializer = layers.xavier_initializer(uniform=True), #weights_initializer = utils.normc_initializer(0.5), activation_fn = tf.nn.tanh) out = layers.fully_connected(out, num_outputs=out_dim, weights_initializer = layers.xavier_initializer(uniform=True), #weights_initializer = utils.normc_initializer(0.5), activation_fn = None) return out def _compute_return(self, test=False, store_info=False): """ Runs the current neural network policy. For now, we assume we run **one** episode. Also, we expand the observations to get a dummy dimension, in case we figure out how to make use of minibatches later. Args: test True if testing, False if part of training. The testing could be either the tests done after each weight update, or the tests done as a result fo the `test` method. store_info: True if storing info is desired, meaning that we return observations and actions. Returns: The scalar return to be evaluated by the ES agent. """ max_steps = self.env.spec.timestep_limit obs = self.env.reset() done = False steps = 0 total_rew = 0 observations = [] actions = [] while not done: exp_obs = np.expand_dims(obs, axis=0) action = self.sess.run(self.sampled_ac, {self.ob_no:exp_obs}) observations.append(obs) actions.append(action) # Apply the action *after* storing the current obs/action pair. obs, r, done, _ = self.env.step(action) total_rew += r steps += 1 if self.args.render and test: self.env.render() if steps >= max_steps or done: break if store_info: return total_rew, observations, actions else: return total_rew def _print_summary(self): """ Just for debugging assistance. """ print("\nES Agent NN weight shapes:\n{}".format(self.shapes)) print("\nES Agent NN weights:") for w in self.weights: print(w) print("\nNumber of weights: {}".format(self.num_ws)) print("\naction space: {}".format(self.env.action_space)) print("lower bound: {}".format(self.env.action_space.low)) print("upper bound: {}".format(self.env.action_space.high)) print("self.sampled_ac: {}\n".format(self.sampled_ac)) def run_es(self): """ Runs Evolution Strategies. Tricks used: - Antithetic (i.e. mirrored) sampling. - Rank transformation, using OpenAI's code. Tricks avoided: - Fixed Gaussian block. I like to just regenerate here. - Virtual batch normalization, seems to be only for Atari games. - Weight decay. Not sure how to do this. - Action discretization. For now, it adds extra complexity. Final weights are saved and can be pre-loaded elsewhere. """ args = self.args t_start = time.time() for i in range(args.es_iters): if (i % args.log_every_t_iter == 0): print("\n************ Iteration %i ************"%i) stats = defaultdict(list) # Set stuff up for perturbing weights and determining fitness. weights_old = self.sess.run(self.weights_v) # Shape (numw,) eps_nw = np.random.randn(args.npop/2, self.num_ws) scores_n2 = [] for j in range(args.npop/2): # Mirrored sampling, positive case, +eps_j. weights_new_pos = weights_old + args.sigma * eps_nw[j] self.sess.run(self.set_params_op, feed_dict={self.new_weights_v: weights_new_pos}) rews_pos = self._compute_return() # Mirrored sampling, negative case, -eps_j. weights_new_neg = weights_old - args.sigma * eps_nw[j] self.sess.run(self.set_params_op, feed_dict={self.new_weights_v: weights_new_neg}) rews_neg = self._compute_return() scores_n2.append([rews_pos,rews_neg]) # Determine the new weights based on OpenAI's rank updating. proc_returns_n2 = utils.compute_centered_ranks(np.array(scores_n2)) F_n = proc_returns_n2[:,0] - proc_returns_n2[:,1] grad = np.dot(eps_nw.T, F_n) # Apply the gradient update. TODO: Change this to ADAM. alpha = (args.lrate_es / (args.sigma*args.npop)) next_weights = weights_old + alpha * grad self.sess.run(self.set_params_op, feed_dict={self.new_weights_v: next_weights}) # Report relevant logs. if (i % args.log_every_t_iter == 0): hours = (time.time()-t_start) / (60*60.) # Test roll-outs with these new weights. returns = [] for _ in range(args.test_trajs): returns.append(self._compute_return(test=True)) logz.log_tabular("FinalAvgReturns", np.mean(returns)) logz.log_tabular("FinalStdReturns", np.std(returns)) logz.log_tabular("FinalMaxReturns", np.max(returns)) logz.log_tabular("FinalMinReturns", np.min(returns)) logz.log_tabular("ScoresAvg", np.mean(scores_n2)) logz.log_tabular("ScoresStd", np.std(scores_n2)) logz.log_tabular("ScoresMax", np.max(scores_n2)) logz.log_tabular("ScoresMin", np.min(scores_n2)) logz.log_tabular("TotalTimeHours", hours) logz.log_tabular("TotalIterations", i) logz.dump_tabular() # Save the weights so I can test them later. if (i % args.snapshot_every_t_iter == 0): itr = str(i).zfill(len(str(abs(args.es_iters)))) with open(self.log_dir+'/snapshots/weights_'+itr+'.pkl', 'wb') as f: pickle.dump(next_weights, f) # Save the *final* weights. itr = str(i).zfill(len(str(abs(args.es_iters)))) with open(self.log_dir+'/snapshots/weights_'+itr+'.pkl', 'wb') as f: pickle.dump(next_weights, f) def test(self, just_one=True): """ This is for test-time evaluation. No training is done here. By default, iterate through every snapshot. If `just_one` is true, this only runs one set of weights, to ensure that we record right away since OpenAI will only record subsets and less frequently. Changing the loop over snapshots is also needed. """ os.makedirs(self.args.directory+'/videos') self.env = wrappers.Monitor(self.env, self.args.directory+'/videos', force=True) headdir = self.args.directory+'/snapshots/' snapshots = os.listdir(headdir) snapshots.sort() num_rollouts = 10 if just_one: num_rollouts = 1 for sn in snapshots: print("\n***** Currently on snapshot {} *****".format(sn)) ### Add your own criteria here. # if "800" not in sn: # continue ### with open(headdir+sn, 'rb') as f: weights = pickle.load(f) self.sess.run(self.set_params_op, feed_dict={self.new_weights_v: weights}) returns = [] for i in range(num_rollouts): returns.append( self._compute_return(test=True) ) print("mean: \t{}".format(np.mean(returns))) print("std: \t{}".format(np.std(returns))) print("max: \t{}".format(np.max(returns))) print("min: \t{}".format(np.min(returns))) print("returns:\n{}".format(returns)) def generate_rollout_data(self, weights, num_rollouts): """ Roll out the expert data and save the observations and actions for imitation learning later. The observations and actions are stored in two separate lists of lists. For instance, with InvertedPendulum and 100 rollouts, the shapes will be be (100,1000,4) and (100,1000,1), with the 1000 representing 1000 time steps. The actual expert roll-outs may not last the same time length. Use the `ENV_TO_OBS_SHAPE` to guard against this scenario. We **zero-pad** if needed (maybe randomizing is better? but MuJoCo is continuous and actions are centered at zero...). TL;DR: leading dimension is the minibatch, second leading dimension is the timestep, third is the obs/act shape. If the obs/acts have two dimensions, let's linearize to avoid worrying about it. By the way, to experiment later with the *transits* only, just use the same data here except shuffle the code. This happens elsewhere. Args: weights: The desired weight vector. num_rollouts: The number of expert rollouts to save. """ # These are the shapes we need **for each trajectory**. ENV_TO_OBS_SHAPE = {"InvertedPendulum-v1": (1000,4)} ENV_TO_ACT_SHAPE = {"InvertedPendulum-v1": (1000,1)} if self.args.envname not in ENV_TO_OBS_SHAPE: print("Error, this environment is not supported.") sys.exit() headdir = self.args.directory+ '/expert_data' if not os.path.exists(headdir): os.makedirs(headdir) self.sess.run(self.set_params_op, feed_dict={self.new_weights_v: weights}) returns = [] observations = [] actions = [] for i in range(num_rollouts): if i % 10 == 0: print("rollout {}".format(i)) rew, obs_l, acts_l = self._compute_return(test=False, store_info=True) returns.append(rew) observations.append(obs_l) actions.append(acts_l) print("returns", returns) print("mean return", np.mean(returns)) print("std of return", np.std(returns)) # Fix padding issue to make lists have the same shape; we later make an # array. Check each (ol,al), tuple of lists, to ensure shapes match. If # the obs-list doesn't match, neither will the act-list, so test one. for (i,(ol,al)) in enumerate(zip(observations,actions)): obs_l = np.array(ol) act_l = np.array(al) print("{} {} {}".format(i, obs_l.shape, act_l.shape)) if obs_l.shape != ENV_TO_OBS_SHAPE[self.args.envname]: result_o = np.zeros(ENV_TO_OBS_SHAPE[self.args.envname]) result_a = np.zeros(ENV_TO_ACT_SHAPE[self.args.envname]) result_o[:obs_l.shape[0],:obs_l.shape[1]] = obs_l result_a[:act_l.shape[0],:act_l.shape[1]] = act_l print("revised shapes: {} {}".format(result_o.shape, result_a.shape)) obs_l = result_o act_l = result_a observations[i] = obs_l actions[i] = act_l expert_data = {'observations': np.array(observations), 'actions': np.array(actions)} # Save the data print("obs-shape = {}".format(expert_data['observations'].shape)) print("act-shape = {}".format(expert_data['actions'].shape)) str_roll = str(num_rollouts).zfill(4) name = headdir+ "/" +self.args.envname+ "_" +str_roll+ "rollouts_trajs" np.save(name, expert_data) print("Expert data has been saved in: {}.npy".format(name)) ================================================ FILE: es/logz.py ================================================ """ Some simple logging functionality, inspired by rllab's logging. Assumes that each diagnostic gets logged each iteration Call logz.configure_output_dir() to start logging to a tab-separated-values file (some_folder_name/log.txt) To load the learning curves, you can do, for example A = np.genfromtxt('/tmp/expt_1468984536/log.txt',delimiter='\t',dtype=None, names=True) A['EpRewMean'] """ import os.path as osp, shutil, time, atexit, os, subprocess color2num = dict( gray=30, red=31, green=32, yellow=33, blue=34, magenta=35, cyan=36, white=37, crimson=38 ) def colorize(string, color, bold=False, highlight=False): attr = [] num = color2num[color] if highlight: num += 10 attr.append(str(num)) if bold: attr.append('1') return '\x1b[%sm%s\x1b[0m' % (';'.join(attr), string) class G: output_dir = None output_file = None first_row = True log_headers = [] log_current_row = {} def configure_output_dir(d=None): """ Set output directory to d, or to /tmp/somerandomnumber if d is None """ G.output_dir = d or "/tmp/experiments/%i"%int(time.time()) assert not osp.exists(G.output_dir), "Log dir %s already exists! Delete it first or use a different dir"%G.output_dir os.makedirs(G.output_dir) G.output_file = open(osp.join(G.output_dir, "log.txt"), 'w') atexit.register(G.output_file.close) try: cmd = "cd %s && git diff > %s 2>/dev/null"%(osp.dirname(__file__), osp.join(G.output_dir, "a.diff")) subprocess.check_call(cmd, shell=True) # Save git diff to experiment directory except subprocess.CalledProcessError: print("configure_output_dir: not storing the git diff, probably because you're not in a git repo") print(colorize("Logging data to %s"%G.output_file.name, 'green', bold=True)) def log_tabular(key, val): """ Log a value of some diagnostic Call this once for each diagnostic quantity, each iteration """ if G.first_row: G.log_headers.append(key) else: assert key in G.log_headers, "Trying to introduce a new key %s that you didn't include in the first iteration"%key assert key not in G.log_current_row, "You already set %s this iteration. Maybe you forgot to call dump_tabular()"%key G.log_current_row[key] = val def dump_tabular(): """ Write all of the diagnostics from the current iteration """ vals = [] print("-"*47) for key in G.log_headers: val = G.log_current_row.get(key, "") if hasattr(val, "__float__"): valstr = "%8.4g"%val else: valstr = val print("| %20s | %20s |"%(key, valstr)) vals.append(val) print("-"*47) if G.output_file is not None: if G.first_row: G.output_file.write("\t".join(G.log_headers)) G.output_file.write("\n") G.output_file.write("\t".join(map(str,vals))) G.output_file.write("\n") G.output_file.flush() G.log_current_row.clear() G.first_row=False ================================================ FILE: es/main.py ================================================ """ Use this script for setting the arguments. (c) May 2017 by Daniel Seita """ import argparse import logz import os import pickle import tensorflow as tf import utils from es import ESAgent if __name__ == "__main__": """ LOTS of arguments here, but hopefully most are straightforward. Run `python main.py -h` to visualize the help messages. """ parser = argparse.ArgumentParser() parser.add_argument('envname', type=str, help='The OpenAI gym environment name (case sensitive).') parser.add_argument('--do_not_save', action='store_true', help='Sets the log_dir to be None.') parser.add_argument('--es_iters', type=int, default=100, help='Iterations to run ES.') parser.add_argument('--log_every_t_iter', type=int, default=1, help='Controls the amount of time information is logged.') parser.add_argument('--lrate_es', type=float, default=0.001, help='Learning rate for the ES gradient update.') parser.add_argument('--npop', type=int, default=200, help='Weight vectors to sample for ES (INCLUDING the mirroring') parser.add_argument('--render', action='store_true', help='Use `--render` to visualize trajectories each iteration.') parser.add_argument('--seed', type=int, default=0, help='The random seed.') parser.add_argument('--sigma', type=float, default=0.1, help='Sigma (standard deviation) for the Gaussian noise.') parser.add_argument('--snapshot_every_t_iter', type=int, default=100, help='Save the model every t iterations so we can inspect later.') parser.add_argument('--test_trajs', type=int, default=10, help='Number of evaluation trajectories after each iteration.') parser.add_argument('--verbose', action='store_true', help='Use `--verbose` for a few additional debugging messages.') args = parser.parse_args() assert args.npop % 2 == 0 # Just to be consistent with my other code. # Make the TensorFlow session and do some logic with handling arguments. session = utils.get_tf_session() log_dir = None if not args.do_not_save: log_dir = 'outputs/' +args.envname+ '/seed' +str(args.seed).zfill(4) logz.configure_output_dir(log_dir) os.makedirs(log_dir+'/snapshots/') with open(log_dir+'/args.pkl','wb') as f: pickle.dump(args, f) # Build and run evolution strategies. es_agent = ESAgent(session, args, log_dir) es_agent.run_es() ================================================ FILE: es/optimizers.py ================================================ """ This code was written by Jonathan Ho. See: https://github.com/openai/evolution-strategies-starter/blob/master/es_distributed/optimizers.py """ import numpy as np class Optimizer(object): def __init__(self, pi): self.pi = pi self.dim = pi.num_params self.t = 0 def update(self, globalg): self.t += 1 step = self._compute_step(globalg) theta = self.pi.get_trainable_flat() ratio = np.linalg.norm(step) / np.linalg.norm(theta) self.pi.set_trainable_flat(theta + step) return ratio def _compute_step(self, globalg): raise NotImplementedError class SGD(Optimizer): def __init__(self, pi, stepsize, momentum=0.9): Optimizer.__init__(self, pi) self.v = np.zeros(self.dim, dtype=np.float32) self.stepsize, self.momentum = stepsize, momentum def _compute_step(self, globalg): self.v = self.momentum * self.v + (1. - self.momentum) * globalg step = -self.stepsize * self.v return step class Adam(Optimizer): def __init__(self, pi, stepsize, beta1=0.9, beta2=0.999, epsilon=1e-08): Optimizer.