[
{
"path": "README.md",
"content": "# CS294\nhomework for CS294 Fall 2017\n"
},
{
"path": "hw1/.idea/hw1.iml",
"content": "\n\n \n \n \n \n \n \n \n \n \n \n \n"
},
{
"path": "hw1/.idea/misc.xml",
"content": "\n\n \n"
},
{
"path": "hw1/.idea/modules.xml",
"content": "\n\n \n \n \n \n \n"
},
{
"path": "hw1/.idea/workspace.xml",
"content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n int64_list\n value=\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 1516085912078\n \n \n 1516085912078\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n file://$PROJECT_DIR$/run_expert.py\n 33\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n"
},
{
"path": "hw1/BehavioralCloning.py",
"content": "import tensorflow as tf\nimport os\nimport numpy as np\nimport tqdm\nimport gym\nimport logz\nimport time\nimport math\n\nclass Config(object):\n n_features = 11\n n_classes = 3\n dropout = 0.5\n hidden_size_1 = 128\n hidden_size_2 = 256\n hidden_size_3 = 64\n batch_size = 256\n lr = 0.0005\n itera = 20\n train_itera = 20\n envname = 'Hopper-v1'\n max_steps = 1000\n\nclass NN(object):\n def add_placeholders(self):\n self.input_placeholder = tf.placeholder(tf.float32, shape=(None, Config.n_features), name=\"input\")\n self.labels_placeholder = tf.placeholder(tf.float32, shape=(None, Config.n_classes), name=\"label\")\n self.dropout_placeholder = tf.placeholder(tf.float32, name=\"drop\")\n self.is_training = tf.placeholder(tf.bool)\n\n def create_feed_dict(self, inputs_batch, labels_batch=None, dropout=1, is_training=False):\n if labels_batch is None:\n feed_dict = {self.input_placeholder: inputs_batch,\n self.dropout_placeholder: dropout, self.is_training: is_training}\n else:\n feed_dict = {self.input_placeholder: inputs_batch, self.labels_placeholder: labels_batch,\n self.dropout_placeholder: dropout, self.is_training: is_training}\n return feed_dict\n\n def add_prediction_op(self):\n self.global_step = tf.Variable(0)\n with tf.name_scope('layer1'):\n hidden1 = tf.contrib.layers.fully_connected(self.input_placeholder, num_outputs=Config.hidden_size_1,\n activation_fn=tf.nn.relu)\n with tf.name_scope('layer2'):\n hidden2 = tf.contrib.layers.fully_connected(hidden1, num_outputs=Config.hidden_size_2,\n activation_fn=tf.nn.relu)\n with tf.name_scope('layer3'):\n hidden3 = tf.contrib.layers.fully_connected(hidden2, num_outputs=Config.hidden_size_3,\n activation_fn=tf.nn.relu)\n # hidden3 = tf.nn.dropout(hidden3, self.dropout_placeholder)\n with tf.name_scope('output'):\n pred = tf.contrib.layers.fully_connected(hidden3, num_outputs=Config.n_classes,\n activation_fn=None)\n return pred\n\n def add_loss_op(self, pred):\n loss = tf.losses.mean_squared_error(predictions=pred, labels=self.labels_placeholder)\n tf.summary.scalar('loss', loss)\n return loss\n\n def add_training_op(self, loss):\n extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n with tf.control_dependencies(extra_update_ops):\n learning_rate = tf.train.exponential_decay(Config.lr, self.global_step, 1000, 0.8, staircase=True)\n train_op = tf.train.AdamOptimizer(learning_rate).minimize(loss, global_step=self.global_step)\n return train_op\n\n def train_on_batch(self, sess, inputs_batch, labels_batch, merged, train_writer, i):\n feed = self.create_feed_dict(inputs_batch, labels_batch, self.config.dropout, True)\n rs, _, loss = sess.run([merged, self.train_op, self.loss], feed_dict=feed)\n train_writer.add_summary(rs, i)\n return loss\n\n def __init__(self, config):\n self.config = config\n self.build()\n\n def fit(self, sess, train_x, train_y):\n loss = self.train_on_batch(sess, train_x, train_y)\n\n def build(self):\n with tf.name_scope('inputs'):\n self.add_placeholders()\n with tf.name_scope('predict'):\n self.pred = self.add_prediction_op()\n with tf.name_scope('loss'):\n self.loss = self.add_loss_op(self.pred)\n with tf.name_scope('train'):\n self.train_op = self.add_training_op(self.loss)\n\n def get_pred(self, sess, inputs_batch):\n feed = self.create_feed_dict(inputs_batch, dropout=1, is_training=False)\n p = sess.run(self.pred, feed_dict=feed)\n return p\n\ndef load(path):\n all = np.load(path)\n X = all[\"arr_0\"]\n y = all[\"arr_1\"]\n y1 = y.reshape(y.shape[0], y.shape[2])\n return X, y1\n\n\ndef main():\n PROJECT_ROOT = os.path.dirname(os.path.realpath(__file__))\n train_path = os.path.join(PROJECT_ROOT, \"data/\"+Config.envname+\".train.npz\")\n train_log_path = os.path.join(PROJECT_ROOT, \"log/train/\")\n logz.configure_output_dir(os.path.join(PROJECT_ROOT, \"log/\"+Config.envname+\"_BC_\"+time.strftime(\"%d-%m-%Y_%H-%M-%S\")))\n\n X_train, y_train = load(train_path)#debug\n\n print(\"train size :\", X_train.shape, y_train.shape)\n print(\"start training\")\n\n with tf.Graph().as_default():\n config = Config()\n nn = NN(config)\n init = tf.global_variables_initializer()\n saver = tf.train.Saver(max_to_keep=10, keep_checkpoint_every_n_hours=0.5)\n #必须在session外面\n shuffle_batch_x, shuffle_batch_y = tf.train.shuffle_batch(\n [X_train, y_train], batch_size=Config.batch_size, capacity=10000,\n min_after_dequeue=5000, enqueue_many=True)\n\n with tf.Session() as session:\n merged = tf.summary.merge_all()\n train_writer = tf.summary.FileWriter(train_log_path, session.graph)\n session.run(init)\n coord = tf.train.Coordinator()\n threads = tf.train.start_queue_runners(session, coord)\n\n for j in tqdm.tqdm(range(Config.itera)):\n i = 0\n try:\n for i in range(int(math.ceil(Config.train_itera * X_train.shape[0] / Config.batch_size))):\n batch_x, batch_y = session.run([shuffle_batch_x, shuffle_batch_y])\n loss = nn.train_on_batch(session, batch_x, batch_y, merged, train_writer, i)\n i += 1\n if i % 1000 == 0:\n print(\"step:\", i, \"loss:\", loss)\n saver.save(session, os.path.join(PROJECT_ROOT, \"model/model_ckpt\"), global_step=i)\n\n except tf.errors.OutOfRangeError:\n print(\"\")\n finally:\n coord.request_stop()\n coord.join(threads)\n\n env = gym.make(Config.envname)\n rollouts = 20\n returns = []\n for _ in range(rollouts):\n obs = env.reset()\n done = False\n totalr = 0.\n steps = 0\n while not done:\n action = nn.get_pred(session, obs[None, :])\n obs, r, done, _ = env.step(action)\n totalr += r\n steps += 1\n # if args.render:\n # env.render()\n if steps >= Config.max_steps:\n break\n returns.append(totalr)\n\n # print('results for ', Config.envname)\n # print('returns', returns)\n # print('mean return', np.mean(returns))\n # print('std of return', np.std(returns))\n logz.log_tabular('Iteration', j)\n logz.log_tabular('AverageReturn', np.mean(returns))\n logz.log_tabular('StdReturn', np.std(returns))\n logz.dump_tabular()\n\n\n\n\nif __name__ == '__main__':\n main()"
},
{
"path": "hw1/DAgger.py",
"content": "import tensorflow as tf\nimport os\nimport numpy as np\nimport tqdm\nimport gym\nimport load_policy\nimport math\nimport logz\nimport time\n\nclass Config(object):\n n_features = 17\n n_classes = 6\n dropout = 0.5\n hidden_size_1 = 128\n hidden_size_2 = 256\n hidden_size_3 = 64\n batch_size = 256\n lr = 0.0005\n itera = 20\n train_itera = 20\n envname = 'Walker2d-v1'\n max_steps = 1000\n\nclass NN(object):\n def add_placeholders(self):\n self.input_placeholder = tf.placeholder(tf.float32, shape=(None, Config.n_features), name=\"input\")\n self.labels_placeholder = tf.placeholder(tf.float32, shape=(None, Config.n_classes), name=\"label\")\n self.dropout_placeholder = tf.placeholder(tf.float32, name=\"drop\")\n self.is_training = tf.placeholder(tf.bool)\n\n def create_feed_dict(self, inputs_batch, labels_batch=None, dropout=1, is_training=False):\n if labels_batch is None:\n feed_dict = {self.input_placeholder: inputs_batch,\n self.dropout_placeholder: dropout, self.is_training: is_training}\n else:\n feed_dict = {self.input_placeholder: inputs_batch, self.labels_placeholder: labels_batch,\n self.dropout_placeholder: dropout, self.is_training: is_training}\n return feed_dict\n\n def add_prediction_op(self):\n