__init__(self, pi) self.stepsize = stepsize self.beta1 = beta1 self.beta2 = beta2 self.epsilon = epsilon self.m = np.zeros(self.dim, dtype=np.float32) self.v = np.zeros(self.dim, dtype=np.float32) def _compute_step(self, globalg): a = self.stepsize * np.sqrt(1 - self.beta2 ** self.t) / (1 - self.beta1 ** self.t) self.m = self.beta1 * self.m + (1 - self.beta1) * globalg self.v = self.beta2 * self.v + (1 - self.beta2) * (globalg * globalg) step = -a * self.m / (np.sqrt(self.v) + self.epsilon) return step ================================================ FILE: es/plot.py ================================================ """ To plot this, you need to provide the experiment directory plus an output stem. I use this for InvertedPendulum: python plot.py outputs/InvertedPendulum-v1 --envname InvertedPendulum-v1 \ --out figures/InvertedPendulum-v1 (c) May 2017 by Daniel Seita """ import argparse import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import numpy as np import os import pickle import seaborn as sns import sys from os.path import join from pylab import subplots plt.style.use('seaborn-darkgrid') sns.set_context(rc={'lines.markeredgewidth': 1.0}) np.set_printoptions(edgeitems=100,linewidth=100,suppress=True) # Some matplotlib settings. LOGDIR = 'outputs/' FIGDIR = 'figures/' title_size = 22 tick_size = 18 legend_size = 17 ysize = 18 xsize = 18 lw = 1 ms = 8 error_region_alpha = 0.3 # Attributes to include in a plot. ATTRIBUTES = ["FinalAvgReturns", "FinalStdReturns", "FinalMaxReturns", "FinalMinReturns", "ScoresAvg", "ScoresStd", "ScoresMax", "ScoresMin"] # Axes labels for environments. ENV_TO_YLABELS = {"HalfCheetah-v1": [-800,1000], "InvertedPendulum-v1": [0,1000]} # Colors. In general we won't use all of these. COLORS = ['blue', 'red', 'gold', 'black'] def plot_one_dir(args, directory): """ The actual plotting code. Assumes that we'll be plotting from one directory, which usually means considering one random seed only, however it's better to have multiple random seeds so this code generalizes. For ES, we should store the output at *every* timestep, so A['TotalIterations'] should be like np.arange(...), but this generalizes in case Ray can help me run for many more iterations. """ print("Now plotting based on directory {} ...".format(directory)) ### Figure 1: The log.txt file. num = len(ATTRIBUTES) fig, axes = subplots(num, figsize=(12,3*num)) for (dd, cc) in zip(directory, COLORS): A = np.genfromtxt(join(args.expdir, dd, 'log.txt'), delimiter='\t', dtype=None, names=True) x = A['TotalIterations'] for (i,attr) in enumerate(ATTRIBUTES): axes[i].plot(x, A[attr], '-', lw=lw, color=cc, label=dd) axes[i].set_ylabel(attr, fontsize=ysize) axes[i].tick_params(axis='x', labelsize=tick_size) axes[i].tick_params(axis='y', labelsize=tick_size) axes[i].legend(loc='best', ncol=1, prop={'size':legend_size}) plt.tight_layout() plt.savefig(args.out+'_log.png') ### Figure 2: Error regions. num = len(directory) if num == 1: num+= 1 fig, axes = subplots(1,num, figsize=(12*num,10)) for (i, (dd, cc)) in enumerate(zip(directory, COLORS)): A = np.genfromtxt(join(args.expdir, dd, 'log.txt'), delimiter='\t', dtype=None, names=True) axes[i].plot(A['TotalIterations'], A["FinalAvgReturns"], color=cc, marker='x', ms=ms, lw=lw) axes[i].fill_between(A['TotalIterations'], A["FinalAvgReturns"] - A["FinalStdReturns"], A["FinalAvgReturns"] + A["FinalStdReturns"], alpha = error_region_alpha, facecolor='y') axes[i].set_ylim(ENV_TO_YLABELS[args.envname]) axes[i].tick_params(axis='x', labelsize=tick_size) axes[i].tick_params(axis='y', labelsize=tick_size) axes[i].set_title("Mean Episode Rewards ({})".format(dd), fontsize=title_size) axes[i].set_xlabel("ES Iterations", fontsize=xsize) axes[i].set_ylabel("Rewards", fontsize=ysize) plt.tight_layout() plt.savefig(args.out+'_rewards_std.png') if __name__ == "__main__": """ Handle logic with argument parsing. """ parser = argparse.ArgumentParser() parser.add_argument("expdir", help="experiment dir, e.g., /tmp/experiments") parser.add_argument("--out", type=str, help="full directory where to save") parser.add_argument("--envname", type=str) args = parser.parse_args() plot_one_dir(args, directory=os.listdir(args.expdir)) ================================================ FILE: es/test.py ================================================ """ This will load in snapshots of weights generated from Evolution Strategies and evaluate the agent by generating roll-outs. Use this to produce videos and so forth. No weight updates happen here, as this is like the agent evaluation stage. Provide the directory (envname and seed) and the arguments and snapshots will be automatically loaded. Also provide the rendering option if desired. This will OVERRIDE the previous render option from the training stage. Fortunately, the `Namespace` class means adding and updating is easy. Usage example: python test.py outputs/InvertedPendulum-v1/seed0004 --numr 2000 Add --render if desired. Videos are recorded and stored in a special folder in the directory. (c) May 2017 by Daniel Seita """ import argparse import pickle import utils from es import ESAgent if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument('directory', type=str, help='Must include envname and random seed!') parser.add_argument('--numr', type=int, default=1000, help='The number of expert rollouts to save.') parser.add_argument('--render', action='store_true', help='Use `--render` to visualize trajectories each iteration.') args = parser.parse_args() # Extract the old arguments and update the rendering. with open(args.directory+'/args.pkl', 'rb') as f: old_args = pickle.load(f) old_args.render = args.render old_args.directory = args.directory # Run a test to see performance and/or save expert rollout data. session = utils.get_tf_session() es_agent = ESAgent(session, old_args, log_dir=None) # Option 1: just run a test (videos) #es_agent.test(just_one=False) # Option 2: save expert roll-outs, dimensions = (#trajs, #times, state/act) ### PUT WEIGHT PICKLE FILE HERE ### pklweights = args.directory+'/snapshots/weights_700.pkl' with open(pklweights, 'rb') as f: weights = pickle.load(f) es_agent.generate_rollout_data(weights=weights, num_rollouts=args.numr) ================================================ FILE: es/toy_es.py ================================================ """ Basic evolution strategies, based on Andrej Karpathy's starter code. We have the actual solutions here only for didactic purposes, and to get the number of weights correct. Actually, how similar is this to the cross entropy method that I've worked with before? Both re-scale and then center at the new update. I remember that the CEM may have only used a strict cutoff, but that's not really a huge difference (we could smooth it out). Tested on both solution sets here. """ import argparse import sys import matplotlib.pyplot as plt plt.style.use('seaborn-darkgrid') import numpy as np np.set_printoptions(suppress=True, precision=5) # Adjust the solutions here as needed. SOLUTIONS = [ np.array([0.5, 0.1, -0.3]), np.array([0.5, 0.1, -0.3, 1.0, 1.0, 0.4]) ] def f(w, sol): """ Define whatever function we want to optimize. """ return -np.sum(np.square(sol-w)) def run_es(args): """ Runs basic evolution strategies using the provided arguments. It uses the solution here only for the size and determining performance. In a real problem, we wouldn't have access to it. Also, we standardize the rewards. I think just because it makes sense to standardize a lot of things and in our case we're just concerned about the relative benefit for each weight, so rescaling is OK (e.g. for numerical purposes). We adjust the amount of noise with sigma. This is the standard deviation and we multiply it with the standard Gaussian, which is the usual way to do it. The last line performs something that _looks_ like a gradient update. What really happens is that each weight is updated using a linear combination of all the weights, plus some jittered noise. We divide by npop because otherwise we'd get super-large changes. """ sol = SOLUTIONS[args.sol_index] w = np.random.randn(sol.size) for i in range(args.num_iters): if (i % args.print_every == 0): print("iter {}. w: {}, solution: {}, reward: {:.5f}".format( str(i).zfill(4), str(w), sol, f(w,sol))) N = np.random.randn(args.npop, sol.size) R = np.zeros(args.npop) for j in range(args.npop): R[j] = f(w+args.sigma*N[j], sol) A = (R - np.mean(R)) / (np.std(R)+0.000001) w = w + args.lrate/(args.npop*args.sigma) * np.dot(N.T, A) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument('--npop', type=int, default=50) parser.add_argument('--sigma', type=float, default=0.1) parser.add_argument('--lrate', type=float, default=0.001) parser.add_argument('--sol_index', type=int, default=0) parser.add_argument('--num_iters', type=int, default=500) parser.add_argument('--print_every', type=int, default=20) args = parser.parse_args() run_es(args) ================================================ FILE: es/utils.py ================================================ """ Random supporting methods. (c) May 2017 by Daniel Seita """ import numpy as np import sys import tensorflow as tf def compute_ranks(x): """ Returns ranks in [0, len(x)) Note: This is different from scipy.stats.rankdata, which returns ranks in [1, len(x)]. Note: this is from OpenAI's code. """ assert x.ndim == 1 ranks = np.empty(len(x), dtype=int) ranks[x.argsort()] = np.arange(len(x)) return ranks def compute_centered_ranks(x): """ This is OpenAI's rank transformation code. They call this with x.shape = (n,2). The first column indicates the return for the +eps_i case, the second for the -eps_i case (mirrored sampling). Each time a roll-out happens, they append [rews_pos, rews_neg] to a list, which they then vertically concatenate to get to (n,2), so n must indicate the npop parameter (or maybe half of it). This will make the maximum score have a rank of 0.5, the smallest score have a rank of -0.5, and all other values get ranks uniformly distributed in (-0.5, 0.5), with ties broken based on the order from np.argsort(). The OpenAI code, for each generated +eps_i noise vector, the weight for that vector is actually (F_i-F_i') where F_i' is the result from negating that vector. So when they do the update, they don't "use" the -eps_i vector. It's just the +eps_i vector with a *weight* that takes into account the negative case. Actually that seems right to me, if the weight is negative then we would have wanted -eps_i and that would be encouraged. See their `batched_weighted_sum` method, which takes as its first argument a vector of length (n,) where n is presumably npop. Each component in that vector represents an F_i-F_i' term. They then do a (1,n)*(n,numparams) matrix multiply to get a final (1,numparam) weight update. Finally, they *further* divide that vector by (n*2) before feeding it to the update. That should represent the (1/npop) which I've been doing. """ y = compute_ranks(x.ravel()).reshape(x.shape).astype(np.float32) y /= (x.size - 1) y -= .5 return y def get_tf_session(): """ Returning a session. Set options here (e.g. for GPUs) if desired. """ tf.reset_default_graph() tf_config = tf.ConfigProto(inter_op_parallelism_threads=1, intra_op_parallelism_threads=1) gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5) session = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) def get_available_gpus(): from tensorflow.python.client import device_lib local_device_protos = device_lib.list_local_devices() return [x.physical_device_desc for x in local_device_protos if x.device_type == 'GPU'] print("AVAILABLE GPUS: ", get_available_gpus()) return session def normc_initializer(std=1.0): """ Initialize array with normalized columns """ def _initializer(shape, dtype=None, partition_info=None): #pylint: disable=W0613 out = np.random.randn(*shape).astype(np.float32) out *= std / np.sqrt(np.square(out).sum(axis=0, keepdims=True)) return tf.constant(out) return _initializer ================================================ FILE: g_learning/G-Learning.py ================================================ """ (c) 2017 by Daniel Seita G-Learning, as described in: Taming the Noise in Reinforcement Learning via Soft Updates (UAI 2016) Authors: Roy Fox*, Ari Pakman*, Naftali Tishby This code will face the same constraints as Denny Britz's code, i.e. we need simple tabular scenarios. I have tested G-learning on the following: 1. CliffWorldEnv. It runs, but the results seem to be worse than Q-learning. """ from __future__ import print_function import gym import itertools import matplotlib import numpy as np import sys if "../" not in sys.path: sys.path.append('../') from collections import defaultdict from lib.envs.cliff_walking import CliffWalkingEnv from lib.envs.gridworld import GridworldEnv from lib import plotting matplotlib.style.use('ggplot') class GLearningAgent(): def __init__(self, env, k): """ For now, we'll make a limitation that we know the number of states and actions. It creates: - G: Contains G(state,action) values. - N: Contains N(state,action) values, counts of the times each state-action pair was visited; needed for the alpha update. - rho: The \rho(a|s) function in the paper, the prior policy (see Section 3.1 in the paper). ** Assumes uniform prior!! ** Args: env: An OpenAI gym environment, either custom or built-in, but be aware that not all of them can be used in this setting. k: The parameter which adjusts the beta term. Should be tested. """ ns, na = env.observation_space.n, env.action_space.n self.G = np.zeros((ns,na)) self.N = np.zeros((ns,na)) self.rho = np.ones((ns,na), dtype=float) / na self.k = k def policy_exploration(self, state, epsilon=0.0): """ The agent's current exploration policy. Right now we default to epsilon-greedy on the G(s,a) values. Args: state: The current state the agent is in. epsilon: The probability of taking a random action. Returns: The action to take. """ na = self.G.shape[1] action_probs = np.ones(na, dtype=float) * epsilon / na best_action = np.argmax(self.G[state,:]) action_probs[best_action] += (1.0-epsilon) return np.random.choice(np.arange(na), p=action_probs) def alpha_schedule(self, t, state, action): """ The alpha scheduling. By default, Equation 29 in the paper. Args: t: The iteration of the current episode (t >= 1). state: The current state. action: The current action. Returns: The alpha to use for the G-learning update. """ alpha = self.N[state,action] ** -0.8 assert 0 < alpha <= 1, "Error, alpha = {}".format(alpha) return alpha def beta_schedule(self, t): """ The beta scheduling. By default, Equation 26 in the paper. Args: t: The iteration of the current episode (t >= 1). Returns: The beta to use for the G-learning update. If it's 0, G-learning turns into Q^\rho-learning. If it approaches infinity, G-learning approaches Q-learning. """ beta = self.k * t assert beta >= 0, "Error, beta={}, k={}, t={}".format(beta, self.k, t) return beta def g_learning(self, num_episodes, max_ep_steps=10000, discount=1.0, epsilon=0.1): """ The G-learning algorithm. Args: num_episodes: Number of episodes to run. max_ep_steps: Maximum time steps allocated to one episode. discount: Standard discount factor, usually denoted as \gamma. epsilon: Probability of taking random actions during exploration. Returns: A tuple (G, stats) of the G-values and statistics, which should be plotted and thoroughly analyzed. """ cum_t = 0 stats = plotting.EpisodeStats(episode_lengths=np.zeros(num_episodes), episode_rewards=np.zeros(num_episodes)) for i_episode in range(num_episodes): if (i_episode+1) % 1 == 0: print("\rEpisode {}/{}.".format(i_episode+1, num_episodes), end="") sys.stdout.flush() state = env.reset() # Run this episode until we finish as indicated by the environment. for t in range(1, max_ep_steps+1): # Uses exploration policy to take a step. action = self.policy_exploration(state, epsilon) next_state, reward, done, _ = env.step(action) cost = -reward # Collect statistics (cum_t currently not used). stats.episode_rewards[i_episode] += cost stats.episode_lengths[i_episode] = t self.N[state,action] += 1 cum_t += 1 # Intermediate terms for the G-learning update. alpha = self.alpha_schedule(t, state, action) beta = self.beta_schedule(t) temp = np.sum(self.rho[next_state,:] * np.exp(-beta * self.G[next_state,:])) # Official G-learning update at last. Equation 18 in the paper. td_target = cost - (discount/beta) * np.log(temp) td_delta = td_target - self.G[state,action] self.G[state,action] += (alpha * td_delta) if done: break state = next_state print("") return self.G, stats if __name__ == "__main__": """ This will run G-learning. Be sure to double check all parameters, including the ones for plotting (e.g., file names). """ # Should test with k in {1e-3, 1e-4, 5*1e-5, 1e-6} env = CliffWalkingEnv() agent = GLearningAgent(env, k=1e-3) G, stats = agent.g_learning(num_episodes=1000, max_ep_steps=500, discount=0.95, epsilon=0.1) plotting.plot_episode_stats(stats, smoothing_window=5, noshow=False, figdir="figures/cliff_", dosave=True) ================================================ FILE: g_learning/README.md ================================================ # Standard Tabular G-learning (not Q-Learning!) This is mainly to benchmark against tabular Q-learning, [which I've implemented here](https://github.com/DanielTakeshi/rl_algorithms/tree/master/q_learning). **Note**: The paper reference for G-learning [1] uses *costs*, not rewards, so whenever I say reward here, just negate it and view it as the cost. Thus, the reward of -100 for Q-learning is really a cost of 100 for G-learning. ## Cliff World ### Environment I tested with `CliffWorldEnv` using the following settings: - number of episodes = 1000 - discount factor = 0.95 - alpha = n(s,a)^(-0.8) for the temporal difference update, where n(s,a) is the number of times that (s,a) have been visited. It's Equation 29 in reference [1]. - epsilon = 0.1 for greedy-epsilon exploration. Once the agent executes an action, it's deterministic. - k = 1e-3, a special scheduling parameter for the \beta term in G-learning, which not used in Q-learning. I also tuned with 1e4, 5\*1e-5, and 1e-6 as reported in [1] and got similar results. For rewards, the agent gets a -1 living reward, gets 0 if it manages to get to the bottom right corner, but gets -100 if it falls off the cliff. The agent starts at the bottom left corner and can move in one of four directions deterministically (but the **exploration policy** is epsilon-greedy based on the G(s,a) values). **Note**: In [1], the reported cost is 5 for going off the cliff, but I made it 100 because with the discount factor we have here, I don't see how the agent can learn to go to the goal. It takes a cost of 5 to immediately jump off the cliff, but it would take about a cost of 10 to go to the goal ... so the agent would prefer to jump off the cliff in the first move? When I ran Q-learning with this, Q-learning indeed learned to jump off the cliff right away. ### Results First, episode **COSTS** over the trials, smoothed over a window of five trials. I know it says "rewards" in the figure but it's really "costs". We want the curve to decrease. ![Rewards of episodes](figures/cliff_episode_reward_time.png?raw=true) Unfortunately, the agent doesn't seem to be learning anything except to jump off the cliff right away and incur a cost of 100. This is what I see when rendering the environment on the command line. We never see costs below 100, which would happen if the agent took a path (either the riksy or the safe one) to go to the correct goal state. Next, we have the length of episodes (i.e. number of time steps). The minium we can get is 1 since the agent could actually dive into the pit on the first action. If the agent is following the "risky" path during training and manages to make it to the end (despite the randomness in the epsilon-greedy policy) then that's 13 steps. ![Length of episodes](figures/cliff_episode_length_time.png?raw=true) Unfortunately this isn't what I want to see. I actually had to cap the number of steps in an episode (which I didn't need to do for G-learning). I'm not sure what's going on. ### References [1] Taming the Noise in Reinforcement Learning via Soft Updates, UAI 2016 ================================================ FILE: g_learning/__init__.py ================================================ ================================================ FILE: lib/__init__.py ================================================ ================================================ FILE: lib/envs/README.md ================================================ Different (custom) environments that we can load in and test. From Denny Britz: - `blackjack.py` - `cliff_walking.py` - `gridworld.py` - `windy_gridworld.py` From myself: - `two_room_domain.py` ================================================ FILE: lib/envs/__init__.py ================================================ ================================================ FILE: lib/envs/blackjack.py ================================================ import gym from gym import spaces from gym.utils import seeding def cmp(a, b): return int((a > b)) - int((a < b)) # 1 = Ace, 2-10 = Number cards, Jack/Queen/King = 10 deck = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10, 10] def draw_card(np_random): return np_random.choice(deck) def draw_hand(np_random): return [draw_card(np_random), draw_card(np_random)] def usable_ace(hand): # Does this hand have a usable ace? return 1 in hand and sum(hand) + 10 <= 21 def sum_hand(hand): # Return current hand total if usable_ace(hand): return sum(hand) + 10 return sum(hand) def is_bust(hand): # Is this hand a bust? return sum_hand(hand) > 21 def score(hand): # What is the score of this hand (0 if bust) return 0 if is_bust(hand) else sum_hand(hand) def is_natural(hand): # Is this hand a natural blackjack? return sorted(hand) == [1, 10] class BlackjackEnv(gym.Env): """Simple blackjack environment Blackjack is a card game where the goal is to obtain cards that sum to as near as possible to 21 without going over. They're playing against a fixed dealer. Face cards (Jack, Queen, King) have point value 10. Aces can either count as 11 or 1, and it's called 'usable' at 11. This game is placed with an infinite deck (or with replacement). The game starts with each (player and dealer) having one face up and one face down card. The player can request additional cards (hit=1) until they decide to stop (stick=0) or exceed 21 (bust). After the player sticks, the dealer reveals their facedown card, and draws until their sum is 17 or greater. If the dealer goes bust the player wins. If neither player nor dealer busts, the outcome (win, lose, draw) is decided by whose sum is closer to 21. The reward for winning is +1, drawing is 0, and losing is -1. The observation of a 3-tuple of: the players current sum, the dealer's one showing card (1-10 where 1 is ace), and whether or not the player holds a usable ace (0 or 1). This environment corresponds to the version of the blackjack problem described in Example 5.1 in Reinforcement Learning: An Introduction by Sutton and Barto (1998). https://webdocs.cs.ualberta.ca/~sutton/book/the-book.html """ def __init__(self, natural=False): self.action_space = spaces.Discrete(2) self.observation_space = spaces.Tuple(( spaces.Discrete(32), spaces.Discrete(11), spaces.Discrete(2))) self._seed() # Flag to payout 1.5 on a "natural" blackjack win, like casino rules # Ref: http://www.bicyclecards.com/how-to-play/blackjack/ self.natural = natural # Start the first game self._reset() # Number of self.nA = 2 def _seed(self, seed=None): self.np_random, seed = seeding.np_random(seed) return [seed] def _step(self, action): assert self.action_space.contains(action) if action: # hit: add a card to players hand and return self.player.append(draw_card(self.np_random)) if is_bust(self.player): done = True reward = -1 else: done = False reward = 0 else: # stick: play out the dealers hand, and score done = True while sum_hand(self.dealer) < 17: self.dealer.append(draw_card(self.np_random)) reward = cmp(score(self.player), score(self.dealer)) if self.natural and is_natural(self.player) and reward == 1: reward = 1.5 return self._get_obs(), reward, done, {} def _get_obs(self): return (sum_hand(self.player), self.dealer[0], usable_ace(self.player)) def _reset(self): self.dealer = draw_hand(self.np_random) self.player = draw_hand(self.np_random) # Auto-draw another card if the score is less than 12 while sum_hand(self.player) < 12: self.player.append(draw_card(self.np_random)) return self._get_obs() ================================================ FILE: lib/envs/cliff_walking.py ================================================ """ This is adapted from Denny Britz's repository. I will modify as needed to replicate existing literature results. """ import numpy as np import sys from gym.envs.toy_text import discrete UP = 0 RIGHT = 1 DOWN = 2 LEFT = 3 class CliffWalkingEnv(discrete.DiscreteEnv): metadata = {'render.modes': ['human', 'ansi']} def _limit_coordinates(self, coord): coord[0] = min(coord[0], self.shape[0] - 1) coord[0] = max(coord[0], 0) coord[1] = min(coord[1], self.shape[1] - 1) coord[1] = max(coord[1], 0) return coord def _calculate_transition_prob(self, current, delta): new_position = np.array(current) + np.array(delta) new_position = self._limit_coordinates(new_position).astype(int) new_state = np.ravel_multi_index(tuple(new_position), self.shape) # Newer version of rewards/costs from G-learning paper # reward = -100.0 if self._cliff[tuple(new_position)] else -1.0 reward = -1.0 if self._cliff[tuple(new_position)]: reward = -100.0 elif tuple(new_position) == (3,11): reward = 0.0 is_done = self._cliff[tuple(new_position)] or (tuple(new_position) == (3,11)) return [(1.0, new_state, reward, is_done)] def __init__(self): self.shape = (4, 12) nS = np.prod(self.shape) nA = 4 # Cliff Location self._cliff = np.zeros(self.shape, dtype=np.bool) self._cliff[3, 1:-1] = True # Calculate transition probabilities P = {} for s in range(nS): position = np.unravel_index(s, self.shape) P[s] = { a : [] for a in range(nA) } P[s][UP] = self._calculate_transition_prob(position, [-1, 0]) P[s][RIGHT] = self._calculate_transition_prob(position, [0, 1]) P[s][DOWN] = self._calculate_transition_prob(position, [1, 0]) P[s][LEFT] = self._calculate_transition_prob(position, [0, -1]) # We always start in state (3, 0) isd = np.zeros(nS) isd[np.ravel_multi_index((3,0), self.shape)] = 1.0 super(CliffWalkingEnv, self).__init__(nS, nA, P, isd) def _render(self, mode='human', close=False): if close: return outfile = StringIO() if mode == 'ansi' else sys.stdout for s in range(self.nS): position = np.unravel_index(s, self.shape) # print(self.s) if self.s == s: output = " x " elif position == (3,11): output = " T " elif self._cliff[position]: output = " C " else: output = " o " if position[1] == 0: output = output.lstrip() if position[1] == self.shape[1] - 1: output = output.rstrip() output += "\n" outfile.write(output) outfile.write("\n") ================================================ FILE: lib/envs/gridworld.py ================================================ import numpy as np import sys from gym.envs.toy_text import discrete UP = 0 RIGHT = 1 DOWN = 2 LEFT = 3 class GridworldEnv(discrete.DiscreteEnv): """ Grid World environment from Sutton's Reinforcement Learning book chapter 4. You are an agent on an MxN grid and your goal is to reach the terminal state at the top left or the bottom right corner. For example, a 4x4 grid looks as follows: T o o o o x o o o o o o o o o T x is your position and T are the two terminal states. You can take actions in each direction (UP=0, RIGHT=1, DOWN=2, LEFT=3). Actions going off the edge leave you in your current state. You receive a reward of -1 at each step until you reach a terminal state. """ metadata = {'render.modes': ['human', 'ansi']} def __init__(self, shape=[4,4]): if not isinstance(shape, (list, tuple)) or not len(shape) == 2: raise ValueError('shape argument must be a list/tuple of length 2') self.shape = shape nS = np.prod(shape) nA = 4 MAX_Y = shape[0] MAX_X = shape[1] P = {} grid = np.arange(nS).reshape(shape) it = np.nditer(grid, flags=['multi_index']) while not it.finished: s = it.iterindex y, x = it.multi_index P[s] = {a : [] for a in range(nA)} is_done = lambda s: s == 0 or s == (nS - 1) reward = 0.0 if is_done(s) else -1.0 # We're stuck in a terminal state if is_done(s): P[s][UP] = [(1.0, s, reward, True)] P[s][RIGHT] = [(1.0, s, reward, True)] P[s][DOWN] = [(1.0, s, reward, True)] P[s][LEFT] = [(1.0, s, reward, True)] # Not a terminal state else: ns_up = s if y == 0 else s - MAX_X ns_right = s if x == (MAX_X - 1) else s + 1 ns_down = s if y == (MAX_Y - 1) else s + MAX_X ns_left = s if x == 0 else s - 1 P[s][UP] = [(1.0, ns_up, reward, is_done(ns_up))] P[s][RIGHT] = [(1.0, ns_right, reward, is_done(ns_right))] P[s][DOWN] = [(1.0, ns_down, reward, is_done(ns_down))] P[s][LEFT] = [(1.0, ns_left, reward, is_done(ns_left))] it.iternext() # Initial state distribution is uniform isd = np.ones(nS) / nS # We expose the model of the environment for educational purposes # This should not be used in any model-free learning algorithm self.P = P super(GridworldEnv, self).__init__(nS, nA, P, isd) def _render(self, mode='human', close=False): if close: return outfile = StringIO() if mode == 'ansi' else sys.stdout grid = np.arange(self.nS).reshape(self.shape) it = np.nditer(grid, flags=['multi_index']) while not it.finished: s = it.iterindex y, x = it.multi_index if self.s == s: output = " x " elif s == 0 or s == self.nS - 1: output = " T " else: output = " o " if x == 0: output = output.lstrip() if x == self.shape[1] - 1: output = output.rstrip() outfile.write(output) if x == self.shape[1] - 1: outfile.write("\n") it.iternext() ================================================ FILE: lib/envs/two_room_domain.py ================================================ """ (c) December 2016 by Daniel Seita This implements the two room domain, as described in the experiment of: Principled Option Learning in Markov Decision Processes Roy Fox*, Michal Moshkovitz*, Naftali Tishby, EWRL 2016 I'm trying to get it similar to the OpenAI gym interface, with a 'step' function that returns similar stuff. Also, the number of non-terminal actions is hard-coded at 9 and follows numpad conventions: 6 7 8 3 4 5 0 1 2 So 5 is the NO-OP action, for instance. The special termination action is indicated by -1. The grid is represented with a grid such as: _ _ _ X X X _ _ _ _ _ _ X X X _ S _ _ _ _ X X X _ _ _ _ _ _ X X X _ _ A _ _ _ _ _ _ _ _ _ _ _ _ X X X _ _ _ G _ _ X X X _ _ _ _ _ _ X X X _ _ _ _ _ _ X X X _ _ _ X = wall _ = open spot G = goal S = start A = agent Though actually, there probably isn't any reason for me to use the starting state if the agent will be there. UPDATE: Actually wait, we do want it if we need to reset the scenario. Status: WIP """ import numpy as np import sys np.set_printoptions(suppress=True) A_TERM = -1 WALL = "X" OPEN = "_" AGENT = "A" GOAL = "G" class TwoRooms: def __init__(self, length=9): """ Initializes the state. See elsewhere for details. """ assert length >= 3, "assert={} is too low".format(length) self.length = length self.num_acts = 9 self.grid = np.zeros((self.length,self.length), dtype=str) self.s_start, self.s_agent, self.s_goal = self._init_grid() def _init_grid(self): """ Initializes the grid. Currently works best for multiples of 3 which are also odd. For now let's only test on 9x9 grids. """ self.grid.fill(OPEN) w1 = np.maximum((self.length/3), 1) w2 = np.minimum(2*(self.length/3), self.length) self.grid[:, w1:w2].fill(WALL) self.grid[self.length/2, :].fill(OPEN) sx = np.random.randint(0, self.length) sy = np.random.randint(0, w1) gx = np.random.randint(0, self.length) gy = np.random.randint(w2, self.length) s_agent = (sx,sy) s_goal = (gx,gy) assert s_agent != s_goal assert self.grid[s_agent] != WALL assert self.grid[s_goal] != WALL self.grid[s_agent] = AGENT self.grid[s_goal] = GOAL s_start = s_agent return s_start, s_agent, s_goal def _check_coords_and_move(self, coord): """ Checks if the coordinates are valid. If true, move the agent there. Otherwise, we don't move. """ if (coord[0] < 0 or coord[0] >= self.length or \ coord[1] < 0 or coord[1] >= self.length or \ self.grid[coord] == WALL): pass else: self.grid[self.s_agent] = OPEN self.s_agent = coord self.grid[self.s_agent] = AGENT def step(self, action): """ Take one step through the environment, and return the same stuff that OpenAI gym returns, with the exception of costs instead of rewards. This is meant to be called by external agents. Args: action: The action to be taken by the agent. """ if action == 0: self._check_coords_and_move((self.s_agent[0]+1, self.s_agent[1]-1)) elif action == 1: self._check_coords_and_move((self.s_agent[0]+1, self.s_agent[1])) elif action == 2: self._check_coords_and_move((self.s_agent[0]+1, self.s_agent[1]+1)) elif action == 3: self._check_coords_and_move((self.s_agent[0], self.s_agent[1]-1)) elif action == 4: pass elif action == 5: self._check_coords_and_move((self.s_agent[0], self.s_agent[1]+1)) elif action == 6: self._check_coords_and_move((self.s_agent[0]-1, self.s_agent[1]-1)) elif action == 7: self._check_coords_and_move((self.s_agent[0]-1, self.s_agent[1])) elif action == 8: self._check_coords_and_move((self.s_agent[0]-1, self.s_agent[1]+1)) cost = 1 done = (action == A_TERM or self.s_agent == self.s_goal) return (self.grid, cost, done, None) def reset(self): """ Resets the environment, like OpenAI gym. """ self.s_start, self.s_agent, self.s_goal = self._init_grid() def render(self): """ Like in OpenAI gym, except I'll probably use it to write the image to a file or something, instead of playing a video. """ pass def action_space_sample(self): """ This is meant to be the equivalent of OpenAI gym's action_space.sample, except it's in one method for simplicity. """ return np.random.randint(low=0, high=self.num_acts) def _pretty_print(self): print(self.grid) def test_nine_rooms(): # Test the basic 9 room set-up. env = TwoRooms(9) print("Initial environment") env._pretty_print() for i in range(100): a = env.action_space_sample() print("\nTaking {}-th action a={}. Here's the environment:".format(i,a)) (_, _, done, _) = env.step(a) env._pretty_print() if done: break env.reset() print("\nAfter resetting the environment:") env._pretty_print() if __name__ == "__main__": """ Some testing code. """ test_nine_rooms() ================================================ FILE: lib/envs/windy_gridworld.py ================================================ import gym import numpy as np import sys from gym.envs.toy_text import discrete UP = 0 RIGHT = 1 DOWN = 2 LEFT = 3 class WindyGridworldEnv(discrete.DiscreteEnv): metadata = {'render.modes': ['human', 'ansi']} def _limit_coordinates(self, coord): coord[0] = min(coord[0], self.shape[0] - 1) coord[0] = max(coord[0], 0) coord[1] = min(coord[1], self.shape[1] - 1) coord[1] = max(coord[1], 0) return coord def _calculate_transition_prob(self, current, delta, winds): new_position = np.array(current) + np.array(delta) + np.array([-1, 0]) * winds[tuple(current)] new_position = self._limit_coordinates(new_position).astype(int) new_state = np.ravel_multi_index(tuple(new_position), self.shape) is_done = tuple(new_position) == (3, 7) return [(1.0, new_state, -1.0, is_done)] def __init__(self): self.shape = (7, 10) nS = np.prod(self.shape) nA = 4 # Wind strength winds = np.zeros(self.shape) winds[:,[3,4,5,8]] = 1 winds[:,[6,7]] = 2 # Calculate transition probabilities P = {} for s in range(nS): position = np.unravel_index(s, self.shape) P[s] = { a : [] for a in range(nA) } P[s][UP] = self._calculate_transition_prob(position, [-1, 0], winds) P[s][RIGHT] = self._calculate_transition_prob(position, [0, 1], winds) P[s][DOWN] = self._calculate_transition_prob(position, [1, 0], winds) P[s][LEFT] = self._calculate_transition_prob(position, [0, -1], winds) # We always start in state (3, 0) isd = np.zeros(nS) isd[np.ravel_multi_index((3,0), self.shape)] = 1.0 super(WindyGridworldEnv, self).__init__(nS, nA, P, isd) def _render(self, mode='human', close=False): if close: return outfile = StringIO() if mode == 'ansi' else sys.stdout for s in range(self.nS): position = np.unravel_index(s, self.shape) # print(self.s) if self.s == s: output = " x " elif position == (3,7): output = " T " else: output = " o " if position[1] == 0: output = output.lstrip() if position[1] == self.shape[1] - 1: output = output.rstrip() output += "\n" outfile.write(output) outfile.write("\n") ================================================ FILE: lib/plotting.py ================================================ import matplotlib import numpy as np import pandas as pd from collections import namedtuple from matplotlib import pyplot as plt from mpl_toolkits.mplot3d import Axes3D EpisodeStats = namedtuple("Stats",["episode_lengths", "episode_rewards"]) def plot_cost_to_go_mountain_car(env, estimator, num_tiles=20): x = np.linspace(env.observation_space.low[0], env.observation_space.high[0], num=num_tiles) y = np.linspace(env.observation_space.low[1], env.observation_space.high[1], num=num_tiles) X, Y = np.meshgrid(x, y) Z = np.apply_along_axis(lambda _: -np.max(estimator.predict(_)), 2, np.dstack([X, Y])) fig = plt.figure(figsize=(10, 5)) ax = fig.add_subplot(111, projection='3d') surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=matplotlib.cm.coolwarm, vmin=-1.0, vmax=1.0) ax.set_xlabel('Position') ax.set_ylabel('Velocity') ax.set_zlabel('Value') ax.set_title("Mountain \"Cost To Go\" Function") fig.colorbar(surf) plt.show() def plot_value_function(V, title="Value Function"): """ Plots the value function as a surface plot. """ min_x = min(k[0] for k in V.keys()) max_x = max(k[0] for k in V.keys()) min_y = min(k[1] for k in V.keys()) max_y = max(k[1] for k in V.keys()) x_range = np.arange(min_x, max_x + 1) y_range = np.arange(min_y, max_y + 1) X, Y = np.meshgrid(x_range, y_range) # Find value for all (x, y) coordinates Z_noace = np.apply_along_axis(lambda _: V[(_[0], _[1], False)], 2, np.dstack([X, Y])) Z_ace = np.apply_along_axis(lambda _: V[(_[0], _[1], True)], 2, np.dstack([X, Y])) def plot_surface(X, Y, Z, title): fig = plt.figure(figsize=(20, 10)) ax = fig.add_subplot(111, projection='3d') surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=matplotlib.cm.coolwarm, vmin=-1.0, vmax=1.0) ax.set_xlabel('Player Sum') ax.set_ylabel('Dealer Showing') ax.set_zlabel('Value') ax.set_title(title) ax.view_init(ax.elev, -120) fig.colorbar(surf) plt.show() plot_surface(X, Y, Z_noace, "{} (No Usable Ace)".format(title)) plot_surface(X, Y, Z_ace, "{} (Usable Ace)".format(title)) def plot_episode_stats(stats, smoothing_window=10, noshow=False, dosave=True, figdir=""): """ Modified to now save figures. Use the figdir to act as the directory stem. """ # Plot the episode length over time fig1 = plt.figure(figsize=(10,5)) plt.plot(stats.episode_lengths) plt.xlabel("Epsiode") plt.ylabel("Epsiode Length") plt.title("Episode Length over Time") if dosave: plt.savefig(figdir + "episode_length_time.png", dpi=fig1.dpi*2) if noshow: plt.close(fig1) else: plt.show(fig1) # Plot the episode reward over time fig2 = plt.figure(figsize=(10,5)) rewards_smoothed = pd.Series(stats.episode_rewards).rolling(smoothing_window, min_periods=smoothing_window).mean() plt.plot(rewards_smoothed) plt.xlabel("Epsiode") plt.ylabel("Epsiode Reward (Smoothed)") plt.title("Episode Reward over Time (Smoothed over window size {})".format(smoothing_window)) if dosave: plt.savefig(figdir + "episode_reward_time.png", dpi=fig2.dpi*2) if noshow: plt.close(fig2) else: plt.show(fig2) # Plot time steps and episode number fig3 = plt.figure(figsize=(10,5)) plt.plot(np.cumsum(stats.episode_lengths), np.arange(len(stats.episode_lengths))) plt.xlabel("Time Steps") plt.ylabel("Episode") plt.title("Episode per time step") if dosave: plt.savefig(figdir + "episodes_per_time.png", dpi=fig3.dpi*2) if noshow: plt.close(fig3) else: plt.show(fig3) return fig1, fig2, fig3 ================================================ FILE: q_learning/Q-Learning.py ================================================ """ Code for basic tabular Q-learning. This is adapted from Denny Britz's repository. I updated it to use a class to more closely match the G-learning code. Observations for the **cliff_walking** scenario: - I was confused why the agent always seemed to go up at the start. But then I found out it's because np.argmax(Q[observation]) will return the leading index, and at the start, it's 0 all the way, so it always picks the first one which is to go up (see cliff_walking.py). It's a little annoying but not a big deal, it works out in the end. - The policy_fn will return one 0.925 and three 0.025s, since it will pick the best action and *then* adjust for epsilons. If I want to see how the Q-values are evolving, I need to use Q[observation] directly, not A. - To print, use env.render(). Very useful for the grid-like settings. - There's no extra reward for the goal state. It's just -1 for all R(s,a,s') unless the successor (new_position in the code) is the cliff, in which case it's -100. Other observations: - This code is general so it doesn't have to be cliff-walking, **but** you have to be careful to use an environment that gives a single number as a state, not a continuous state (e.g., unfortunately CartPole wouldn't work here). I can use FrozenLake-v0 but even a 4x4 space requires 10k or so iterations to see improvement. - At the end, it uses the plotting script, but I should probably roll out a modified verison for my own use. """ from __future__ import print_function import gym import itertools import matplotlib import numpy as np import sys if "../" not in sys.path: sys.path.append("../") from collections import defaultdict from lib.envs.cliff_walking import CliffWalkingEnv from lib.envs.gridworld import GridworldEnv from lib import plotting matplotlib.style.use('ggplot') class QLearningAgent(): def __init__(self, env): """ For now, we'll make a limitation that we know the number of states and actions. It creates: - Q: Contains Q(state,action) values. - N: Contains N(state,action) values, counts of the times each state-action pair was visited; needed for some alpha updates. Args: env: An OpenAI gym environment, either custom or built-in, but be aware that not all of them can be used in this setting. """ ns, na = env.observation_space.n, env.action_space.n self.Q = np.zeros((ns,na)) self.N = np.zeros((ns,na)) def policy_exploration(self, state, epsilon=0.0): """ The agent's current exploration policy. Right now we default to epsilon-greedy on the Q(s,a) values. Args: state: The current state the agent is in. epsilon: The probability of taking a random action. Returns: The action to take. """ na = self.Q.shape[1] action_probs = np.ones(na, dtype=float) * epsilon / na best_action = np.argmax(self.Q[state,:]) action_probs[best_action] += (1.0-epsilon) return np.random.choice(np.arange(na), p=action_probs) def alpha_schedule(self, t, state, action): """ The alpha scheduling. Args: t: The iteration of the current episode (t >= 1). state: The current state. action: The current action. Returns: The alpha to use for the Q-learning update. """ # return 0.5 # the most basic strategy alpha = self.N[state,action] ** -0.8 assert 0 < alpha <= 1, "Error, alpha = {}".format(alpha) return alpha def q_learning(self, num_episodes, max_ep_steps=10000, discount=1.0, epsilon=0.1): """ The Q-learning algorithm. Args: num_episodes: Number of episodes to run. max_ep_steps: Maximum time steps allocated to one episode. discount: Standard discount factor, usually denoted as \gamma. epsilon: Probability of taking random actions during exploration. Returns: A tuple (Q, stats) of the Q-values and statistics, which should be plotted and thoroughly analyzed. """ cum_t = 0 stats = plotting.EpisodeStats(episode_lengths=np.zeros(num_episodes), episode_rewards=np.zeros(num_episodes)) for i_episode in range(num_episodes): if (i_episode+1) % 1 == 0: print("\rEpisode {}/{}.".format(i_episode+1, num_episodes), end="") sys.stdout.flush() state = env.reset() # Run this episode until we finish as indicated by the environment. for t in range(1, max_ep_steps+1): # Uses exploration policy to take a step. action = self.policy_exploration(state, epsilon) next_state, reward, done, _ = env.step(action) # Collect statistics (cum_t currently not used). stats.episode_rewards[i_episode] += reward stats.episode_lengths[i_episode] = t self.N[state,action] += 1 cum_t += 1 # The official Q-learning update. alpha = self.alpha_schedule(t, state, action) best_next_action = np.argmax(self.Q[next_state,:]) td_target = reward + discount * self.Q[next_state,best_next_action] td_delta = td_target - self.Q[state,action] self.Q[state,action] += (alpha * td_delta) if done: break state = next_state print("") return self.Q, stats if __name__ == "__main__": """ This will run Q-learning. Be sure to double check all parameters, including the ones for plotting (e.g., file names). """ env = CliffWalkingEnv() agent = QLearningAgent(env) Q, stats = agent.q_learning(num_episodes=1000, max_ep_steps=500, discount=0.95, epsilon=0.1) plotting.plot_episode_stats(stats, smoothing_window=5, noshow=False, figdir="figures/cliff_", dosave=True) ================================================ FILE: q_learning/README.md ================================================ # Standard Tabular Q-learning ## Cliff World ### Environment I tested with `CliffWorldEnv` using the following settings: - number of episodes = 1000 - discount factor = 0.95 - alpha = n(s,a)^(-0.8) for the temporal difference update, where n(s,a) is the number of times that (s,a) have been visited. It's Equation 29 in reference [1]. - epsilon = 0.1 for greedy-epsilon exploration. Once the agent executes an action, it's deterministic. For rewards, the agent gets a -1 living reward, gets 0 if it manages to get to the bottom right corner, but gets -100 if it falls off the cliff. The agent starts at the bottom left corner and can move in one of four directions deterministically (but the **exploration policy** is epsilon-greedy based on the Q(s,a) values). ### Results First, episode reward over the trials, smoothed over a window of five trials: ![Rewards of episodes](figures/cliff_episode_reward_time.png?raw=true) Early on we get some -100s due to falling off the cliff, but later we get closer the theoretical best possible of -12. However, because Q-learning is off policy, it learns the path that goes directly next to the cliff, so in **exploration** it will often fall off the cliff due to greedy-epsilon. That's the reason for the spiky nature of the graph. When I printed the environment in the last round of training, the agent would always take the risky path, sometimes succeeding, sometimes failing. Next, we have the length of episodes (i.e. number of time steps). The minium we can get is 1 since the agent could actually dive into the pit on the first action. If the agent is following the "risky" path during training and manages to make it to the end (despite the randomness in the epsilon-greedy policy) then that's 13 steps. ![Length of episodes](figures/cliff_episode_length_time.png?raw=true) For the most part, this is what I'd expect. We want episodes to be shorter later because the agent "knows" where it's going now. ### References [1] Taming the Noise in Reinforcement Learning via Soft Updates, UAI 2016 ================================================ FILE: q_learning/__init__.py ================================================ ================================================ FILE: trpo/README.md ================================================ # Trust Region Policy Optimization Code outline: - `main.py` sets up the options and the top-level call to TRPO. - `trpo.py` contains the TRPO agent, describing how it gets paths, computes advantages, etc. - `utils_trpo.py` contains two particular utils ... - `fxn_approx.py` contains linear and neural network value functions. ================================================ FILE: trpo/fxn_approx.py ================================================ """ This will make some function approximators that we can use, particularly: linear and neural network value functions. Instantiate instances of these in other pieces of the code base. (c) April 2017 by Daniel Seita, built upon `starter code` from John Schulman. """ import numpy as np import tensorflow as tf import tensorflow.contrib.distributions as distr import sys if "../" not in sys.path: sys.path.append("../") from utils import utils_pg as utils np.set_printoptions(edgeitems=100) class LinearValueFunction(object): """ Estimates the baseline function for PGs via ridge regression. """ coef = None def fit(self, X, y): """ Updates weights (self.coef) with design matrix X (i.e. observations) and targets (i.e. actual returns) y. """ assert X.shape[0] == y.shape[0] assert len(y.shape) == 1 Xp = self.preproc(X) A = Xp.T.dot(Xp) nfeats = Xp.shape[1] A[np.arange(nfeats), np.arange(nfeats)] += 1e-3 # a little ridge regression b = Xp.T.dot(y) self.coef = np.linalg.solve(A, b) def predict(self, X): """ Predicts return