self.global_step = tf.Variable(0)\n with tf.name_scope('layer1'):\n hidden1 = tf.contrib.layers.fully_connected(self.input_placeholder, num_outputs=Config.hidden_size_1,\n activation_fn=tf.nn.relu)\n with tf.name_scope('layer2'):\n hidden2 = tf.contrib.layers.fully_connected(hidden1, num_outputs=Config.hidden_size_2,\n activation_fn=tf.nn.relu)\n with tf.name_scope('layer3'):\n hidden3 = tf.contrib.layers.fully_connected(hidden2, num_outputs=Config.hidden_size_3,\n activation_fn=tf.nn.relu)\n with tf.name_scope('output'):\n pred = tf.contrib.layers.fully_connected(hidden3, num_outputs=Config.n_classes,\n activation_fn=None)\n return pred\n\n def add_loss_op(self, pred):\n loss = tf.losses.mean_squared_error(predictions=pred, labels=self.labels_placeholder)\n tf.summary.scalar('loss', loss)\n return loss\n\n def add_training_op(self, loss):\n extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n with tf.control_dependencies(extra_update_ops):\n learning_rate = tf.train.exponential_decay(Config.lr, self.global_step, 1000, 0.8, staircase=True)\n train_op = tf.train.AdamOptimizer(learning_rate).minimize(loss, global_step=self.global_step)\n return train_op\n\n def train_on_batch(self, sess, inputs_batch, labels_batch, merged, train_writer, i):\n feed = self.create_feed_dict(inputs_batch, labels_batch, self.config.dropout, True)\n rs, _, loss = sess.run([merged, self.train_op, self.loss], feed_dict=feed)\n train_writer.add_summary(rs, i)\n return loss\n\n def __init__(self, config):\n self.config = config\n self.build()\n\n def fit(self, sess, train_x, train_y):\n loss = self.train_on_batch(sess, train_x, train_y)\n\n def build(self):\n with tf.name_scope('inputs'):\n self.add_placeholders()\n with tf.name_scope('predict'):\n self.pred = self.add_prediction_op()\n with tf.name_scope('loss'):\n self.loss = self.add_loss_op(self.pred)\n with tf.name_scope('train'):\n self.train_op = self.add_training_op(self.loss)\n\n def get_pred(self, sess, inputs_batch):\n feed = self.create_feed_dict(inputs_batch, dropout=1, is_training=False)\n p = sess.run(self.pred, feed_dict=feed)\n return p\n\ndef load(path):\n all = np.load(path)\n X = all[\"arr_0\"]\n y = all[\"arr_1\"]\n y1 = y.reshape(y.shape[0], y.shape[2])\n return X, y1\n\ndef run_env(env, nn,session):\n obs = env.reset()\n done = False\n totalr = 0.\n steps = 0\n observations = []\n while not done:\n action = nn.get_pred(session, obs[None, :])\n observations.append(obs)\n obs, r, done, _ = env.step(action)\n totalr += r\n steps += 1\n # if args.render:\n # env.render()\n if steps >= Config.max_steps:\n break\n return totalr, observations\n\ndef shuffle(X_train, y_train):\n training_data = np.concatenate((X_train, y_train), axis=1)\n np.random.shuffle(training_data)\n X = training_data[:, :-Config.n_classes]\n y = training_data[:, -Config.n_classes:]\n return X, y\n\n\ndef main():\n PROJECT_ROOT = os.path.dirname(os.path.realpath(__file__))\n train_path = os.path.join(PROJECT_ROOT, \"data/\"+Config.envname+\".train.npz\")\n policy_path = os.path.join(PROJECT_ROOT, \"experts/\"+Config.envname+\".pkl\")\n train_log_path = os.path.join(PROJECT_ROOT, \"log/train/\")\n logz.configure_output_dir(os.path.join(PROJECT_ROOT, \"log/\"+Config.envname+\"_DA_\"+time.strftime(\"%d-%m-%Y_%H-%M-%S\")))\n\n X_train, y_train = load(train_path)#debug\n\n print(\"train size :\", X_train.shape, y_train.shape)\n print(\"start training\")\n\n with tf.Graph().as_default():\n config = Config()\n nn = NN(config)\n init = tf.global_variables_initializer()\n saver = tf.train.Saver(max_to_keep=10, keep_checkpoint_every_n_hours=0.5)\n\n print('loading and building expert policy')\n policy_fn = load_policy.load_policy(policy_path)\n print('loaded and built')\n\n with tf.Session() as session:\n merged = tf.summary.merge_all()\n train_writer = tf.summary.FileWriter(train_log_path, session.graph)\n session.run(init)\n coord = tf.train.Coordinator()\n threads = tf.train.start_queue_runners(session, coord)\n\n #iter\n for j in tqdm.tqdm(range(Config.itera)):\n #train\n X_train, y_train = shuffle(X_train, y_train)\n i = 0\n try:\n for i in range(int(math.ceil(Config.train_itera * X_train.shape[0] / Config.batch_size))):\n offset = (i * Config.batch_size) % X_train.shape[0]\n # shuffle\n batch_x = X_train[offset:(offset + Config.batch_size), :]\n batch_y = y_train[offset:(offset + Config.batch_size)]\n loss = nn.train_on_batch(session, batch_x, batch_y, merged, train_writer, i)\n i += 1\n print(\"step:\", i, \"loss:\", loss)\n # saver.save(session, os.path.join(PROJECT_ROOT, \"model/model_ckpt\"), global_step=i)\n except tf.errors.OutOfRangeError:\n print(\"done\")\n finally:\n coord.request_stop()\n coord.join(threads)\n\n #get new data and label\n observations = []\n actions = []\n env = gym.make(Config.envname)\n for _ in range(10):\n _, o = run_env(env, nn, session)\n observations.extend(o)\n action = policy_fn(o)\n actions.extend(action)\n\n new_x = np.array(observations)\n new_y = np.array(actions)\n X_train = np.concatenate((X_train, new_x))\n y_train = np.concatenate((y_train, new_y))\n print(\"train size :\", X_train.shape, y_train.shape)\n\n #test\n # print(\"iter:\", j, \" train finished\")\n # print(Config.envname + \" start\")\n\n rollouts = 20\n returns = []\n for _ in range(rollouts):\n totalr, _ = run_env(env, nn, session)\n returns.append(totalr)\n\n # print('results for ', Config.envname)\n # print('returns', returns)\n # print('mean return', np.mean(returns), 'std of return', np.std(returns))\n # print('mean return', np.mean(returns))\n print()\n\n logz.log_tabular('Iteration', j)\n logz.log_tabular('AverageReturn', np.mean(returns))\n logz.log_tabular('StdReturn', np.std(returns))\n logz.dump_tabular()\n\n\nif __name__ == '__main__':\n main()"
},
{
"path": "hw1/README.md",
"content": "# CS294-112 HW 1: Imitation Learning\n\nDependencies: TensorFlow, MuJoCo version 1.31, OpenAI Gym\n\n**Note**: MuJoCo versions until 1.5 do not support NVMe disks therefore won't be compatible with recent Mac machines.\nThere is a request for OpenAI to support it that can be followed [here](https://github.com/openai/gym/issues/638).\n\nThe 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.\n\nIn `experts/`, the provided expert policies are:\n* Ant-v1.pkl\n* HalfCheetah-v1.pkl\n* Hopper-v1.pkl\n* Humanoid-v1.pkl\n* Reacher-v1.pkl\n* Walker2d-v1.pkl\n\nThe name of the pickle file corresponds to the name of the gym environment.\n"
},
{
"path": "hw1/demo.bash",
"content": "#!/bin/bash\nset -eux\nfor e in Hopper-v1 Ant-v1 HalfCheetah-v1 Humanoid-v1 Reacher-v1 Walker2d-v1\ndo\n python run_expert.py experts/$e.pkl $e --render --num_rollouts=1\ndone\n"
},
{
"path": "hw1/load_policy.py",
"content": "import pickle, tensorflow as tf, tf_util, numpy as np\n\ndef load_policy(filename):\n with open(filename, 'rb') as f:\n data = pickle.loads(f.read())\n\n # assert len(data.keys()) == 2\n nonlin_type = data['nonlin_type']\n policy_type = [k for k in data.keys() if k != 'nonlin_type'][0]\n\n assert policy_type == 'GaussianPolicy', 'Policy type {} not supported'.format(policy_type)\n policy_params = data[policy_type]\n\n assert set(policy_params.keys()) == {'logstdevs_1_Da', 'hidden', 'obsnorm', 'out'}\n\n # Keep track of input and output dims (i.e. observation and action dims) for the user\n\n def build_policy(obs_bo):\n def read_layer(l):\n assert list(l.keys()) == ['AffineLayer']\n assert sorted(l['AffineLayer'].keys()) == ['W', 'b']\n return l['AffineLayer']['W'].astype(np.float32), l['AffineLayer']['b'].astype(np.float32)\n\n def apply_nonlin(x):\n if nonlin_type == 'lrelu':\n return tf_util.lrelu(x, leak=.01) # openai/imitation nn.py:233\n elif nonlin_type == 'tanh':\n return tf.tanh(x)\n else:\n raise NotImplementedError(nonlin_type)\n\n # Build the policy. First, observation normalization.\n assert list(policy_params['obsnorm'].keys()) == ['Standardizer']\n obsnorm_mean = policy_params['obsnorm']['Standardizer']['mean_1_D']\n obsnorm_meansq = policy_params['obsnorm']['Standardizer']['meansq_1_D']\n obsnorm_stdev = np.sqrt(np.maximum(0, obsnorm_meansq - np.square(obsnorm_mean)))\n print('obs', obsnorm_mean.shape, obsnorm_stdev.shape)\n normedobs_bo = (obs_bo - obsnorm_mean) / (obsnorm_stdev + 1e-6) # 1e-6 constant from Standardizer class in nn.py:409 in openai/imitation\n\n curr_activations_bd = normedobs_bo\n\n # Hidden layers next\n assert list(policy_params['hidden'].keys()) == ['FeedforwardNet']\n layer_params = policy_params['hidden']['FeedforwardNet']\n for layer_name in sorted(layer_params.keys()):\n l = layer_params[layer_name]\n W, b = read_layer(l)\n curr_activations_bd = apply_nonlin(tf.matmul(curr_activations_bd, W) + b)\n\n # Output layer\n W, b = read_layer(policy_params['out'])\n output_bo = tf.matmul(curr_activations_bd, W) + b\n return output_bo\n\n obs_bo = tf.placeholder(tf.float32, [None, None])\n a_ba = build_policy(obs_bo)\n policy_fn = tf_util.function([obs_bo], a_ba)\n return policy_fn"
},
{
"path": "hw1/log/Ant-v1_BC_30-01-2018_10-32-45/log.txt",
"content": "Iteration\tAverageReturn\tStdReturn\n0\t4460.34148867\t504.993919545\n1\t4361.62089192\t787.910341533\n2\t4238.35328327\t1021.28077134\n3\t4551.3024466\t220.243030137\n4\t4587.54091281\t168.335418179\n5\t4536.1299135\t153.39311344\n6\t4571.65449705\t123.782802078\n7\t4468.68140776\t489.42171743\n8\t4363.04400464\t670.930898282\n9\t4477.84996454\t448.159752304\n10\t4428.80299023\t509.894288663\n11\t4219.75072886\t982.726078559\n12\t4315.68265715\t819.480876493\n13\t4549.6823578\t116.079270449\n14\t4108.95615517\t1174.93913987\n15\t4215.44535307\t936.071898708\n16\t4431.84062818\t476.960091374\n17\t4586.95139781\t110.834919291\n18\t4431.21646369\t610.424550643\n19\t4520.70269831\t347.196475846\n"
},
{
"path": "hw1/log/Ant-v1_DA_30-01-2018_10-51-03/log.txt",
"content": "Iteration\tAverageReturn\tStdReturn\n0\t4543.92556297\t163.700815617\n1\t4532.86754019\t651.078836035\n2\t4718.40341227\t334.079528476\n3\t4804.59842376\t93.0563181661\n4\t4785.65700683\t127.465976282\n5\t4831.53888416\t93.6407853724\n6\t4767.63356673\t112.208596337\n7\t4801.27090284\t91.1866352943\n8\t4601.36647927\t951.83089995\n9\t4787.38593852\t275.842000254\n10\t4664.15443565\t710.401178585\n11\t4804.25359337\t113.174008233\n12\t4806.84341705\t88.881374471\n13\t4772.95084954\t103.853744593\n14\t4646.54370371\t603.505425269\n15\t4604.3418062\t770.681770359\n16\t4809.23885621\t100.484625256\n17\t4703.23717096\t418.746724941\n18\t4790.78266831\t113.777606254\n19\t4783.07195431\t108.151801332\n"
},
{
"path": "hw1/log/HalfCheetah-v1_BC_30-01-2018_10-51-19/log.txt",
"content": "Iteration\tAverageReturn\tStdReturn\n0\t3817.96495454\t121.424448089\n1\t3936.04442016\t149.995208225\n2\t3852.30571246\t120.093945537\n3\t3906.3257817\t137.039474081\n4\t3927.96557883\t92.3172335412\n5\t3905.73231232\t132.250735059\n6\t3894.78388609\t104.606664587\n7\t3927.04549651\t108.76612839\n8\t3891.86357872\t120.74589099\n9\t3857.56540305\t135.371682439\n10\t3876.91409242\t118.576842405\n11\t3907.41133635\t109.120138546\n12\t3877.17466\t141.174229105\n13\t3875.88491827\t149.663161746\n14\t3893.42180931\t125.035365858\n15\t3927.75757089\t114.108252154\n16\t3901.79379918\t131.083160122\n17\t3884.49668982\t145.973126754\n18\t3926.50703736\t107.673754351\n19\t3902.56928807\t121.983220478\n"
},
{
"path": "hw1/log/HalfCheetah-v1_DA_30-01-2018_11-11-33/log.txt",
"content": "Iteration\tAverageReturn\tStdReturn\n0\t3976.17700506\t114.034047864\n1\t4080.46221353\t84.1893455539\n2\t4133.95250959\t81.0167961747\n3\t4101.48136021\t94.9826005054\n4\t4135.3255688\t80.7781973991\n5\t4140.00386975\t97.4145974268\n6\t4127.59048502\t98.0957605992\n7\t4136.09651306\t87.8447357049\n8\t4168.61448977\t58.1743328113\n9\t4175.37724354\t76.5500116081\n10\t4152.01156376\t100.515577257\n11\t4121.61130162\t86.6234243495\n12\t4134.83157964\t109.120217262\n13\t4128.08832282\t70.0049383219\n14\t4142.06574835\t99.4376440103\n15\t4145.22541853\t68.6286481839\n16\t4164.73279931\t89.1040051378\n17\t4096.95377812\t90.370097307\n18\t4148.86261807\t88.9318617818\n19\t4160.6740329\t50.5316630273\n"
},
{
"path": "hw1/log/Hopper-v1_BC_30-01-2018_10-55-39/log.txt",
"content": "Iteration\tAverageReturn\tStdReturn\n0\t696.648776946\t27.07925021\n1\t748.47888752\t151.429815664\n2\t700.073941219\t72.9177435482\n3\t731.887652211\t113.92031914\n4\t715.92036383\t112.632748448\n5\t702.746427086\t72.807579689\n6\t737.028510731\t118.896270982\n7\t753.175757248\t123.58988432\n8\t728.528427466\t88.1725040827\n9\t767.419942833\t135.862982943\n10\t704.908491269\t37.7345201986\n11\t820.057900984\t162.74570912\n12\t722.067580433\t95.4241626287\n13\t710.949850348\t96.7033252778\n14\t721.164026913\t74.5446602988\n15\t748.766636126\t126.705015019\n16\t732.385777969\t99.6523391183\n17\t732.09846089\t121.161806134\n18\t754.424397187\t137.548581585\n19\t718.219629462\t97.6934148631\n"
},
{
"path": "hw1/log/Hopper-v1_BCbig_30-01-2018_11-02-29/log.txt",
"content": "Iteration\tAverageReturn\tStdReturn\n0\t1138.58195909\t332.371517359\n1\t896.423291123\t254.391048933\n2\t1151.49985307\t695.596874896\n3\t933.454536471\t275.828983102\n4\t1060.26470964\t711.936395806\n5\t943.419330413\t322.135059245\n6\t914.833224501\t272.414134066\n7\t999.866949869\t522.351021601\n8\t1061.79869625\t347.475856152\n9\t1000.63941619\t386.03117231\n10\t975.857956381\t353.259897077\n11\t862.772266804\t337.564889514\n12\t997.908818987\t339.37936976\n13\t1161.14782133\t389.451785869\n14\t994.682731442\t313.7296743\n15\t1108.21639631\t427.538386532\n16\t1014.65476481\t321.52762664\n17\t1051.94580151\t372.616078853\n18\t1072.21491361\t383.494569749\n19\t1168.59860878\t531.649394659\n"
},
{
"path": "hw1/log/Hopper-v1_DA_30-01-2018_11-24-58/log.txt",
"content": "Iteration\tAverageReturn\tStdReturn\n0\t1054.7477548\t44.5027933049\n1\t2294.09469315\t467.288023567\n2\t3707.44200499\t260.030021057\n3\t3172.08454655\t856.691867273\n4\t3778.9154217\t4.68171457329\n5\t3775.94642069\t2.83087791895\n6\t3776.23605724\t3.29135654141\n7\t3776.0847846\t4.88839549559\n8\t3775.58082393\t3.35472305027\n9\t3776.7114718\t3.99064616387\n10\t3775.31472267\t2.87022549201\n11\t3776.24774355\t2.89276807641\n12\t3775.22554377\t3.05415386535\n13\t3775.01634936\t4.56489670726\n14\t3777.48680881\t3.09514046612\n15\t3776.23465168\t4.09691179256\n16\t3777.64782077\t4.50033528355\n17\t3775.7392206\t3.79610240281\n18\t3777.38831693\t3.00064812486\n19\t3775.72011362\t4.24151812416\n"
},
{
"path": "hw1/log/Humanoid-v1_BC_30-01-2018_10-28-53/log.txt",
"content": "Iteration\tAverageReturn\tStdReturn\n0\t295.814730365\t88.5813632879\n1\t238.349607493\t76.3277190139\n2\t243.129245237\t81.1495698412\n3\t259.916241087\t57.306794984\n4\t247.425765644\t56.0124802139\n5\t241.914222088\t46.0838442162\n6\t253.931450644\t63.2003431654\n7\t251.555778685\t78.5263630011\n8\t268.208717827\t64.7656412232\n9\t247.556079083\t72.1923660942\n10\t251.774811937\t69.0744558299\n11\t254.696568892\t45.9480436897\n12\t253.242338361\t76.8047371933\n13\t228.623935494\t59.7224981115\n14\t229.862209508\t57.370454754\n15\t229.956419335\t55.6306188663\n16\t234.963972145\t54.0542848773\n17\t265.384523529\t68.401751621\n18\t254.279404448\t82.3064869712\n19\t232.650468408\t63.4344756441\n"
},
{
"path": "hw1/log/Humanoid-v1_DA_30-01-2018_10-31-26/log.txt",
"content": "Iteration\tAverageReturn\tStdReturn\n0\t277.763885265\t74.5699695095\n1\t340.013799005\t83.8174345466\n2\t281.151168831\t58.8467520166\n3\t266.38751159\t29.5122759836\n4\t273.078970953\t22.275127559\n5\t245.575394751\t18.4615901498\n6\t328.153923421\t40.2691355568\n7\t388.722635173\t78.4953628383\n8\t398.844472211\t117.74492814\n9\t474.793752092\t205.53054157\n10\t423.280078799\t99.1227466344\n11\t500.104497995\t187.592754877\n12\t454.008649777\t133.582121407\n13\t508.123738555\t171.632223784\n14\t464.06309332\t136.519517931\n15\t505.570051114\t158.128001299\n16\t537.379418303\t162.645475863\n17\t496.810935012\t105.635839043\n18\t539.956389194\t170.934547794\n19\t500.060948891\t133.550493957\n"