from observations (i.e. environment states) X. """ if self.coef is None: return np.zeros(X.shape[0]) else: return self.preproc(X).dot(self.coef) def preproc(self, X): """ Adding a bias column, and also adding squared values (huh). """ return np.concatenate([np.ones([X.shape[0], 1]), X, np.square(X)/2.0], axis=1) class NnValueFunction(object): """ Estimates the baseline function for PGs via neural network. """ def __init__(self, session, ob_dim=None, n_epochs=10, stepsize=1e-3): """ They provide us with an ob_dim in the code so I assume we can use it; makes it easy to define the layers anyway. This gets constructed upon initialization so future calls to self.fit should remember this. I actually use the pre-processed version, though. """ self.n_epochs = n_epochs self.lrate = stepsize self.sy_ytarg = tf.placeholder(shape=[None], name="nnvf_y", dtype=tf.float32) self.sy_ob_no = tf.placeholder(shape=[None, ob_dim+1], name="nnvf_ob", dtype=tf.float32) self.sy_h1 = utils.lrelu(utils.dense(self.sy_ob_no, 32, "nnvf_h1", weight_init=utils.normc_initializer(1.0)), leak=0.0) self.sy_h2 = utils.lrelu(utils.dense(self.sy_h1, 32, "nnvf_h2", weight_init=utils.normc_initializer(1.0)), leak=0.0) self.sy_final_n = utils.dense(self.sy_h2, 1, "nnvf_final", weight_init=utils.normc_initializer(1.0)) self.sy_ypred = tf.reshape(self.sy_final_n, [-1]) self.sy_l2_error = tf.reduce_mean(tf.square(self.sy_ypred - self.sy_ytarg)) self.fit_op = tf.train.AdamOptimizer(stepsize).minimize(self.sy_l2_error) self.sess = session def fit(self, X, y): """ Updates weights (self.coef) with design matrix X (i.e. observations) and targets (i.e. actual returns) y. NOTE! We now return a dictionary `out` so that we can provide information relevant information for the logger. """ assert X.shape[0] == y.shape[0] assert len(y.shape) == 1 out = {} out["PredStdevBefore"]= self.predict(X).std() Xp = self.preproc(X) for i in range(self.n_epochs): _,err = self.sess.run( [self.fit_op, self.sy_l2_error], feed_dict={self.sy_ob_no: Xp, self.sy_ytarg: y }) if i == 0: out["MSEBefore"] = np.sqrt(err) if i == self.n_epochs-1: out["MSEAfter"] = np.sqrt(err) out["PredStdevAfter"] = self.predict(X).std() out["TargStdev"] = y.std() return out def predict(self, X): """ Predicts returns from observations (i.e. environment states) X. I also think we need a session here. No need to expand dimensions, BTW! It's effectively already done for us elsewhere. """ Xp = self.preproc(X) return self.sess.run(self.sy_ypred, feed_dict={self.sy_ob_no:Xp}) def preproc(self, X): """ Let's add this here to increase dimensionality. """ #return np.concatenate([np.ones([X.shape[0], 1]), X, np.square(X)/2.0], axis=1) return np.concatenate([np.ones([X.shape[0], 1]), X], axis=1) ================================================ FILE: trpo/main.py ================================================ """ This is the main point for the Trust Region Policy Optimization (TRPO) algorithm. Call this code using one of the bash scripts in my repository. Otherwise, to quickly test without saving, use: python main.py Pendulum-v0 --vf_type nn --do_not_save --render Disable --render if this is over SSH!! (There might be a way around this, I'm not sure.) (c) April 2017 by Daniel Seita. This code is built upon starter code from Berkeley CS 294-112. """ import argparse import gym import itertools import numpy as np import sys import tensorflow as tf import time if "../" not in sys.path: sys.path.append("../") from utils import logz from fxn_approx import * from trpo import * np.set_printoptions(edgeitems=100) def run_trpo_algorithm(args, vf_params, logdir): """ Runs TRPO and prints/saves the result as needed. Params: args: Contains a LOT of user-provided arguments. vf_params: Parameters for the value function we're using. logdir: Where to save the output, if desired. """ tf.set_random_seed(args.seed) np.random.seed(args.seed) env = gym.make(args.envname) logz.configure_output_dir(logdir) # Create `sess` here so that we can pass it to the NN value function. tf_config = tf.ConfigProto(inter_op_parallelism_threads=1, intra_op_parallelism_threads=1) sess = tf.Session(config=tf_config) # Create the TRPO agent, which will also construct its computational graph. TRPOAgent = TRPO(args, sess, env, vf_params) # Now some administration to get things started. sess.__enter__() tf.global_variables_initializer().run() #pylint: disable=E1101 stepsize = args.initial_stepsize tstart = time.time() seed_iter = itertools.count() # Official TRPO iterations. for i in range(args.n_iter): print("********** iteration %i ************"%i) infodict = {} vfdict = {} paths = TRPOAgent.get_paths(seed_iter, env) TRPOAgent.compute_advantages(paths) TRPOAgent.fit_value_function(paths, vfdict) TRPOAgent.update_policy(paths, infodict) TRPOAgent.log_diagnostics(paths, infodict, vfdict) print("\nAll done!") if __name__ == "__main__": # Get all the major arguments set up for TRPO here. p = argparse.ArgumentParser() p.add_argument('envname', type=str) p.add_argument('--cg_damping', type=float, default=0.1) p.add_argument('--do_not_save', action='store_true') p.add_argument('--gamma', type=float, default=0.98) p.add_argument('--initial_stepsize', type=float, default=1e-3) p.add_argument('--max_kl', type=float, default=0.01) p.add_argument('--min_timesteps_per_batch', type=int, default=5000) p.add_argument('--n_iter', type=int, default=250) p.add_argument('--nnvf_epochs', type=int, default=50) p.add_argument('--nnvf_ssize', type=float, default=1e-3) p.add_argument('--render', action='store_true') p.add_argument('--render_frequency', type=int, default=20) p.add_argument('--seed', type=int, default=0) p.add_argument('--vf_type', type=str, default='linear') args = p.parse_args() print("\nRunning TRPO with args:\n{}\n".format(args.__dict__)) assert args.vf_type == 'linear' or args.vf_type == 'nn' vf_params = {} outstr = 'linearvf-kl' +str(args.max_kl) if args.vf_type == 'nn': vf_params = dict(n_epochs=args.nnvf_epochs, stepsize=args.nnvf_ssize) outstr = 'nnvf-kl' +str(args.max_kl) outstr += '-cg' +str(args.cg_damping) outstr += '-seed' +str(args.seed).zfill(2) logdir = 'outputs/' +args.envname+ '/' +outstr if args.do_not_save: logdir = None run_trpo_algorithm(args, vf_params, logdir) ================================================ FILE: trpo/trpo.py ================================================ """ This contains the TRPO class. Following John's code, this will contain the bulk of the Tensorflow construction and related code. Call this from the `main.py` script. (c) April 2017 by Daniel Seita, based upon `starter code` by John Schulman, who used a Theano version. """ import gym import numpy as np import tensorflow as tf import time import utils_trpo from collections import defaultdict from fxn_approx import * np.set_printoptions(suppress=True, precision=5, edgeitems=10) import sys if "../" not in sys.path: sys.path.append("../") from utils import utils_pg as utils from utils import logz class TRPO: """ A TRPO agent. The constructor builds its computational graph. """ def __init__(self, args, sess, env, vf_params): """ Initializes the TRPO agent. For now, assume continuous control, so we'll be outputting the mean of Gaussian policies. It's similar to John Schulman's code. Here, `args` plays roughly the role of his `usercfg`, and we also initialize the computational graph here, this time in Tensorflow and not Theano. In his code, agents are already outfitted with value functions and policy functions, among other things. We do something similar by supplying the value function as input. For symbolic variables, I try to be consistent with the naming conventions at the end with `n`, `o`, and/or `a` to describe dimensions. """ self.args = args self.sess = sess self.env = env self.ob_dim = ob_dim = env.observation_space.shape[0] self.ac_dim = ac_dim = env.action_space.shape[0] if args.vf_type == 'linear': self.vf = LinearValueFunction(**vf_params) elif args.vf_type == 'nn': self.vf = NnValueFunction(session=sess, ob_dim=ob_dim, **vf_params) # Placeholders for the feed_dicts, i.e. the "beginning" of the graph. self.ob_no = tf.placeholder(shape=[None, ob_dim], name="ob", dtype=tf.float32) self.ac_na = tf.placeholder(shape=[None, ac_dim], name="ac", dtype=tf.float32) self.adv_n = tf.placeholder(shape=[None], name="adv", dtype=tf.float32) # Constructing the policy network, mapping from states -> mean vector. self.h1 = utils.lrelu(utils.dense(self.ob_no, 64, "h1", weight_init=utils.normc_initializer(1.0))) self.h2 = utils.lrelu(utils.dense(self.h1, 64, "h2", weight_init=utils.normc_initializer(1.0))) # Last layer of the network to get the mean, plus also an `old` version. self.mean_na = utils.dense(self.h2, ac_dim, "mean", weight_init=utils.normc_initializer(0.05)) self.oldmean_na = tf.placeholder(shape=[None, ac_dim], name='oldmean', dtype=tf.float32) # The log standard deviation *vector*, to be concatenated with the mean vector. self.logstd_a = tf.get_variable("logstd", [ac_dim], initializer=tf.zeros_initializer()) self.oldlogstd_a = tf.placeholder(shape=[ac_dim], name="oldlogstd", dtype=tf.float32) # In VPG, use logprob in surrogate loss. In TRPO, we also need the old one. self.logprob_n = utils.gauss_log_prob_1(mu=self.mean_na, logstd=self.logstd_a, x=self.ac_na) self.oldlogprob_n = utils.gauss_log_prob_1(mu=self.oldmean_na, logstd=self.oldlogstd_a, x=self.ac_na) self.surr = - tf.reduce_mean(self.adv_n * tf.exp(self.logprob_n - self.oldlogprob_n)) # Sample the action. Here, self.mean_na should be of shape (1,a). self.sampled_ac = (tf.random_normal(tf.shape(self.mean_na)) * tf.exp(self.logstd_a) + self.mean_na)[0] # Diagnostics, KL divergence, entropy. self.kl = tf.reduce_mean(utils.gauss_KL_1(self.mean_na, self.logstd_a, self.oldmean_na, self.oldlogstd_a)) self.ent = 0.5 * ac_dim * tf.log(2.*np.pi*np.e) + 0.5 * tf.reduce_sum(self.logstd_a) # Do we need these? ## self.nbatch = tf.shape(self.ob_no)[0] (maybe) ## self.stepsize = tf.placeholder(shape=[], dtype=tf.float32) (maybe) ## self.update_op = tf.train.AdamOptimizer(sy_stepsize).minimize(sy_surr) (almost surely delete) # Policy gradient vector. Only weights for the policy net, NOT value function. if args.vf_type == 'linear': self.params = tf.trainable_variables() elif args.vf_type == 'nn': self.params = [x for x in tf.trainable_variables() if 'nnvf' not in x.name] self.pg = self._flatgrad(self.surr, self.params) assert len((self.pg).get_shape()) == 1 # Prepare the Fisher-Vector product computation. I _think_ this is how # to do it, stopping gradients from the _current_ policy (not the old # one) so that the KL divegence is computed with a fixed first argument. # It seems to make sense from John Schulman's slides. Also, the # reduce_mean here should be the mean KL approximation to the max KL. kl_firstfixed = tf.reduce_mean(utils.gauss_KL_1( tf.stop_gradient(self.mean_na), tf.stop_gradient(self.logstd_a), self.mean_na, self.logstd_a )) grads = tf.gradients(kl_firstfixed, self.params) # Here, `flat_tangent` is a placeholder vector of size equal to #of (PG) # params. Then `tangents` contains various subsets of that vector. self.flat_tangent = tf.placeholder(tf.float32, shape=[None], name="flat_tangent") shapes = [var.get_shape().as_list() for var in self.params] start = 0 tangents = [] for shape in shapes: size = np.prod(shape) tangents.append(tf.reshape(self.flat_tangent[start:start+size], shape)) start += size self.num_params = start # Do elementwise g*tangent then sum components, then add everything at the end. # John Schulman used T.add(*[...]). The TF equivalent seems to be tf.add_n. assert len(grads) == len(tangents) self.gradient_vector_product = tf.add_n(inputs= [tf.reduce_sum(g*tangent) for (g, tangent) in zip(grads, tangents)] ) # The actual Fisher-vector product operation, where the gradients are # taken w.r.t. the "loss" function `gvp`. I _think_ the `grads` from # above computes the first derivatives, and then the `gvp` is computing # the second derivatives. But what about hessian_vector_product? self.fisher_vector_product = self._flatgrad(self.gradient_vector_product, self.params) # Deal with logic about *getting* parameters (as a flat vector). self.get_params_flat_op = tf.concat([tf.reshape(v, [-1]) for v in self.params], axis=0) # Finally, deal with logic about *setting* parameters. self.theta = tf.placeholder(tf.float32, shape=[self.num_params], name="theta") start = 0 updates = [] for v in self.params: shape = v.get_shape() size = tf.reduce_prod(shape) # Note that tf.assign(ref, value) assigns `value` to `ref`. updates.append( tf.assign(v, tf.reshape(self.theta[start:start+size], shape)) ) start += size self.set_params_flat_op = tf.group(*updates) # Performs all updates together. print("In TRPO init, shapes:\n{}\nstart={}".format(shapes, start)) print("self.pg: {}\ngvp: {}\nfvp: {}".format(self.pg, self.gradient_vector_product, self.fisher_vector_product)) print("Finished with the TRPO agent initialization.") self.start_time = time.time() def update_policy(self, paths, infodict): """ Performs the TRPO policy update based on a minibach of data. Note: this is mostly where the differences between TRPO and VPG become apparent. We do a conjugate gradient step followed by a line search. I'm not sure if we should be adjusting the step size based on the KL divergence, as we did in VPG. Right now we don't. This is where we do a lot of session calls, FYI. Params: paths: A LIST of defaultdicts with information from the rollouts. infodict: A dictionary with statistics for logging later. """ prob_np = np.concatenate([path["prob"] for path in paths]) ob_no = np.concatenate([path["observation"] for path in paths]) action_na = np.concatenate([path["action"] for path in paths]) adv_n = np.concatenate([path["advantage"] for path in paths]) assert prob_np.shape[0] == ob_no.shape[0] == action_na.shape[0] == adv_n.shape[0] assert len(prob_np.shape) == len(ob_no.shape) == len(action_na.shape) == 2 assert len(adv_n.shape) == 1 # Daniel: simply gets a flat vector of the parameters. thprev = self.sess.run(self.get_params_flat_op) # Make a feed to avoid clutter later. Note, our code differs slightly # from John Schulman as we have to explicitly provide the old means and # old logstds, which we concatenated together into the `prob` keyword. # The mean is the first half and the logstd is the second half. k = self.ac_dim feed = {self.ob_no: ob_no, self.ac_na: action_na, self.adv_n: adv_n, self.oldmean_na: prob_np[:,:k], self.oldlogstd_a: prob_np[0,k:]} # Use 0 because all logstd are same. # Had to add the extra flat_tangent to the feed, otherwise I'd get errors. def fisher_vector_product(p): feed[self.flat_tangent] = p fvp = self.sess.run(self.fisher_vector_product, feed_dict=feed) return fvp + self.args.cg_damping*p # Get the policy gradient. Also the losses, for debugging. g = self.sess.run(self.pg, feed_dict=feed) surrloss_before, kl_before, ent_before = self.sess.run( [self.surr, self.kl, self.ent], feed_dict=feed) assert kl_before == 0 if np.allclose(g, 0): print("\tGot zero gradient, not updating ...") else: stepdir = utils_trpo.cg(fisher_vector_product, -g) shs = 0.5*stepdir.dot(fisher_vector_product(stepdir)) lm = np.sqrt(shs / self.args.max_kl) infodict["LagrangeM"] = lm fullstep = stepdir / lm neggdotstepdir = -g.dot(stepdir) # Returns the current self.surr (surrogate loss). def loss(th): self.sess.run(self.set_params_flat_op, feed_dict={self.theta: th}) return self.sess.run(self.surr, feed_dict=feed) # Update the weights using `self.set_params_flat_op`. success, theta = utils_trpo.backtracking_line_search(loss, thprev, fullstep, neggdotstepdir/lm) self.sess.run(self.set_params_flat_op, feed_dict={self.theta: theta}) surrloss_after, kl_after, ent_after = self.sess.run( [self.surr, self.kl, self.ent], feed_dict=feed) logstd_new = self.sess.run(self.logstd_a, feed_dict=feed) print("logstd new = {}".format(logstd_new)) # For logging later. infodict["gNorm"] = np.linalg.norm(g) infodict["Success"] = success infodict["LagrangeM"] = lm infodict["pol_surr_before"] = surrloss_before infodict["pol_surr_after"] = surrloss_after infodict["pol_kl_before"] = kl_before infodict["pol_kl_after"] = kl_after