},
{
"path": "hw1/log/Reacher-v1_BC_30-01-2018_10-57-25/log.txt",
"content": "Iteration\tAverageReturn\tStdReturn\n0\t-9.14313732169\t2.1057737525\n1\t-10.2464604937\t2.91303780186\n2\t-9.36505170342\t2.89134055057\n3\t-9.6073462012\t2.38382175938\n4\t-9.0086068586\t2.22443507996\n5\t-9.14235838387\t1.41554386753\n6\t-9.32533608551\t1.92161010184\n7\t-9.52716548162\t2.15173210654\n8\t-9.18721318721\t2.43739650362\n9\t-8.76734130122\t1.87788078057\n10\t-9.31907915487\t2.54378382632\n11\t-8.70922062576\t1.71550860527\n12\t-9.42346594896\t2.18220439629\n13\t-8.48235034653\t1.9664693231\n14\t-9.52440599013\t2.67361159834\n15\t-10.0461903527\t2.14692809316\n16\t-10.4576060332\t2.28085450116\n17\t-9.40975591137\t2.4899395935\n18\t-9.7550098025\t2.27262128127\n19\t-8.75621744838\t1.7181797476\n"
},
{
"path": "hw1/log/Reacher-v1_DA_30-01-2018_11-27-44/log.txt",
"content": "Iteration\tAverageReturn\tStdReturn\n0\t-9.514077968\t4.67060656967\n1\t-6.8483366701\t2.22806332751\n2\t-7.28919411373\t3.00020247685\n3\t-5.14896746284\t1.78107674457\n4\t-4.61655540225\t1.47905228634\n5\t-4.17923458664\t1.21148217865\n6\t-4.23271202296\t1.57480039431\n7\t-4.30797880136\t1.56205175651\n8\t-3.86158286292\t1.32237728902\n9\t-4.11994541821\t1.39324099865\n10\t-4.33389428086\t1.80342014308\n11\t-4.66312498685\t1.19078597682\n12\t-3.88081879349\t1.56953718566\n13\t-4.16823813172\t1.25349473697\n14\t-3.78993609335\t1.38466284504\n15\t-4.19153633158\t1.7151942544\n16\t-3.35060677979\t1.29864476833\n17\t-4.76815479509\t1.75597118833\n18\t-4.48480997587\t1.46280164822\n19\t-3.52572072065\t1.45917975484\n"
},
{
"path": "hw1/log/Walker2d-v1_BC_30-01-2018_10-58-02/log.txt",
"content": "Iteration\tAverageReturn\tStdReturn\n0\t731.896679135\t356.841079416\n1\t245.597115443\t370.464617925\n2\t517.360851842\t524.222617\n3\t375.242482521\t396.621897565\n4\t586.719647307\t560.506067304\n5\t452.02708465\t572.73838478\n6\t315.448568014\t432.609821525\n7\t605.402818104\t488.550645007\n8\t399.929967675\t379.255007212\n9\t662.501627219\t561.574809086\n10\t464.163909044\t642.583052804\n11\t572.401418705\t554.032101995\n12\t446.562993296\t583.800058368\n13\t408.118004614\t472.033537121\n14\t341.961541246\t468.061476134\n15\t537.260068744\t668.650000523\n16\t455.874748994\t496.489902983\n17\t403.340101964\t466.662009166\n18\t391.087910426\t636.457336138\n19\t596.027603618\t501.401229289\n"
},
{
"path": "hw1/log/Walker2d-v1_DA_30-01-2018_11-28-50/log.txt",
"content": "Iteration\tAverageReturn\tStdReturn\n0\t672.000153601\t623.031121462\n1\t1867.64727184\t877.010515332\n2\t5434.69323044\t45.0795878762\n3\t5421.01186299\t47.2655173625\n4\t5454.79401462\t90.7731058749\n5\t5453.22811906\t123.152769513\n6\t5460.86284453\t63.7242425634\n7\t5473.87194235\t65.0663030077\n8\t5469.63486304\t67.2099162387\n9\t5484.99459822\t44.1331706997\n10\t5282.960857\t866.813632308\n11\t5460.96627823\t80.2270775643\n12\t5469.98407369\t63.2927678597\n13\t5472.67758044\t53.748518416\n14\t5283.16625756\t864.037643147\n15\t5480.75283479\t61.9244106372\n16\t5388.20256771\t512.71497588\n17\t5085.95568353\t984.286351731\n18\t5496.42654867\t51.7899718995\n19\t5285.86858374\t857.049898125\n"
},
{
"path": "hw1/logz.py",
"content": "import json\n\n\"\"\"\n\nSome simple logging functionality, inspired by rllab's logging.\nAssumes that each diagnostic gets logged each iteration\n\nCall logz.configure_output_dir() to start logging to a \ntab-separated-values file (some_folder_name/log.txt)\n\nTo load the learning curves, you can do, for example\n\nA = np.genfromtxt('/tmp/expt_1468984536/log.txt',delimiter='\\t',dtype=None, names=True)\nA['EpRewMean']\n\n\"\"\"\n\nimport os.path as osp, shutil, time, atexit, os, subprocess\nimport pickle\nimport tensorflow as tf\n\ncolor2num = dict(\n gray=30,\n red=31,\n green=32,\n yellow=33,\n blue=34,\n magenta=35,\n cyan=36,\n white=37,\n crimson=38\n)\n\ndef colorize(string, color, bold=False, highlight=False):\n attr = []\n num = color2num[color]\n if highlight: num += 10\n attr.append(str(num))\n if bold: attr.append('1')\n return '\\x1b[%sm%s\\x1b[0m' % (';'.join(attr), string)\n\nclass G:\n output_dir = None\n output_file = None\n first_row = True\n log_headers = []\n log_current_row = {}\n\ndef configure_output_dir(d=None):\n \"\"\"\n Set output directory to d, or to /tmp/somerandomnumber if d is None\n \"\"\"\n G.output_dir = d or \"/tmp/experiments/%i\"%int(time.time())\n if osp.exists(G.output_dir):\n print(\"Log dir %s already exists! Delete it first or use a different dir\"%G.output_dir)\n else:\n os.makedirs(G.output_dir)\n G.output_file = open(osp.join(G.output_dir, \"log.txt\"), 'w')\n atexit.register(G.output_file.close)\n print(colorize(\"Logging data to %s\"%G.output_file.name, 'green', bold=True))\n\ndef log_tabular(key, val):\n \"\"\"\n Log a value of some diagnostic\n Call this once for each diagnostic quantity, each iteration\n \"\"\"\n if G.first_row:\n G.log_headers.append(key)\n else:\n assert key in G.log_headers, \"Trying to introduce a new key %s that you didn't include in the first iteration\"%key\n assert key not in G.log_current_row, \"You already set %s this iteration. Maybe you forgot to call dump_tabular()\"%key\n G.log_current_row[key] = val\n\ndef save_params(params):\n with open(osp.join(G.output_dir, \"params.json\"), 'w') as out:\n out.write(json.dumps(params, separators=(',\\n','\\t:\\t'), sort_keys=True))\n\ndef pickle_tf_vars(): \n \"\"\"\n Saves tensorflow variables\n Requires them to be initialized first, also a default session must exist\n \"\"\"\n _dict = {v.name : v.eval() for v in tf.global_variables()}\n with open(osp.join(G.output_dir, \"vars.pkl\"), 'wb') as f:\n pickle.dump(_dict, f)\n \n\ndef dump_tabular():\n \"\"\"\n Write all of the diagnostics from the current iteration\n \"\"\"\n vals = []\n key_lens = [len(key) for key in G.log_headers]\n max_key_len = max(15,max(key_lens))\n keystr = '%'+'%d'%max_key_len\n fmt = \"| \" + keystr + \"s | %15s |\"\n n_slashes = 22 + max_key_len\n print(\"-\"*n_slashes)\n for key in G.log_headers:\n val = G.log_current_row.get(key, \"\")\n if hasattr(val, \"__float__\"): valstr = \"%8.3g\"%val\n else: valstr = val\n print(fmt%(key, valstr))\n vals.append(val)\n print(\"-\"*n_slashes)\n if G.output_file is not None:\n if G.first_row:\n G.output_file.write(\"\\t\".join(G.log_headers))\n G.output_file.write(\"\\n\")\n G.output_file.write(\"\\t\".join(map(str,vals)))\n G.output_file.write(\"\\n\")\n G.output_file.flush()\n G.log_current_row.clear()\n G.first_row=False\n"
},
{
"path": "hw1/plot.py",
"content": "import seaborn as sns\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport json\nimport os\n\n\"\"\"\nUsing the plotter:\n\nCall it from the command line, and supply it with logdirs to experiments.\nSuppose you ran an experiment with name 'test', and you ran 'test' for 10 \nrandom seeds. The runner code stored it in the directory structure\n\n data\n L test_EnvName_DateTime\n L 0\n L log.txt\n L params.json\n L 1\n L log.txt\n L params.json\n .\n .\n .\n L 9\n L log.txt\n L params.json\n\nTo plot learning curves from the experiment, averaged over all random\nseeds, call\n\n python plot.py data/test_EnvName_DateTime --value AverageReturn\n\nand voila. To see a different statistics, change what you put in for\nthe keyword --value. You can also enter /multiple/ values, and it will \nmake all of them in order.\n\n\nSuppose you ran two experiments: 'test1' and 'test2'. In 'test2' you tried\na different set of hyperparameters from 'test1', and now you would like \nto compare them -- see their learning curves side-by-side. Just call\n\n python plot.py data/test1 data/test2\n\nand it will plot them both! They will be given titles in the legend according\nto their exp_name parameters. If you want to use custom legend titles, use\nthe --legend flag and then provide a title for each logdir.\n\n\"\"\"\n\ndef plot_data(data, value=\"AverageReturn\"):\n if isinstance(data, list):\n data = pd.concat(data, ignore_index=True)\n sns.set(style=\"darkgrid\", font_scale=1.5)\n sns.tsplot(data=data, time=\"Iteration\", value=value, unit=\"Unit\", condition=\"Condition\")\n plt.legend(loc='best').draggable()\n plt.show()\n\n\ndef get_datasets(fpath, condition=None):\n unit = 0\n flag = \"\"\n datasets = []\n for root, dir, files in os.walk(fpath):\n if 'log.txt' in files:\n # param_path = open(os.path.join(root,'params.json'))\n # params = json.load(param_path)\n # exp_name = params['exp_name']\n paths = fpath.split(\"_\")\n exp_name = paths[0].split(\"/\")[1]+\"_\"+paths[1]\n log_path = os.path.join(root,'log.txt')\n experiment_data = pd.read_table(log_path)\n\n experiment_data.insert(\n len(experiment_data.columns),\n 'Unit',\n unit\n )\n experiment_data.insert(\n len(experiment_data.columns),\n 'Condition',\n condition or exp_name\n )\n\n datasets.append(experiment_data)\n unit += 1\n\n return datasets\n\n\ndef main():\n import argparse\n parser = argparse.ArgumentParser()\n parser.add_argument('logdir', nargs='*')\n parser.add_argument('--legend', nargs='*')\n parser.add_argument('--value', default='AverageReturn', nargs='*')\n args = parser.parse_args()\n\n use_legend = False\n if args.legend is not None:\n assert len(args.legend) == len(args.logdir), \\\n \"Must give a legend title for each set of experiments.\"\n use_legend = True\n\n data = []\n if use_legend:\n for logdir, legend_title in zip(args.logdir, args.legend):\n data += get_datasets(logdir, legend_title)\n else:\n for logdir in args.logdir:\n data += get_datasets(logdir)\n\n if isinstance(args.value, list):\n values = args.value\n else:\n values = [args.value]\n for value in values:\n plot_data(data, value=value)\n\nif __name__ == \"__main__\":\n main()\n"
},
{
"path": "hw1/run_expert.py",
"content": "#!/usr/bin/env python\n\n\"\"\"\nCode to load an expert policy and generate roll-out data for behavioral cloning.\nExample usage:\n python run_expert.py experts/Humanoid-v1.pkl Humanoid-v1 --render \\\n --num_rollouts 20\n\nAuthor of this script and included expert policies: Jonathan Ho (hoj@openai.com)\n\"\"\"\n\nimport pickle\nimport tensorflow as tf\nimport numpy as np\nimport tf_util\nimport gym\nimport load_policy\nimport os\n\n\ndef main():\n import argparse\n parser = argparse.ArgumentParser()\n parser.add_argument('expert_policy_file', type=str)\n parser.add_argument('envname', type=str)\n parser.add_argument('--render', action='store_true')\n parser.add_argument(\"--max_timesteps\", type=int)\n parser.add_argument('--num_rollouts', type=int, default=20,\n help='Number of expert roll outs')\n args = parser.parse_args()\n\n print('loading and building expert policy')\n policy_fn = load_policy.load_policy(args.expert_policy_file)\n print('loaded and built')\n\n with tf.Session():\n tf_util.initialize()\n\n env = gym.make(args.envname)\n max_steps = args.max_timesteps or env.spec.timestep_limit\n print('max_steps:', max_steps)\n returns = []\n observations = []\n actions = []\n for i in range(args.num_rollouts):\n print('iter', i)\n obs = env.reset()\n done = False\n totalr = 0.\n steps = 0\n while not done:\n action = policy_fn(obs[None,:])\n observations.append(obs)\n actions.append(action)\n obs, r, done, _ = env.step(action)\n totalr += r\n steps += 1\n # if args.render:\n # env.render()\n if steps % 100 == 0: print(\"%i/%i\"%(steps, max_steps))\n if steps >= max_steps:\n break\n returns.append(totalr)\n\n print('returns', returns)\n print('mean return', np.mean(returns))\n print('std of return', np.std(returns))\n\n expert_data = {'observations': np.array(observations), #(1000, 11)\n 'actions': np.array(actions)} #(1000, 1, 3)\n\n #write\n PROJECT_ROOT = os.path.dirname(os.path.realpath(__file__))\n save_dir = os.path.join(PROJECT_ROOT, \"data/\")\n out = os.path.join(save_dir, args.envname+'.train')\n np.savez(out, expert_data['observations'], expert_data['actions'])\n\n print(\"finished\")\n\nif __name__ == '__main__':\n main()\n"
},
{
"path": "hw1/tf_util.py",
"content": "import numpy as np\nimport tensorflow as tf # pylint: ignore-module\n#import builtins\nimport functools\nimport copy\nimport os\nimport collections\n\n# ================================================================\n# Import all names into common namespace\n# ================================================================\n\nclip = tf.clip_by_value\n\n# Make consistent with numpy\n# ----------------------------------------\n\ndef sum(x, axis=None, keepdims=False):\n return tf.reduce_sum(x, reduction_indices=None if axis is None else [axis], keep_dims = keepdims)\ndef mean(x, axis=None, keepdims=False):\n return tf.reduce_mean(x, reduction_indices=None if axis is None else [axis], keep_dims = keepdims)\ndef var(x, axis=None, keepdims=False):\n meanx = mean(x, axis=axis, keepdims=keepdims)\n return mean(tf.square(x - meanx), axis=axis, keepdims=keepdims)\ndef std(x, axis=None, keepdims=False):\n return tf.sqrt(var(x, axis=axis, keepdims=keepdims))\ndef max(x, axis=None, keepdims=False):\n return tf.reduce_max(x, reduction_indices=None if axis is None else [axis], keep_dims = keepdims)\ndef min(x, axis=None, keepdims=False):\n return tf.reduce_min(x, reduction_indices=None if axis is None else [axis], keep_dims = keepdims)\ndef concatenate(arrs, axis=0):\n return tf.concat(axis, arrs)\ndef argmax(x, axis=None):\n return tf.argmax(x, dimension=axis)\n\ndef switch(condition, then_expression, else_expression):\n '''Switches between two operations depending on a scalar value (int or bool).\n Note that both `then_expression` and `else_expression`\n should be symbolic tensors of the *same shape*.\n\n # Arguments\n condition: scalar tensor.\n then_expression: TensorFlow operation.\n else_expression: TensorFlow operation.\n '''\n x_shape = copy.copy(then_expression.get_shape())\n x = tf.cond(tf.cast(condition, 'bool'),\n lambda: then_expression,\n lambda: else_expression)\n x.set_shape(x_shape)\n return x\n\n# Extras\n# ----------------------------------------\ndef l2loss(params):\n if len(params) == 0:\n return tf.constant(0.0)\n else:\n return tf.add_n([sum(tf.square(p)) for p in params])\ndef lrelu(x, leak=0.2):\n f1 = 0.5 * (1 + leak)\n f2 = 0.5 * (1 - leak)\n return f1 * x + f2 * abs(x)\ndef categorical_sample_logits(X):\n # https://github.com/tensorflow/tensorflow/issues/456\n U = tf.random_uniform(tf.shape(X))\n return argmax(X - tf.log(-tf.log(U)), axis=1)\n\n# ================================================================\n# Global session\n# ================================================================\n\ndef get_session():\n return tf.get_default_session()\n\ndef single_threaded_session():\n tf_config = tf.ConfigProto(\n inter_op_parallelism_threads=1,\n intra_op_parallelism_threads=1)\n return tf.Session(config=tf_config)\n\ndef make_session(num_cpu):\n tf_config = tf.ConfigProto(\n inter_op_parallelism_threads=num_cpu,\n intra_op_parallelism_threads=num_cpu)\n return tf.Session(config=tf_config)\n\n\nALREADY_INITIALIZED = set()\ndef initialize():\n new_variables = set(tf.all_variables()) - ALREADY_INITIALIZED\n get_session().run(tf.initialize_variables(new_variables))\n ALREADY_INITIALIZED.update(new_variables)\n\n\ndef eval(expr, feed_dict=None):\n if feed_dict is None: feed_dict = {}\n return get_session().run(expr, feed_dict=feed_dict)\n\ndef set_value(v, val):\n get_session().run(v.assign(val))\n\ndef load_state(fname):\n saver = tf.train.Saver()\n saver.restore(get_session(), fname)\n\ndef save_state(fname):\n os.makedirs(os.path.dirname(fname), exist_ok=True)\n saver = tf.train.Saver()\n saver.save(get_session(), fname)\n\n# ================================================================\n# Model