infodict["pol_ent_before"] = ent_before infodict["pol_ent_after"] = ent_after def _flatgrad(self, loss, var_list): """ A Tensorflow version of John Schulman's `flatgrad` function. It computes the gradients but does NOT apply them (for now). This is only called during the `init` of the TRPO graph, so I think it's OK. Otherwise, wouldn't it be constantly rebuilding the computational graph? Or doing something else? Eh, for now I think it's OK. Params: loss: The loss function we're optimizing, which I assume is always scalar-valued. var_list: The list of variables (from `tf.trainable_variables()`) to take gradients. This should only be for the policynets. Returns: A single flat vector with all gradients concatenated. """ grads = tf.gradients(loss, var_list) return tf.concat([tf.reshape(g, [-1]) for g in grads], axis=0) def _act(self, ob): """ A "private" method for the TRPO agent so that it acts and then can provide extra information. Note that the mean and logstd here are for the current policy. There is no updating done here; that's done _afterwards_. The agentinfo is a vector of shape (2a,) where a is the action dimension. """ action, mean, logstd = self.sess.run( [self.sampled_ac, self.mean_na, self.logstd_a], feed_dict={self.ob_no : ob[None]} ) agentinfo = dict() agentinfo["prob"] = np.concatenate((mean.flatten(), logstd.flatten())) return (action, agentinfo) def get_paths(self, seed_iter, env): """ Computes the paths, which contains all the information from the rollouts that we need for the TRPO update. We run enough times (which may be many episodes) as desired from our user-provided parameters, storing relevant material into `paths` for future use. The main difference from VPG is that we have to get extra information about the current log probabilities (which will later be the _old_ log probs) when calling self.act(ob). Equivalent to John Schulman's `do_rollouts_serial` and `do_rollouts`. It's easy to put all lists inside a single defaultdict. Params: seed_iter: Itertools for getting new random seeds via incrementing. env: The current OpenAI gym environment. Returns: paths: A _list_ where each element is a _dictionary_ corresponding to statistics from ONE episode. """ paths = [] timesteps_sofar = 0 while True: np.random.seed(seed_iter.next()) ob = env.reset() data = defaultdict(list) # Run one episode and put the data inside `data`, then in `paths`. while True: data["observation"].append(ob) action, agentinfo = self._act(ob) data["action"].append(action) for (k,v) in agentinfo.iteritems(): data[k].append(v) ob, rew, done, _ = env.step(action) data["reward"].append(rew) if done: break data = {k:np.array(v) for (k,v) in data.iteritems()} paths.append(data) timesteps_sofar += utils.pathlength(data) if (timesteps_sofar >= self.args.min_timesteps_per_batch): break return paths def compute_advantages(self, paths): """ Computes standardized advantages from data collected during the most recent set of rollouts. No need to return anything, because advantages can be stored in `paths`. Also, self.vf is used to estimate the baseline to reduce variance, and later we will utilize the `path["baseline"]` to refit the value function. Finally, note that the iteration over `paths` means each `path` item is a dictionary, corresponding to the statistics garnered over ONE episode. This makes computing the discount easy since we don't have to worry about crossing over different episodes. Params: paths: A LIST of defaultdicts with information from the rollouts. Each defaultdict element contains information about ONE episode. """ for path in paths: path["reward"] = utils.discount(path["reward"], self.args.gamma) path["baseline"] = self.vf.predict(path["observation"]) path["advantage"] = path["reward"] - path["baseline"] adv_n = np.concatenate([path["advantage"] for path in paths]) for path in paths: path["advantage"] = (path["advantage"] - adv_n.mean()) / (adv_n.std() + 1e-8) def fit_value_function(self, paths, vfdict): """ Fits the TRPO's value function with the current minibatch of data. Also takes in another dictionary, `vfdict`, for relevant statistics related to the value function. """ ob_no = np.concatenate([path["observation"] for path in paths]) vtarg_n = np.concatenate([path["reward"] for path in paths]) assert ob_no.shape[0] == vtarg_n.shape[0] out = self.vf.fit(ob_no, vtarg_n) for key in out: vfdict[key] = out[key] def log_diagnostics(self, paths, infodict, vfdict): """ Just logging using the `logz` functionality. """ ob_no = np.concatenate([path["observation"] for path in paths]) vpred_n = np.concatenate([path["baseline"] for path in paths]) vtarg_n = np.concatenate([path["reward"] for path in paths]) elapsed_time = (time.time() - self.start_time) # In seconds episode_rewards = np.array([path["reward"].sum() for path in paths]) episode_lengths = np.array([utils.pathlength(path) for path in paths]) # These are *not* logged in John Schulman's code. #logz.log_tabular("Success", infodict["Success"]) #logz.log_tabular("LagrangeM", infodict["LagrangeM"]) #logz.log_tabular("gNorm", infodict["gNorm"]) # These *are* logged in John Schulman's code. First, rewards: logz.log_tabular("NumEpBatch", len(paths)) logz.log_tabular("EpRewMean", episode_rewards.mean()) logz.log_tabular("EpRewMax", episode_rewards.max()) logz.log_tabular("EpRewSEM", episode_rewards.std()/np.sqrt(len(paths))) logz.log_tabular("EpLenMean", episode_lengths.mean()) logz.log_tabular("EpLenMax", episode_lengths.max()) logz.log_tabular("RewPerStep", episode_rewards.sum()/episode_lengths.sum()) logz.log_tabular("vf_mse_before", vfdict["MSEBefore"]) logz.log_tabular("vf_mse_after", vfdict["MSEAfter"]) logz.log_tabular("vf_PredStdevBefore", vfdict["PredStdevBefore"]) logz.log_tabular("vf_PredStdevAfter", vfdict["PredStdevAfter"]) logz.log_tabular("vf_TargStdev", vfdict["TargStdev"]) logz.log_tabular("vf_EV_before", utils.explained_variance_1d(vpred_n, vtarg_n)) logz.log_tabular("vf_EV_after", utils.explained_variance_1d(self.vf.predict(ob_no), vtarg_n)) # If overfitting, EVAfter >> EVBefore. Also, we fit the value function # _after_ using it to compute the baseline to avoid introducing bias. logz.log_tabular("pol_surr_before", infodict["pol_surr_before"]) logz.log_tabular("pol_surr_after", infodict["pol_surr_after"]) logz.log_tabular("pol_kl_before", infodict["pol_kl_before"]) logz.log_tabular("pol_kl_after", infodict["pol_kl_after"]) logz.log_tabular("pol_ent_before", infodict["pol_ent_before"]) logz.log_tabular("pol_ent_after", infodict["pol_ent_after"]) logz.log_tabular("TimeElapsed", elapsed_time) logz.dump_tabular() ================================================ FILE: trpo/utils_trpo.py ================================================ """ Some TRPO-specific stuff, which conatins the conjugate gradient and backtracking line search methods. Both of these methods are borrowed from John Schulman's code: https://github.com/joschu/modular_rl They have been slightly modified to suit my code. """ import numpy as np def cg(f_Ax, b, cg_iters=10, verbose=False, residual_tol=1e-10): """ Conjugate gradient, from John Schulman's code. Sculman used Demmel's book on applied linear algebra, page 312. Fortunately I have a copy of it!! Shewchuk also has a version of this in his paper. However, Shewchuk emphasizes that this is most useful for *sparse* matrices `A`. We certainly have a *large* matrix since the number of rows/columns is equal to the number of neural network parameters, but is it sparse? This is used for solving linear systems of `Ax = b`, or `x = A^{-1}b`. In TRPO, we don't want to compute `A` (let alone its inverse). In addition, `b` is our usual policy gradient. The goal is to find `A^{-1}b` and then later (outside this code) scale that by `alpha`, and then we get the update at last. I *think* the alpha-scaling comes from the line search, but I'm not sure yet. Params: f_Ax: A function designed to mimic A*(input). However, we *don't* have the entire matrix A formed. It's the "Fisher-vector" product and should be computed with Tensorflow. [TODO HOW??] b: A known vector. In TRPO, it's the vanilla policy gradient (I think). cg_iters: Number of iterations of CG. verbose: Print extra information for debugging. residual_tol: Exit CG if ||r||_2^2 is small enough. Returns: Our estimate of `A^{-1}b` where A is (approximately?) the Hessian of the KL divergence and `b` is given to us. """ p = b.copy() r = b.copy() x = np.zeros_like(b) rdotr = r.dot(r) fmtstr = "%10i %10.3g %10.3g" titlestr = "%10s %10s %10s" if verbose: print titlestr % ("iter", "residual norm", "soln norm") for i in xrange(cg_iters): if verbose: print fmtstr % (i, rdotr, np.linalg.norm(x)) z = f_Ax(p) v = rdotr / p.dot(z) x += v*p r -= v*z newrdotr = r.dot(r) mu = newrdotr/rdotr p = r + mu*p rdotr = newrdotr if rdotr < residual_tol: break if verbose: print fmtstr % (i+1, rdotr, np.linalg.norm(x)) # pylint: disable=W0631 return x def backtracking_line_search(f, x, fullstep, expected_improve_rate, max_backtracks=10, accept_ratio=0.1): """ Backtracking line search, from John Schulman's code. I think this is the same as what's listed in Boyd & Vandenberghe's book. Remember that with backtracking line search, we have a fixed descent direction (typically the negative gradient, as I explain in my blog post) and we have to progressively decrease the step size. Here, that's `stepfrac`. Remember, this is *one* case of backtracking line search. We are *not* changing any directions, i.e. `fullstep` is our only direction we have. Also, conjugate gradient comes *before* this because we need to get the `fullstep` from it. Params: f: The function we're trying to minimize. x: The starting point for backtracking line search. Remember, it's really `theta` since it's the parameters of our policy net. fullstep: The descent direction, provided by conjugate gradient! expected_improve_rate: The slope dy/dx at the initial point. max_backtracks: The maximum amount of iterations (i.e. backtracks). accept_ratio: The ratio which helps to determine our stopping criterion. It seems like a variant of most textbook descriptions of backtracking line search; here, I think it's to ensure we get above a threshold of improvement. Returns: A tuple (Y, x) where Y is a boolean indicating whether we've successfully found a new point to go to, and x is that final point, or the original x if the line search didn't find a better point. """ fval = f(x) print("fval before {}".format(fval)) for (_n_backtracks, stepfrac) in enumerate(.5**np.arange(max_backtracks)): xnew = x + stepfrac*fullstep newfval = f(xnew) actual_improve = fval - newfval expected_improve = expected_improve_rate*stepfrac ratio = actual_improve/expected_improve print("actual_i / expected_i / ratio = {:.4f} / {:.4f} / {:.4f}".format( actual_improve, expected_improve, ratio)) if ratio > accept_ratio and actual_improve > 0: print("fval after {:.4f}".format(newfval)) return (True, xnew) return (False, x) ================================================ FILE: utils/__init__.py ================================================ ================================================ FILE: utils/logz.py ================================================ """ Some simple logging functionality, inspired by rllab's logging. Assumes that each diagnostic gets logged each iteration Call logz.configure_output_dir() to start logging to a tab-separated-values file (some_folder_name/log.txt) To load the learning curves, you can do, for example A = np.genfromtxt('/tmp/expt_1468984536/log.txt',delimiter='\t',dtype=None, names=True) A['EpRewMean'] """ import os.path as osp, shutil, time, atexit, os, subprocess color2num = dict( gray=30, red=31, green=32, yellow=33, blue=34, magenta=35, cyan=36, white=37, crimson=38 ) def colorize(string, color, bold=False, highlight=False): attr = [] num = color2num[color] if highlight: num += 10 attr.append(str(num)) if bold: attr.append('1') return '\x1b[%sm%s\x1b[0m' % (';'.join(attr), string) class G: output_dir = None output_file = None first_row = True log_headers = [] log_current_row = {} def configure_output_dir(d=None): """ Set output directory to d, or to /tmp/somerandomnumber if d is None """ G.output_dir = d or "/tmp/experiments/%i"%int(time.time()) assert not osp.exists(G.output_dir), "Log dir %s already exists! Delete it first or use a different dir"%G.output_dir os.makedirs(G.output_dir) G.output_file = open(osp.join(G.output_dir, "log.txt"), 'w') atexit.register(G.output_file.close) try: cmd = "cd %s && git diff > %s 2>/dev/null"%(osp.dirname(__file__), osp.join(G.output_dir, "a.diff")) subprocess.check_call(cmd, shell=True) # Save git diff to experiment directory except subprocess.CalledProcessError: print("configure_output_dir: not storing the git diff, probably because you're not in a git repo") print(colorize("Logging data to %s"%G.output_file.name, 'green', bold=True)) def log_tabular(key, val): """ Log a value of some diagnostic Call this once for each diagnostic quantity, each iteration """ if G.first_row: G.log_headers.append(key) else: assert key in G.log_headers, "Trying to introduce a new key %s that you didn't include in the first iteration"%key assert key not in G.log_current_row, "You already set %s this iteration. Maybe you forgot to call dump_tabular()"%key G.log_current_row[key] = val def dump_tabular(): """ Write all of the diagnostics from the current iteration """ vals = [] print("-"*47) for key in G.log_headers: val = G.log_current_row.get(key, "") if hasattr(val, "__float__"): valstr = "%8.4g"%val else: valstr = val print("| %20s | %20s |"%(key, valstr)) vals.append(val) print("-"*47) if G.output_file is not None: if G.first_row: G.output_file.write("\t".join(G.log_headers)) G.output_file.write("\n") G.output_file.write("\t".join(map(str,vals))) G.output_file.write("\n") G.output_file.flush() G.log_current_row.clear() G.first_row=False ================================================ FILE: utils/policies.py ================================================ """ For managing policies. This seems like a better way to organize things. For now, we have **stochastic** policies. This assumes the Python3 way of calling superclasses' init methods. TODO figure out a good way to integrate deterministic policies, figure out how to get a good configuration file (for neural nets), etc. Lots of fun! :) TODO figure out how to make assertions that we're in continuous vs discrete spaces. TODO have a net specification which we can use instead of hard-coding networks here. """ import numpy as np import sys import tensorflow as tf import tensorflow.contrib.layers as layers from . import utils_pg as utils class StochasticPolicy(object): def __init__(self, sess, ob_dim, ac_dim): """ Initializes the neural network policy. Right now there isn't much here, but this is a flexible design pattern for future versions of the code. """ self.sess = sess def sample_action(self, x): """ To be implemented in the subclass. """ raise NotImplementedError class GibbsPolicy(StochasticPolicy): """ A policy where the action is to be sampled based on sampling a categorical random variable; this is for discrete control. """ def __init__(self, sess, ob_dim, ac_dim): super().