components\n# ================================================================\n\n\ndef normc_initializer(std=1.0):\n def _initializer(shape, dtype=None, partition_info=None): #pylint: disable=W0613\n out = np.random.randn(*shape).astype(np.float32)\n out *= std / np.sqrt(np.square(out).sum(axis=0, keepdims=True))\n return tf.constant(out)\n return _initializer\n\n\ndef conv2d(x, num_filters, name, filter_size=(3, 3), stride=(1, 1), pad=\"SAME\", dtype=tf.float32, collections=None,\n summary_tag=None):\n with tf.variable_scope(name):\n stride_shape = [1, stride[0], stride[1], 1]\n filter_shape = [filter_size[0], filter_size[1], int(x.get_shape()[3]), num_filters]\n\n # there are \"num input feature maps * filter height * filter width\"\n # inputs to each hidden unit\n fan_in = intprod(filter_shape[:3])\n # each unit in the lower layer receives a gradient from:\n # \"num output feature maps * filter height * filter width\" /\n # pooling size\n fan_out = intprod(filter_shape[:2]) * num_filters\n # initialize weights with random weights\n w_bound = np.sqrt(6. / (fan_in + fan_out))\n\n w = tf.get_variable(\"W\", filter_shape, dtype, tf.random_uniform_initializer(-w_bound, w_bound),\n collections=collections)\n b = tf.get_variable(\"b\", [1, 1, 1, num_filters], initializer=tf.zeros_initializer,\n collections=collections)\n\n if summary_tag is not None:\n tf.image_summary(summary_tag,\n tf.transpose(tf.reshape(w, [filter_size[0], filter_size[1], -1, 1]),\n [2, 0, 1, 3]),\n max_images=10)\n\n return tf.nn.conv2d(x, w, stride_shape, pad) + b\n\n\ndef dense(x, size, name, weight_init=None, bias=True):\n w = tf.get_variable(name + \"/w\", [x.get_shape()[1], size], initializer=weight_init)\n ret = tf.matmul(x, w)\n if bias:\n b = tf.get_variable(name + \"/b\", [size], initializer=tf.zeros_initializer)\n return ret + b\n else:\n return ret\n\ndef wndense(x, size, name, init_scale=1.0):\n v = tf.get_variable(name + \"/V\", [int(x.get_shape()[1]), size],\n initializer=tf.random_normal_initializer(0, 0.05))\n g = tf.get_variable(name + \"/g\", [size], initializer=tf.constant_initializer(init_scale))\n b = tf.get_variable(name + \"/b\", [size], initializer=tf.constant_initializer(0.0))\n\n # use weight normalization (Salimans & Kingma, 2016)\n x = tf.matmul(x, v)\n scaler = g / tf.sqrt(sum(tf.square(v), axis=0, keepdims=True))\n return tf.reshape(scaler, [1, size]) * x + tf.reshape(b, [1, size])\n\ndef densenobias(x, size, name, weight_init=None):\n return dense(x, size, name, weight_init=weight_init, bias=False)\n\ndef dropout(x, pkeep, phase=None, mask=None):\n mask = tf.floor(pkeep + tf.random_uniform(tf.shape(x))) if mask is None else mask\n if phase is None:\n return mask * x\n else:\n return switch(phase, mask*x, pkeep*x)\n\ndef batchnorm(x, name, phase, updates, gamma=0.96):\n k = x.get_shape()[1]\n runningmean = tf.get_variable(name+\"/mean\", shape=[1, k], initializer=tf.constant_initializer(0.0), trainable=False)\n runningvar = tf.get_variable(name+\"/var\", shape=[1, k], initializer=tf.constant_initializer(1e-4), trainable=False)\n testy = (x - runningmean) / tf.sqrt(runningvar)\n\n mean_ = mean(x, axis=0, keepdims=True)\n var_ = mean(tf.square(x), axis=0, keepdims=True)\n std = tf.sqrt(var_)\n trainy = (x - mean_) / std\n\n updates.extend([\n tf.assign(runningmean, runningmean * gamma + mean_ * (1 - gamma)),\n tf.assign(runningvar, runningvar * gamma + var_ * (1 - gamma))\n ])\n\n y = switch(phase, trainy, testy)\n\n out = y * tf.get_variable(name+\"/scaling\", shape=[1, k], initializer=tf.constant_initializer(1.0), trainable=True)\\\n + tf.get_variable(name+\"/translation\", shape=[1,k], initializer=tf.constant_initializer(0.0), trainable=True)\n return out\n\n\n\n# ================================================================\n# Basic Stuff\n# ================================================================\n\ndef function(inputs, outputs, updates=None, givens=None):\n if isinstance(outputs, list):\n return _Function(inputs, outputs, updates, givens=givens)\n elif isinstance(outputs, (dict, collections.OrderedDict)):\n f = _Function(inputs, outputs.values(), updates, givens=givens)\n return lambda *inputs : type(outputs)(zip(outputs.keys(), f(*inputs)))\n else:\n f = _Function(inputs, [outputs], updates, givens=givens)\n return lambda *inputs : f(*inputs)[0]\n\nclass _Function(object):\n def __init__(self, inputs, outputs, updates, givens, check_nan=False):\n assert all(len(i.op.inputs)==0 for i in inputs), \"inputs should all be placeholders\"\n self.inputs = inputs\n updates = updates or []\n self.update_group = tf.group(*updates)\n self.outputs_update = list(outputs) + [self.update_group]\n self.givens = {} if givens is None else givens\n self.check_nan = check_nan\n def __call__(self, *inputvals):\n assert len(inputvals) == len(self.inputs)\n feed_dict = dict(zip(self.inputs, inputvals))\n feed_dict.update(self.givens)\n results = get_session().run(self.outputs_update, feed_dict=feed_dict)[:-1]\n if self.check_nan:\n if any(np.isnan(r).any() for r in results):\n raise RuntimeError(\"Nan detected\")\n return results\n\ndef mem_friendly_function(nondata_inputs, data_inputs, outputs, batch_size):\n if isinstance(outputs, list):\n return _MemFriendlyFunction(nondata_inputs, data_inputs, outputs, batch_size)\n else:\n f = _MemFriendlyFunction(nondata_inputs, data_inputs, [outputs], batch_size)\n return lambda *inputs : f(*inputs)[0]\n\nclass _MemFriendlyFunction(object):\n def __init__(self, nondata_inputs, data_inputs, outputs, batch_size):\n self.nondata_inputs = nondata_inputs\n self.data_inputs = data_inputs\n self.outputs = list(outputs)\n self.batch_size = batch_size\n def __call__(self, *inputvals):\n assert len(inputvals) == len(self.nondata_inputs) + len(self.data_inputs)\n nondata_vals = inputvals[0:len(self.nondata_inputs)]\n data_vals = inputvals[len(self.nondata_inputs):]\n feed_dict = dict(zip(self.nondata_inputs, nondata_vals))\n n = data_vals[0].shape[0]\n for v in data_vals[1:]:\n assert v.shape[0] == n\n for i_start in range(0, n, self.batch_size):\n slice_vals = [v[i_start:min(i_start+self.batch_size, n)] for v in data_vals]\n for (var,val) in zip(self.data_inputs, slice_vals):\n feed_dict[var]=val\n results = tf.get_default_session().run(self.outputs, feed_dict=feed_dict)\n if i_start==0:\n sum_results = results\n else:\n for i in range(len(results)):\n sum_results[i] = sum_results[i] + results[i]\n for i in range(len(results)):\n sum_results[i] = sum_results[i] / n\n return sum_results\n\n# ================================================================\n# Modules\n# ================================================================\n\nclass Module(object):\n def __init__(self, name):\n self.name = name\n self.first_time = True\n self.scope = None\n self.cache = {}\n def __call__(self, *args):\n if args in self.cache:\n print(\"(%s) retrieving value from cache\"%self.name)\n return self.cache[args]\n with tf.variable_scope(self.name, reuse=not self.first_time):\n scope = tf.get_variable_scope().name\n if self.first_time:\n self.scope = scope\n print(\"(%s) running function for the first time\"%self.name)\n else:\n assert self.scope == scope, \"Tried calling function with a different scope\"\n print(\"(%s) running function on new inputs\"%self.name)\n self.first_time = False\n out = self._call(*args)\n self.cache[args] = out\n return out\n def _call(self, *args):\n raise NotImplementedError\n\n @property\n def trainable_variables(self):\n assert self.scope is not None, \"need to call module once before getting variables\"\n return tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, self.scope)\n\n @property\n def variables(self):\n assert self.scope is not None, \"need to call module once before getting variables\"\n return tf.get_collection(tf.GraphKeys.VARIABLES, self.scope)\n\n\ndef module(name):\n @functools.wraps\n def wrapper(f):\n class WrapperModule(Module):\n def _call(self, *args):\n return f(*args)\n return WrapperModule(name)\n return wrapper\n\n# ================================================================\n# Graph traversal\n# ================================================================\n\nVARIABLES = {}\n\n\ndef get_parents(node):\n return node.op.inputs\n\ndef topsorted(outputs):\n \"\"\"\n Topological sort via non-recursive depth-first search\n \"\"\"\n assert isinstance(outputs, (list,tuple))\n marks = {}\n out = []\n stack = [] #pylint: disable=W0621\n # i: node\n # jidx = number of children visited so far from that node\n # marks: state of each node, which is one of\n # 0: haven't visited\n # 1: have visited, but not done visiting children\n # 2: done visiting children\n for x in outputs:\n stack.append((x,0))\n while stack:\n (i,jidx) = stack.pop()\n if jidx == 0:\n m = marks.get(i,0)\n if m == 0:\n marks[i] = 1\n elif m == 1:\n raise ValueError(\"not a dag\")\n else:\n continue\n ps = get_parents(i)\n if jidx == len(ps):\n marks[i] = 2\n out.append(i)\n else:\n stack.append((i,jidx+1))\n j = ps[jidx]\n stack.append((j,0))\n return out\n\n\n# ================================================================\n# Flat vectors\n# ================================================================\n\ndef var_shape(x):\n out = [k.value for k in x.get_shape()]\n assert all(isinstance(a, int) for a in out), \\\n \"shape function assumes that shape is fully known\"\n return out\n\ndef numel(x):\n return intprod(var_shape(x))\n\ndef intprod(x):\n return int(np.prod(x))\n\ndef flatgrad(loss, var_list):\n grads = tf.gradients(loss, var_list)\n return tf.concat(0, [tf.reshape(grad, [numel(v)])\n for (v, grad) in zip(var_list, grads)])\n\nclass SetFromFlat(object):\n def __init__(self, var_list, dtype=tf.float32):\n assigns = []\n shapes = list(map(var_shape, var_list))\n total_size = np.sum([intprod(shape) for shape in shapes])\n\n self.theta = theta = tf.placeholder(dtype,[total_size])\n start=0\n assigns = []\n for (shape,v) in zip(shapes,var_list):\n size = intprod(shape)\n assigns.append(tf.assign(v, tf.reshape(theta[start:start+size],shape)))\n start+=size\n self.op = tf.group(*assigns)\n def __call__(self, theta):\n get_session().run(self.op, feed_dict={self.theta:theta})\n\nclass GetFlat(object):\n def __init__(self, var_list):\n self.op = tf.concat(0, [tf.reshape(v, [numel(v)]) for v in var_list])\n def __call__(self):\n return get_session().run(self.op)\n\n# ================================================================\n# Misc\n# ================================================================\n\n\ndef fancy_slice_2d(X, inds0, inds1):\n \"\"\"\n like numpy X[inds0, inds1]\n XXX this implementation is bad\n \"\"\"\n inds0 = tf.cast(inds0, tf.int64)\n inds1 = tf.cast(inds1, tf.int64)\n shape = tf.cast(tf.shape(X), tf.int64)\n ncols = shape[1]\n Xflat = tf.reshape(X, [-1])\n return tf.gather(Xflat, inds0 * ncols + inds1)\n\n\ndef scope_vars(scope, trainable_only):\n \"\"\"\n Get variables inside a scope\n The scope can be specified as a string\n \"\"\"\n return tf.get_collection(\n tf.GraphKeys.TRAINABLE_VARIABLES if trainable_only else tf.GraphKeys.VARIABLES,\n scope=scope if isinstance(scope, str) else scope.name\n )\n\ndef lengths_to_mask(lengths_b, max_length):\n \"\"\"\n Turns a vector of lengths into a boolean mask\n\n Args:\n lengths_b: an integer vector of lengths\n max_length: maximum length to fill the mask\n\n Returns:\n a boolean array of shape (batch_size, max_length)\n row[i] consists of True repeated lengths_b[i] times, followed by False\n \"\"\"\n lengths_b = tf.convert_to_tensor(lengths_b)\n assert lengths_b.get_shape().ndims == 1\n mask_bt = tf.expand_dims(tf.range(max_length), 0) < tf.expand_dims(lengths_b, 1)\n return mask_bt\n\n\ndef in_session(f):\n @functools.wraps(f)\n def newfunc(*args, **kwargs):\n with tf.Session():\n f(*args, **kwargs)\n return newfunc\n\n\n_PLACEHOLDER_CACHE = {} # name -> (placeholder, dtype, shape)\ndef get_placeholder(name, dtype, shape):\n print(\"calling get_placeholder\", name)\n if name in _PLACEHOLDER_CACHE:\n out, dtype1, shape1 = _PLACEHOLDER_CACHE[name]\n assert dtype1==dtype and shape1==shape\n return out\n else:\n out = tf.placeholder(dtype=dtype, shape=shape, name=name)\n _PLACEHOLDER_CACHE[name] = (out,dtype,shape)\n return out\ndef get_placeholder_cached(name):\n return _PLACEHOLDER_CACHE[name][0]\n\ndef flattenallbut0(x):\n return tf.reshape(x, [-1, intprod(x.get_shape().as_list()[1:])])\n\ndef reset():\n global _PLACEHOLDER_CACHE\n global VARIABLES\n _PLACEHOLDER_CACHE = {}\n VARIABLES = {}\n tf.reset_default_graph()\n"
},
{
"path": "hw2/.idea/hw2.iml",
"content": "\n\n \n \n \n \n \n \n \n \n \n"
},
{
"path": "hw2/.idea/misc.xml",
"content": "\n\n \n"
},
{
"path": "hw2/.idea/modules.xml",
"content": "\n\n \n \n \n \n \n"
},
{
"path": "hw2/.idea/workspace.xml",
"content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n ob_no\n loss\n #bl2\n build_mlp\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 1516669850648\n \n \n 1516669850648\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n"
},
{
"path": "hw2/data/HalfCheetah_b50000_rtg_na_25bl_HalfCheetah-v1_31-01-2018_19-35-00/1/log.txt",
"content": 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},
{
"path": "hw2/data/HalfCheetah_b50000_rtg_na_25bl_HalfCheetah-v1_31-01-2018_19-35-00/1/params.json",
"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"HalfCheetah-v1\",\n\"exp_name\"\t:\t\"HalfCheetah_b50000_rtg_na_25bl\",\n\"gamma\"\t:\t0.9,\n\"learning_rate\"\t:\t0.025,\n\"logdir\"\t:\t\"data/HalfCheetah_b50000_rtg_na_25bl_HalfCheetah-v1_31-01-2018_19-35-00/1\",\n\"max_path_length\"\t:\t150.0,\n\"min_timesteps_per_batch\"\t:\t50000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\ttrue,\n\"normalize_advantages\"\t:\ttrue,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t1,\n\"size\"\t:\t32}"
},
{
"path": "hw2/data/HalfCheetah_b50000_rtg_na_25bl_HalfCheetah-v1_31-01-2018_19-35-00/11/log.txt",
"content": 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},
{
"path": "hw2/data/HalfCheetah_b50000_rtg_na_25bl_HalfCheetah-v1_31-01-2018_19-35-00/11/params.json",
"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"HalfCheetah-v1\",\n\"exp_name\"\t:\t\"HalfCheetah_b50000_rtg_na_25bl\",\n\"gamma\"\t:\t0.9,\n\"learning_rate\"\t:\t0.025,\n\"logdir\"\t:\t\"data/HalfCheetah_b50000_rtg_na_25bl_HalfCheetah-v1_31-01-2018_19-35-00/11\",\n\"max_path_length\"\t:\t150.0,\n\"min_timesteps_per_batch\"\t:\t50000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\ttrue,\n\"normalize_advantages\"\t:\ttrue,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t11,\n\"size\"\t:\t32}"
},
{
"path": "hw2/data/HalfCheetah_b50000_rtg_na_25bl_HalfCheetah-v1_31-01-2018_19-35-00/21/log.txt",
"content": 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},
{
"path": "hw2/data/HalfCheetah_b50000_rtg_na_25bl_HalfCheetah-v1_31-01-2018_19-35-00/21/params.json",
"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"HalfCheetah-v1\",\n\"exp_name\"\t:\t\"HalfCheetah_b50000_rtg_na_25bl\",\n\"gamma\"\t:\t0.9,\n\"learning_rate\"\t:\t0.025,\n\"logdir\"\t:\t\"data/HalfCheetah_b50000_rtg_na_25bl_HalfCheetah-v1_31-01-2018_19-35-00/21\",\n\"max_path_length\"\t:\t150.0,\n\"min_timesteps_per_batch\"\t:\t50000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\ttrue,\n\"normalize_advantages\"\t:\ttrue,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t21,\n\"size\"\t:\t32}"
},
{
"path": "hw2/data/HalfCheetah_b50000_rtg_na_25bl_HalfCheetah-v1_31-01-2018_19-35-00/31/log.txt",
"content": 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},
{
"path": "hw2/data/HalfCheetah_b50000_rtg_na_25bl_HalfCheetah-v1_31-01-2018_19-35-00/31/params.json",