__init__(sess, ob_dim, ac_dim) # Placeholders for our inputs. self.ob_no = tf.placeholder(shape=[None, ob_dim], name="obs", dtype=tf.float32) self.ac_n = tf.placeholder(shape=[None], name="act", dtype=tf.int32) self.adv_n = tf.placeholder(shape=[None], name="adv", dtype=tf.float32) self.oldlogits_na = tf.placeholder(shape=[None, ac_dim], name='oldlogits', dtype=tf.float32) # Form the policy network and the log probabilities. self.hidden1 = layers.fully_connected(self.ob_no, num_outputs=50, weights_initializer=layers.xavier_initializer(uniform=True), activation_fn=tf.nn.tanh) self.logits_na = layers.fully_connected(self.hidden1, num_outputs=ac_dim, weights_initializer=layers.xavier_initializer(uniform=True), activation_fn=None) self.logp_na = tf.nn.log_softmax(self.logits_na) # Log probabilities of the actions in the minibatch, plus sampled action. self.nbatch = tf.shape(self.ob_no)[0] self.logprob_n = utils.fancy_slice_2d(self.logp_na, tf.range(self.nbatch), self.ac_n) self.sampled_ac = utils.categorical_sample_logits(self.logits_na)[0] # Policy gradients loss function and training step. self.surr_loss = - tf.reduce_mean(self.logprob_n * self.adv_n) self.stepsize = tf.placeholder(shape=[], dtype=tf.float32) self.update_op = tf.train.AdamOptimizer(self.stepsize).minimize(self.surr_loss) # For KL divergence and entropy diagnostic purposes. These are computed # as averages across individual KL/entropy w.r.t each minibatch state. self.oldlogp_na = tf.nn.log_softmax(self.oldlogits_na) self.oldp_na = tf.exp(self.oldlogp_na) self.p_na = tf.exp(self.logp_na) self.kl_n = tf.reduce_sum(self.oldp_na * (self.oldlogp_na - self.logp_na), axis=1) # I'm not sure why the KL divergence can be slightly negative. Each row # corresponds to a valid distribution. Must be numerical instability? self.assert_op = tf.Assert(tf.reduce_all(self.kl_n >= -1e-4), [self.kl_n]) with tf.control_dependencies([self.assert_op]): self.kl_n = tf.identity(self.kl_n) self.kl = tf.reduce_mean(self.kl_n) self.ent = tf.reduce_mean(tf.reduce_sum( -self.p_na * self.logp_na, axis=1)) def sample_action(self, ob): return self.sess.run(self.sampled_ac, feed_dict={self.ob_no: ob[None]}) def update_policy(self, ob_no, ac_n, std_adv_n, stepsize): """ Upon getting observations, those are fed through the network to get the logits. After this the computational graph eventually updates the policy, so the logits are now old logits. """ feed = {self.ob_no: ob_no, self.ac_n: ac_n, self.adv_n: std_adv_n, self.stepsize: stepsize} _, surr_loss, oldlogits_na = self.sess.run( [self.update_op, self.surr_loss, self.logits_na], feed_dict=feed) return surr_loss, oldlogits_na def kldiv_and_entropy(self, ob_no, oldlogits_na): """ Returning KL diverence and current entropy since they can re-use some of the computation. In particular, the entropy doesn't need the old logits, since it just forwards the observation batch through the network to find the current log probabilities. """ return self.sess.run([self.kl, self.ent], feed_dict={self.ob_no:ob_no, self.oldlogits_na:oldlogits_na}) class GaussianPolicy(StochasticPolicy): """ A policy where the action is to be sampled based on sampling a Gaussian; this is for continuous control. """ def __init__(self, sess, ob_dim, ac_dim): super().__init__(sess, ob_dim, ac_dim) # Placeholders for our inputs. Note that actions are floats. self.ob_no = tf.placeholder(shape=[None, ob_dim], name="obs", dtype=tf.float32) self.ac_na = tf.placeholder(shape=[None, ac_dim], name="act", dtype=tf.float32) self.adv_n = tf.placeholder(shape=[None], name="adv", dtype=tf.float32) self.n = tf.shape(self.ob_no)[0] # Special to the continuous case, the log std vector, it's a parameter. # Also, make batch versions so we get shape (n,a) (or (1,a)), not (a,). self.logstd_a = tf.get_variable("logstd", [ac_dim], initializer=tf.zeros_initializer()) self.oldlogstd_a = tf.placeholder(name="oldlogstd", shape=[ac_dim], dtype=tf.float32) self.logstd_na = tf.ones(shape=(self.n,ac_dim), dtype=tf.float32) * self.logstd_a self.oldlogstd_na = tf.ones(shape=(self.n,ac_dim), dtype=tf.float32) * self.oldlogstd_a # The policy network and the logits, which are the mean of a Gaussian. # Then don't forget to make an "old" version of that for KL divergences. self.hidden1 = layers.fully_connected(self.ob_no, num_outputs=32, weights_initializer=layers.xavier_initializer(uniform=True), activation_fn=tf.nn.relu) self.hidden2 = layers.fully_connected(self.hidden1, num_outputs=32, weights_initializer=layers.xavier_initializer(uniform=True), activation_fn=tf.nn.relu) self.mean_na = layers.fully_connected(self.hidden2, num_outputs=ac_dim, weights_initializer=layers.xavier_initializer(uniform=True), activation_fn=None) self.oldmean_na = tf.placeholder(shape=[None, ac_dim], name='oldmean', dtype=tf.float32) # Diagonal Gaussian distribution for sampling actions and log probabilities. self.logprob_n = utils.gauss_log_prob(mu=self.mean_na, logstd=self.logstd_na, x=self.ac_na) self.sampled_ac = (tf.random_normal(tf.shape(self.mean_na)) * tf.exp(self.logstd_na) + self.mean_na)[0] # Loss function that we'll differentiate to get the policy gradient self.surr_loss = - tf.reduce_mean(self.logprob_n * self.adv_n) self.stepsize = tf.placeholder(shape=[], dtype=tf.float32) self.update_op = tf.train.AdamOptimizer(self.stepsize).minimize(self.surr_loss) # KL divergence and entropy among Gaussian(s). self.kl = tf.reduce_mean(utils.gauss_KL(self.mean_na, self.logstd_na, self.oldmean_na, self.oldlogstd_na)) self.ent = 0.5 * ac_dim * tf.log(2.*np.pi*np.e) + 0.5 * tf.reduce_sum(self.logstd_a) def sample_action(self, ob): return self.sess.run(self.sampled_ac, feed_dict={self.ob_no: ob[None]}) def update_policy(self, ob_no, ac_n, std_adv_n, stepsize): """ The input is the same for the discrete control case, except we return a single log standard deviation vector in addition to our logits. In this case, the logits are really the mean vector of Gaussians, which differs among components (observations) in the minbatch. We return the *old* ones since they are assigned, then `self.update_op` runs, which makes them outdated. """ feed = {self.ob_no: ob_no, self.ac_na: ac_n, self.adv_n: std_adv_n, self.stepsize: stepsize} _, surr_loss, oldmean_na, oldlogstd_a = self.sess.run( [self.update_op, self.surr_loss, self.mean_na, self.logstd_a], feed_dict=feed) return surr_loss, oldmean_na, oldlogstd_a def kldiv_and_entropy(self, ob_no, oldmean_na, oldlogstd_a): """ Returning KL diverence and current entropy since they can re-use some of the computation. For the KL divergence, though, we reuqire the old mean *and* the old log standard deviation to fully characterize the set of probability distributions we had earlier, each conditioned on different states in the MDP. Then we take the *average* of these, etc., similar to the discrete case. """ feed = {self.ob_no: ob_no, self.oldmean_na: oldmean_na, self.oldlogstd_a: oldlogstd_a} return self.sess.run([self.kl, self.ent], feed_dict=feed) ================================================ FILE: utils/utils_pg.py ================================================ """ Several utilities to reduce clutter in my policy gradient codes. (c) April-June 2017 (mostly) by Daniel Seita """ import numpy as np import tensorflow as tf import scipy.signal import sys def gauss_log_prob_1(mu, logstd, x): """ Calls `gauss_log_prob` with a broadcasted version of logstd. Assumes that logstd is of shape (n,) and mu is of shape (n,a). """ assert mu.get_shape()[1:] == x.get_shape()[1:] assert len(logstd.get_shape()) == 1 logstd_broadcasted = tf.ones(shape=tf.shape(mu), dtype=tf.float32) * logstd return gauss_log_prob(mu, logstd_broadcasted, x) def gauss_log_prob(mu, logstd, x): """ Used for computing the log probability, following the formula for the multivariate Gaussian density. All the inputs should have shape (n,a). The `gp_na` contains component-wise probabilitiles, then the reduce_sum results in a tensor of size (n,) which contains the log probability for each of the n elements. (We later perform a mean on this.) Also, the 2*pi part needs 1/2, but doesn't need the sum over the number of components (# of actions) because of the reduce sum here. Finally, logstd doesn't need a 1/2 constant because log(\sigma_i^2) will bring the 2 over. This formula generalizes for an arbitrary number of actions, BUT it assumes that the covariance matrix is diagonal. """ var_na = tf.exp(2*logstd) gp_na = -tf.square(x - mu)/(2*var_na) - 0.5*tf.log(tf.constant(2*np.pi)) - logstd return tf.reduce_sum(gp_na, axis=[1]) def gauss_KL_1(mu1, logstd1, mu2, logstd2): """ Calls `gauss_KL` with a broadcasted version of logstd1 and logstd1. Assumes that logstd1 and logstd2 are of shape (n,). """ assert mu1.get_shape()[1:] == mu2.get_shape()[1:] assert len(logstd1.get_shape()) == 1 assert len(logstd2.get_shape()) == 1 logstd1_broadcasted = tf.ones(shape=tf.shape(mu1), dtype=tf.float32) * logstd1 logstd2_broadcasted = tf.ones(shape=tf.shape(mu2), dtype=tf.float32) * logstd2 return gauss_KL(mu1, logstd1_broadcasted, mu2, logstd2_broadcasted) def gauss_KL(mu1, logstd1, mu2, logstd2): """ Returns KL divergence among two multivariate Gaussians, component-wise. It assumes the covariance matrix is diagonal. All inputs have shape (n,a). It is not necessary to know the number of actions because reduce_sum will sum over this to get the `d` constant offset. The part consisting of the trace in the formula is blended with the mean difference squared due to the common "denominator" of var2_na. This forumula generalizes for an arbitrary number of actions. I think mu2 and logstd2 should represent the policy before the update. Returns the KL divergence for each of the n components in the minibatch, then we do a reduce_mean outside this. """ var1_na = tf.exp(2.*logstd1) var2_na = tf.exp(2.*logstd2) tmp_matrix = 2.*(logstd2 - logstd1) + (var1_na + tf.square(mu1-mu2))/var2_na - 1 kl_n = tf.reduce_sum(0.5 * tmp_matrix, axis=[1]) # Don't forget the 1/2 !! assert_op = tf.Assert(tf.reduce_all(kl_n >= -0.0000001), [kl_n]) with tf.control_dependencies([assert_op]): kl_n = tf.identity(kl_n) return kl_n def normc_initializer(std=1.0): """ Initialize array with normalized columns """ def _initializer(shape, dtype=None, partition_info=None): #pylint: disable=W0613 out = np.random.randn(*shape).astype(np.float32) out *= std / np.sqrt(np.square(out).sum(axis=0, keepdims=True)) return tf.constant(out) return _initializer def dense(x, size, name, weight_init=None): """ Dense (fully connected) layer """ w = tf.get_variable(name + "/w", [x.get_shape()[1], size], initializer=weight_init) b = tf.get_variable(name + "/b", [size], initializer=tf.zeros_initializer()) return tf.matmul(x, w) + b def fancy_slice_2d(X, inds0, inds1): """ Like numpy's X[inds0, inds1] """ inds0 = tf.cast(inds0, tf.int64) inds1 = tf.cast(inds1, tf.int64) shape = tf.cast(tf.shape(X), tf.int64) ncols = shape[1] Xflat = tf.reshape(X, [-1]) return tf.gather(Xflat, inds0 * ncols + inds1) def discount(x, gamma): """ Compute discounted sum of future values. Returns a list, NOT a scalar! out[i] = in[i] + gamma * in[i+1] + gamma^2 * in[i+2] + ... """ return scipy.signal.lfilter([1],[1,-gamma],x[::-1], axis=0)[::-1] def lrelu(x, leak=0.2): """ Performs a leaky ReLU operation. """ f1 = 0.5 * (1 + leak) f2 = 0.5 * (1 - leak) return f1 * x + f2 * abs(x) def explained_variance_1d(ypred,y): """ Var[ypred - y] / var[y]. https://www.quora.com/What-is-the-meaning-proportion-of-variance-explained-in-linear-regression """ assert y.ndim == 1 and ypred.ndim == 1 vary = np.var(y) return np.nan if vary==0 else 1 - np.var(y-ypred)/vary def categorical_sample_logits(logits): """ Samples (symbolically) from categorical distribution, where logits is a NxK matrix specifying N categorical distributions with K categories specifically, exp(logits) / sum( exp(logits), axis=1 ) is the probabilities of the different classes Cleverly uses gumbell trick, based on https://github.com/tensorflow/tensorflow/issues/456 """ U = tf.random_uniform(tf.shape(logits)) return tf.argmax(logits - tf.log(-tf.log(U)), dimension=1) def pathlength(path): return len(path["reward"]) ================================================ FILE: utils/value_functions.py ================================================ """ Value functions, for now we apply them to policy gradients. This uses Python3 importing syntax. """ import numpy as np import sys import tensorflow as tf import tensorflow.contrib.layers as layers from . import utils_pg as utils class LinearValueFunction(object): """ Estimates the baseline function for PGs via ridge regression. """ def __init__(self): self.coef = None def fit(self, X, y): """ Updates weights (self.coef) with design matrix X (i.e. observations) and targets (i.e. actual returns) y. """ assert X.shape[0] == y.shape[0] assert len(y.shape) == 1 Xp = self.preproc(X) A = Xp.T.dot(Xp) nfeats = Xp.shape[1] A[np.arange(nfeats), np.arange(nfeats)] += 1e-3 # a little ridge regression b = Xp.T.dot(y) self.coef = np.linalg.solve(A, b) def predict(self, X): """ Predicts return from observations (i.e. environment states) X. """ if self.coef is None: return np.zeros(X.shape[0]) else: return self.preproc(X).dot(self.coef) def preproc(self, X): """ Adding a bias column, and also adding squared values (huh). """ return np.concatenate([np.ones([X.shape[0], 1]), X, np.square(X)/2.0], axis=1) class NnValueFunction(object): """ Estimates the baseline function for PGs via neural network. """ def __init__(self, session, ob_dim=None, n_epochs=20, stepsize=1e-3): """ The network gets constructed upon initialization so future calls to self.fit will remember this. Right now we assume a preprocessing which results ob_dim*2+1 dimensions, and we assume a fixed neural network architecture (input-50-50-1, fully connected with tanh nonlineariites), which we should probably change. The number of outputs is one, so that ypreds_n is the predicted vector of state values, to be compared against ytargs_n. Since ytargs_n is of shape (n,), we need to apply a "squeeze" on the final predictions, which would otherwise be of shape (n,1). Bleh. """ # Value function V(s_t) (or b(s_t)), parameterized as a neural network. self.ob_no = tf.placeholder(shape=[None, ob_dim*2+1], name="nnvf_ob", dtype=tf.float32) self.h1 = layers.fully_connected(self.ob_no, num_outputs=50, weights_initializer=layers.xavier_initializer(uniform=True), activation_fn=tf.nn.tanh) self.h2 = layers.fully_connected(self.h1, num_outputs=50, weights_initializer=layers.xavier_initializer(uniform=True), activation_fn=tf.nn.tanh) self.ypreds_n = layers.fully_connected(self.h2, num_outputs=1, weights_initializer=layers.xavier_initializer(uniform=True), activation_fn=None) self.ypreds_n = tf.reshape(self.ypreds_n, [-1]) # (?,1) --> (?,). =) # Form the loss function, which is the simple (mean) L2 error. self.n_epochs = n_epochs self.lrate = stepsize self.ytargs_n = tf.placeholder(shape=[None], name="nnvf_y", dtype=tf.float32) self.l2_error = tf.reduce_mean(tf.square(self.ypreds_n - self.ytargs_n)) self.fit_op = tf.train.AdamOptimizer(self.lrate).minimize(self.l2_error) self.sess = session def fit(self, X, y, session=None): """ Updates weights (self.coef) with design matrix X (i.e. observations) and targets (i.e. actual returns) y. For now, assume that by refitting, we'll be doing it several times (`n_epoch` times). """ assert X.shape[0] == y.shape[0] assert len(y.shape) == 1 Xp = self.preproc(X) for i in range(self.n_epochs): _,err = self.sess.run( [self.fit_op, self.l2_error], feed_dict={self.ob_no: Xp, self.ytargs_n: y }) def predict(self, X): """ Predicts returns from observations (i.e. environment states) X. I also think we need a session here. No need to expand dimensions, BTW! It's effectively already done for us elsewhere. """ Xp = self.preproc(X) return self.sess.run(self.ypreds_n, feed_dict={self.ob_no:Xp}) def preproc(self, X): """ Let's add this here to increase dimensionality. """ return np.concatenate([np.ones([X.shape[0], 1]), X, np.square(X)/2.0], axis=1) ================================================ FILE: vpg/README.md ================================================ # Vanilla Policy Gradients This is the standard vanilla policy gradients with stochastic policies, either continuous or discrete. I based this off of CS 294-112 starter code. I'm using Python 3.5.2 and Tensorflow 1.2.0. This code will not work with Python 2.7.x. Note to self: when running bash scripts in GNU screen mode, be sure to source my Python 3 conda environment. # Simple Baselines ## CartPole-v0 Based on `bash_scripts/CartPole-v0.sh`: ![](figures/CartPole-v0.png?raw=true) ![](figures/CartPole-v0_sm.png?raw=true) Architectures: - **Policy**: (input) - 50 - (output), tanh - **NN vf**: (input) - 50 - 50 - (output), tanh ## Pendulum-v0 Based on `bash_scripts/Pendulum-v0.sh`: ![Pendulum-v0](figures/Pendulum-v0.png?raw=true) ![Pendulum-v0_sm](figures/Pendulum-v0_sm.png?raw=true) Architectures: - **Policy**: (input) - 32 - 32 - (output), relu - **NN vf**: (input) - 50 - 50 - (output), tanh I think it looks OK. Pendulum is a bit tricky to solve because it requires an adaptive learning rate but I still get close to about -100 or so. I'm not sure what the theoretical best solution is; maybe zero, but that seems impossible. The neural network is only slightly better with these results (I guess?) because the problem is so simple. The action dimension is just one. **TODO** haven't tested with these with new API ... # MuJoCo Baselines Tested on in alphabetical order: - HalfCheetah-v1 - Hopper-v1 - Walker2d-v1 ## HalfCheetah-v1 The raw runs based on `bash_scripts/halfcheetah.sh`: ![HalfCheetah-v1](figures/HalfCheetah-v1.png?raw=true) And the smooth runs: ![HalfCheetah-v1_sm](figures/HalfCheetah-v1_sm.png?raw=true) The GAIL paper said HalfCheetah-v1 should get around 4463.46 ± 105.83 and in fact we are almost getting to that level. That's interesting. What's confusing is that the explained variance for the linear case seems to be terrible. Then why is the linear VF even working, and why is it just barely worse than the NN value function? Hmmm ... I may want to catch a video of this in action. ## Hopper-v1 I used the script in `bash_scripts/hopper.sh`. Here are the raw results: ![Hopper-v1](figures/Hopper-v1.png?raw=true) And now the smoothed versions: ![Hopper-v1_sm](figures/Hopper-v1_sm.png?raw=true) (Ho & Ermon 2016) showed in the GAIL paper that Hopper-v1 should get 3571.38 with a standard deviation of 184.20 so ... yeah, these results are a bit sub-par! But at least they are learning *something*. Maybe my version of TRPO will do better. ## Walker2d-v1 Next, Walker2d-v1. The raw runs based on `bash_scripts/walker.sh`: ![Walker2d-v1](figures/Walker2d-v1.png?raw=true) And the smooth runs: ![Walker2d-v1_sm](figures/Walker2d-v1_sm.png?raw=true) The GAIL paper said Walker-v1 should get around 6717.08 p/m 845.62, but that might not be the same as Walker2d-v1. I'm not sure ... and the code Jonathan Ho has for imitation learning doesn't do as well. ================================================ FILE: vpg/bash_scripts/CartPole-v0.sh ================================================ python main.py CartPole-v0 --vf_type linear --seed 0 --initial_stepsize 0.01 --n_iter 100 python main.py CartPole-v0 --vf_type nn --seed 0 --initial_stepsize 0.01 --n_iter 100 python main.py CartPole-v0 --vf_type linear --seed 1 --initial_stepsize 0.01 --n_iter 100 python main.py CartPole-v0 --vf_type nn --seed 1 --initial_stepsize 0.01 --n_iter 100 python main.py CartPole-v0 --vf_type linear --seed 2 --initial_stepsize 0.01 --n_iter 100 python main.py CartPole-v0 --vf_type nn --seed 2 --initial_stepsize 0.01 --n_iter 100 ================================================ FILE: vpg/bash_scripts/Pendulum-v0.sh ================================================ python main.py Pendulum-v0 --vf_type linear --seed 0 --desired_kl 2e-3 --use_kl_heuristic --n_iter 400 python main.py Pendulum-v0 --vf_type nn --seed 0 --desired_kl 2e-3 --use_kl_heuristic --n_iter 400 python main.py Pendulum-v0 --vf_type linear --seed 1 --desired_kl 2e-3 --use_kl_heuristic --n_iter 400 python main.py Pendulum-v0 --vf_type nn --seed 1 --desired_kl 2e-3 --use_kl_heuristic --n_iter 400 python main.py Pendulum-v0 --vf_type linear --seed 2 --desired_kl 2e-3 --use_kl_heuristic --n_iter 400 python main.py Pendulum-v0 --vf_type nn --seed 2 --desired_kl 2e-3 --use_kl_heuristic --n_iter 400 ================================================ FILE: vpg/bash_scripts/halfcheetah.sh ================================================ #!/bin/bash python main.py HalfCheetah-v1 --vf_type linear --seed 4 --n_iter 3000 python main.py HalfCheetah-v1 --vf_type nn --seed 4 --n_iter 3000 python main.py HalfCheetah-v1 --vf_type linear --seed 6 --n_iter 3000 python main.py HalfCheetah-v1 --vf_type nn --seed 6 --n_iter 3000 python main.py HalfCheetah-v1 --vf_type linear --seed 8 --n_iter 3000 python main.py HalfCheetah-v1 --vf_type nn --seed 8 --n_iter 3000 ================================================ FILE: vpg/bash_scripts/hopper.sh ================================================ #!/bin/bash python main.py Hopper-v1 --vf_type linear --seed 4 --n_iter 3000 python main.py Hopper-v1 --vf_type nn --seed 4 --n_iter 3000 python main.py Hopper-v1 --vf_type linear --seed 6 --n_iter 3000 python main.py Hopper-v1 --vf_type nn --seed 6 --n_iter 3000 python main.py Hopper-v1 --vf_type linear --seed 8 --n_iter 3000 python main.py Hopper-v1 --vf_type nn --seed 8 --n_iter 3000 ================================================ FILE: vpg/bash_scripts/walker.sh ================================================ #!/bin/bash python main.py Walker2d-v1 --vf_type linear --seed 4 --n_iter 3000 python main.py Walker2d-v1 --vf_type nn --seed 4 --n_iter 3000 python main.py Walker2d-v1 --vf_type linear --seed 6 --n_iter 3000 python main.py Walker2d-v1 --vf_type nn --seed 6 --n_iter 3000 python main.py Walker2d-v1 --vf_type linear --seed 8 --n_iter 3000 python main.py Walker2d-v1 --vf_type nn --seed 8 --n_iter 3000 ================================================ FILE: vpg/main.py ================================================ """ Vanilla Policy Gradients, aka REINFORCE, aka Monte Carlo Policy Gradients. To quickly test you can do: python main.py Pendulum-v0 --vf_type nn --use_kl_heuristic --do_not_save As long as --do_not_save is there, it won't overwrite files. If I want to benchmark and save results, see the bash scripts. Add --render if desired. (c) April 2017 by Daniel Seita, built upon starter code from CS 294-112. """ import argparse import gym import numpy as np np.set_printoptions(suppress=True, precision=5, edgeitems=10) import pickle import sys import tensorflow as tf if "../" not in sys.path: sys.path.append("../") from utils import utils_pg as utils from utils import value_functions as vfuncs from utils import logz from utils import policies def run_vpg(args, vf_params, logdir, env, sess, continuous_control): """ General purpose method to run vanilla policy gradients, for both continuous and discrete action environments. Parameters ---------- args: [Namespace] Contains user-provided (or default) arguments for VPGs. vf_params: [dict] Dictionary of parameters for the value function. logdir: [string] Where we store the outputs, can be None to avoid saving. env: [OpenAI gym env] The environment the agent is in, from OpenAI gym. sess: [tf Session] Current Tensorflow session, to be passed to (at least) the policy function, and the value function as well if it's a neural network. continuous_control: [boolean] True if continuous control (i.e. actions), false if otherwise. """ ob_dim = env.observation_space.shape[0] if args.vf_type == 'linear': vf = vfuncs.LinearValueFunction(**vf_params) elif args.vf_type == 'nn': vf = vfuncs.NnValueFunction(session=sess, ob_dim=ob_dim, **vf_params) if continuous_control: ac_dim = env.action_space.shape[0] policyfn = policies.GaussianPolicy(sess, ob_dim, ac_dim) else: ac_dim = env.action_space.n policyfn = policies.GibbsPolicy(sess, ob_dim, ac_dim) sess.__enter__() # equivalent to `with sess:` tf.global_variables_initializer().run() #pylint: disable=E1101 total_timesteps = 0 stepsize = args.initial_stepsize for i in range(args.n_iter): print("\n********** Iteration %i ************"%i) # Collect paths until we have enough timesteps. timesteps_this_batch = 0 paths = [] while True: ob = env.reset() terminated = False obs, acs, rewards = [], [], [] animate_this_episode = (len(paths) == 0 and (i%100 == 0) and args.render) while True: if animate_this_episode: env.render() obs.append(ob) ac = policyfn.sample_action(ob) acs.append(ac) ob, rew, done, _ = env.step(ac) rewards.append(rew) if done: break path = {"observation" : np.array(obs), "terminated" : terminated, "reward" : np.array(rewards), "action" : np.array(acs)} paths.append(path) timesteps_this_batch += utils.pathlength(path) if timesteps_this_batch > args.min_timesteps_per_batch: break total_timesteps += timesteps_this_batch # Estimate advantage function using baseline vf (these are lists!). # return_t: list of sum of discounted rewards (to end of episode), one per time # vpred_t: list of value function's predictions of components of return_t vtargs, vpreds, advs = [], [], [] for path in paths: rew_t = path["reward"] return_t = utils.discount(rew_t, args.gamma) vpred_t = vf.predict(path["observation"]) adv_t = return_t - vpred_t advs.append(adv_t) vtargs.append(return_t) vpreds.append(vpred_t) # Build arrays for policy update and **re-fit the baseline**. ob_no = np.concatenate([path["observation"] for path in paths]) ac_n = np.concatenate([path["action"] for path in paths]) adv_n = np.concatenate(advs) std_adv_n = (adv_n - adv_n.mean()) / (adv_n.std() + 1e-8) vtarg_n = np.concatenate(vtargs) vpred_n = np.concatenate(vpreds) vf.fit(ob_no, vtarg_n) # Policy update, plus diagnostics stuff. Is there a better way to handle # the continuous vs discrete control cases? if continuous_control: surr_loss, oldmean_na, oldlogstd_a = policyfn.update_policy( ob_no, ac_n, std_adv_n, stepsize) kl, ent = policyfn.kldiv_and_entropy(ob_no, oldmean_na, oldlogstd_a) else: surr_loss, oldlogits_na = policyfn.update_policy( ob_no, ac_n, std_adv_n, stepsize) kl, ent = policyfn.kldiv_and_entropy(ob_no, oldlogits_na) # A step size heuristic to ensure that we don't take too large steps. if args.use_kl_heuristic: if kl > args.desired_kl * 2: stepsize /= 1.5 print('PG stepsize -> %s' % stepsize) elif kl < args.desired_kl / 2: stepsize *= 1.5 print('PG stepsize -> %s' % stepsize) else: print('PG stepsize OK') # Log diagnostics if i % args.log_every_t_iter == 0: logz.log_tabular("EpRewMean", np.mean([path["reward"].sum() for path in paths])) logz.log_tabular("EpLenMean", np.mean([utils.pathlength(path) for path in paths])) logz.log_tabular("KLOldNew", kl) logz.log_tabular("Entropy", ent) logz.log_tabular("EVBefore", utils.explained_variance_1d(vpred_n, vtarg_n)) logz.log_tabular("EVAfter", utils.explained_variance_1d(vf.predict(ob_no), vtarg_n)) logz.log_tabular("SurrogateLoss", surr_loss) logz.log_tabular("TimestepsSoFar", total_timesteps) # If you're overfitting, EVAfter will be way larger than EVBefore. # Note that we fit the value function AFTER using it to compute the # advantage function to avoid introducing bias logz.dump_tabular() if __name__ == "__main__": p = argparse.ArgumentParser() p.add_argument('envname', type=str) p.add_argument('--render', action='store_true') p.add_argument('--do_not_save', action='store_true') p.add_argument('--use_kl_heuristic', action='store_true') p.add_argument('--n_iter', type=int, default=500) p.add_argument('--seed', type=int, default=0) p.add_argument('--gamma', type=float, default=0.97) p.add_argument('--desired_kl', type=float, default=2e-3) p.add_argument('--min_timesteps_per_batch', type=int, default=2500) p.add_argument('--initial_stepsize', type=float, default=1e-3) p.add_argument('--log_every_t_iter', type=int, default=1) p.add_argument('--vf_type', type=str, default='linear') p.add_argument('--nnvf_epochs', type=int, default=20) p.add_argument('--nnvf_ssize', type=float, default=1e-3) args = p.parse_args() # Handle value function type and the log directory (and save the args!). assert args.vf_type == 'linear' or args.vf_type == 'nn' vf_params = {} outstr = 'linearvf-kl' +str(args.desired_kl) if args.vf_type == 'nn': vf_params = dict(n_epochs=args.nnvf_epochs, stepsize=args.nnvf_ssize) outstr = 'nnvf-kl' +str(args.desired_kl) outstr += '-seed' +str(args.seed).zfill(2) logdir = 'outputs/' +args.envname+ '/' +outstr if args.do_not_save: logdir = None logz.configure_output_dir(logdir) if logdir is not None: with open(logdir+'/args.pkl', 'wb') as f: pickle.dump(args, f) print("Saving in logdir: {}".format(logdir)) # Other stuff for seeding and getting things set up. tf.set_random_seed(args.seed) np.random.seed(args.seed) env = gym.make(args.envname) continuous = True if 'discrete' in str(type(env.action_space)).lower(): # A bit of a hack, is there a better way to do this? Another option # could be following Jonathan Ho's code and detecting spaces.Box? continuous = False print("Continuous control? {}".format(continuous)) tf_config = tf.ConfigProto(inter_op_parallelism_threads=1, intra_op_parallelism_threads=1) sess = tf.Session(config=tf_config) run_vpg(args, vf_params, logdir, env, sess, continuous) ================================================ FILE: vpg/plot_learning_curves.py ================================================ """ To plot, you need to provide the experiment directory. python plot_learning_curves.py outputs/Pendulum-v0 --out figures/Pendulum-v0.png python plot_learning_curves.py outputs/Pendulum-v0 --out figures/Pendulum-v0_sm.png --smooth (Don't forget to add `sm` to the figure name!) Do this for each environment tested, e.g. with Hopper-v1 as well. Also, be careful to check the number of iterations! """ import argparse import os from os.path import join import sys import matplotlib matplotlib.use('Agg') from pylab import * import matplotlib.pyplot as plt plt.style.use('seaborn-darkgrid') # Argument parser. parser = argparse.ArgumentParser() parser.add_argument("expdir", help="experiment dir, e.g., /tmp/experiments") parser.add_argument("--out", type=str, help="full directory where to save") parser.add_argument("--niter", type=int, default=100, help="the number of iterations used") parser.add_argument('--smooth', action='store_true') args = parser.parse_args() dirnames = os.listdir(args.expdir) niter = args.niter # CAREFUL! if 'CartPole-v0' in args.expdir: niter = 100 if 'Pendulum-v0' in args.expdir: niter = 400 if ('Hopper-v1' in args.expdir) or ('Walker2d-v1' in args.expdir) or \ ('HalfCheetah-v1' in args.expdir): niter = 3000 print("dirnames:\n{}".format(dirnames)) print("niter: {}".format(niter)) # Matplotlib settings lw = 2 font = 18 fig, axes = subplots(4, figsize=(14,18)) # Try to handle the smoothed case separately. It's a bit ugly. I'm assuming I # did three different seeds, BTW. if args.smooth: n = 3 colors = ['gold', 'midnightblue'] avgx_lin = np.zeros(niter,) avgx_nn = np.zeros(niter,) avg_rew_lin = np.zeros(niter,) avg_rew_nn = np.zeros(niter,) avg_kl_lin = np.zeros(niter,) avg_kl_nn = np.zeros(niter,) avg_ent_lin = np.zeros(niter,) avg_ent_nn = np.zeros(niter,) avg_ev_lin = np.zeros(niter,) avg_ev_nn = np.zeros(niter,) for dirname in dirnames: A = np.genfromtxt(join(args.expdir, dirname, 'log.txt'),delimiter='\t',dtype=None, names=True) if 'linearvf' in dirname: avgx_lin += A['TimestepsSoFar'] avg_rew_lin += A['EpRewMean'] avg_kl_lin += A['KLOldNew'] avg_ent_lin += A['Entropy'] avg_ev_lin += A['EVBefore'] elif 'nnvf' in dirname: avgx_nn += A['TimestepsSoFar'] avg_rew_nn += A['EpRewMean'] avg_kl_nn += A['KLOldNew'] avg_ent_nn += A['Entropy'] avg_ev_nn += A['EVBefore'] avgx_lin /= n avgx_nn /= n avg_rew_lin /= n avg_rew_nn /= n avg_kl_lin /= n avg_kl_nn /= n avg_ent_lin /= n avg_ent_nn /= n avg_ev_lin /= n avg_ev_nn /= n axes[0].plot(avgx_lin, avg_rew_lin, '-', color=colors[0], lw=lw) axes[0].plot(avgx_nn, avg_rew_nn, '-', color=colors[1], lw=lw) axes[1].plot(avgx_lin, avg_kl_lin, '-', color=colors[0], lw=lw) axes[1].plot(avgx_nn, avg_kl_nn, '-', color=colors[1], lw=lw) axes[2].plot(avgx_lin, avg_ent_lin, '-', color=colors[0], lw=lw) axes[2].plot(avgx_nn, avg_ent_nn, '-', color=colors[1], lw=lw) axes[3].plot(avgx_lin, avg_ev_lin, '-', color=colors[0], lw=lw, label='Linear VF') axes[3].plot(avgx_nn, avg_ev_nn, '-', color=colors[1], lw=lw, label='NN VF') axes[3].legend(loc='best',ncol=2) else: colors = ['blue', 'red', 'yellow', 'black', 'gray', 'cyan'] for dirname, c in zip(dirnames, colors): A = np.genfromtxt(join(args.expdir, dirname, 'log.txt'),delimiter='\t',dtype=None, names=True) x = A['TimestepsSoFar'] axes[0].plot(x, A['EpRewMean'], '-', color=c, lw=lw) axes[1].plot(x, A['KLOldNew'], '-', color=c, lw=lw) axes[2].plot(x, A['Entropy'], '-', color=c, lw=lw) axes[3].plot(x, A['EVBefore'], '-', color=c, lw=lw) legend(dirnames,loc='best',ncol=2).draggable() axes[0].set_ylabel("EpRewMean", fontsize=font) axes[1].set_ylabel("KLOldNew", fontsize=font) axes[2].set_ylabel("Entropy", fontsize=font) axes[3].set_ylabel("EVBefore", fontsize=font) axes[3].set_ylim(-1,1) axes[-1].set_xlabel("Iterations", fontsize=font) plt.tight_layout() fig.savefig(args.out)