"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"HalfCheetah-v1\",\n\"exp_name\"\t:\t\"HalfCheetah_b50000_rtg_na_25bl\",\n\"gamma\"\t:\t0.9,\n\"learning_rate\"\t:\t0.025,\n\"logdir\"\t:\t\"data/HalfCheetah_b50000_rtg_na_25bl_HalfCheetah-v1_31-01-2018_19-35-00/31\",\n\"max_path_length\"\t:\t150.0,\n\"min_timesteps_per_batch\"\t:\t50000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\ttrue,\n\"normalize_advantages\"\t:\ttrue,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t31,\n\"size\"\t:\t32}"
},
{
"path": "hw2/data/HalfCheetah_b50000_rtg_na_25bl_HalfCheetah-v1_31-01-2018_19-35-00/41/log.txt",
"content": 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},
{
"path": "hw2/data/HalfCheetah_b50000_rtg_na_25bl_HalfCheetah-v1_31-01-2018_19-35-00/41/params.json",
"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"HalfCheetah-v1\",\n\"exp_name\"\t:\t\"HalfCheetah_b50000_rtg_na_25bl\",\n\"gamma\"\t:\t0.9,\n\"learning_rate\"\t:\t0.025,\n\"logdir\"\t:\t\"data/HalfCheetah_b50000_rtg_na_25bl_HalfCheetah-v1_31-01-2018_19-35-00/41\",\n\"max_path_length\"\t:\t150.0,\n\"min_timesteps_per_batch\"\t:\t50000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\ttrue,\n\"normalize_advantages\"\t:\ttrue,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t41,\n\"size\"\t:\t32}"
},
{
"path": "hw2/data/InvertedPendulum_sb_rtg_na_0.02_InvertedPendulum-v1_02-02-2018_10-42-58/1/log.txt",
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"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"InvertedPendulum-v1\",\n\"exp_name\"\t:\t\"InvertedPendulum_sb_rtg_na_0.02\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.02,\n\"logdir\"\t:\t\"data/InvertedPendulum_sb_rtg_na_0.02_InvertedPendulum-v1_02-02-2018_10-42-58/1\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t1000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t2,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\ttrue,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t1,\n\"size\"\t:\t32}"
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"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"InvertedPendulum-v1\",\n\"exp_name\"\t:\t\"InvertedPendulum_sb_rtg_na_0.02\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.02,\n\"logdir\"\t:\t\"data/InvertedPendulum_sb_rtg_na_0.02_InvertedPendulum-v1_02-02-2018_10-42-58/11\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t1000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t2,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\ttrue,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t11,\n\"size\"\t:\t32}"
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"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"InvertedPendulum-v1\",\n\"exp_name\"\t:\t\"InvertedPendulum_sb_rtg_na_0.02\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.02,\n\"logdir\"\t:\t\"data/InvertedPendulum_sb_rtg_na_0.02_InvertedPendulum-v1_02-02-2018_10-42-58/21\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t1000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t2,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\ttrue,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t21,\n\"size\"\t:\t32}"
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"path": "hw2/data/InvertedPendulum_sb_rtg_na_0.02_InvertedPendulum-v1_02-02-2018_10-42-58/31/params.json",
"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"InvertedPendulum-v1\",\n\"exp_name\"\t:\t\"InvertedPendulum_sb_rtg_na_0.02\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.02,\n\"logdir\"\t:\t\"data/InvertedPendulum_sb_rtg_na_0.02_InvertedPendulum-v1_02-02-2018_10-42-58/31\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t1000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t2,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\ttrue,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t31,\n\"size\"\t:\t32}"
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"path": "hw2/data/InvertedPendulum_sb_rtg_na_0.02_InvertedPendulum-v1_02-02-2018_10-42-58/41/params.json",
"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"InvertedPendulum-v1\",\n\"exp_name\"\t:\t\"InvertedPendulum_sb_rtg_na_0.02\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.02,\n\"logdir\"\t:\t\"data/InvertedPendulum_sb_rtg_na_0.02_InvertedPendulum-v1_02-02-2018_10-42-58/41\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t1000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t2,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\ttrue,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t41,\n\"size\"\t:\t32}"
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"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"InvertedPendulum-v1\",\n\"exp_name\"\t:\t\"InvertedPendulum_sb_rtg_na_bl_0.02\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.02,\n\"logdir\"\t:\t\"data/InvertedPendulum_sb_rtg_na_bl_0.02_InvertedPendulum-v1_02-02-2018_10-42-44/1\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t1000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t2,\n\"nn_baseline\"\t:\ttrue,\n\"normalize_advantages\"\t:\ttrue,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t1,\n\"size\"\t:\t32}"
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"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"InvertedPendulum-v1\",\n\"exp_name\"\t:\t\"InvertedPendulum_sb_rtg_na_bl_0.02\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.02,\n\"logdir\"\t:\t\"data/InvertedPendulum_sb_rtg_na_bl_0.02_InvertedPendulum-v1_02-02-2018_10-42-44/11\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t1000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t2,\n\"nn_baseline\"\t:\ttrue,\n\"normalize_advantages\"\t:\ttrue,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t11,\n\"size\"\t:\t32}"
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{
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"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"InvertedPendulum-v1\",\n\"exp_name\"\t:\t\"InvertedPendulum_sb_rtg_na_bl_0.02\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.02,\n\"logdir\"\t:\t\"data/InvertedPendulum_sb_rtg_na_bl_0.02_InvertedPendulum-v1_02-02-2018_10-42-44/21\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t1000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t2,\n\"nn_baseline\"\t:\ttrue,\n\"normalize_advantages\"\t:\ttrue,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t21,\n\"size\"\t:\t32}"
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"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"InvertedPendulum-v1\",\n\"exp_name\"\t:\t\"InvertedPendulum_sb_rtg_na_bl_0.02\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.02,\n\"logdir\"\t:\t\"data/InvertedPendulum_sb_rtg_na_bl_0.02_InvertedPendulum-v1_02-02-2018_10-42-44/31\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t1000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t2,\n\"nn_baseline\"\t:\ttrue,\n\"normalize_advantages\"\t:\ttrue,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t31,\n\"size\"\t:\t32}"
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{
"path": "hw2/data/InvertedPendulum_sb_rtg_na_bl_0.02_InvertedPendulum-v1_02-02-2018_10-42-44/41/params.json",
"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"InvertedPendulum-v1\",\n\"exp_name\"\t:\t\"InvertedPendulum_sb_rtg_na_bl_0.02\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.02,\n\"logdir\"\t:\t\"data/InvertedPendulum_sb_rtg_na_bl_0.02_InvertedPendulum-v1_02-02-2018_10-42-44/41\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t1000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t2,\n\"nn_baseline\"\t:\ttrue,\n\"normalize_advantages\"\t:\ttrue,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t41,\n\"size\"\t:\t32}"
},
{
"path": "hw2/data/lb_no_rtg_dna_CartPole-v0_24-01-2018_09-28-29/1/log.txt",
"content": 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},
{
"path": "hw2/data/lb_no_rtg_dna_CartPole-v0_24-01-2018_09-28-29/1/params.json",
"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"lb_no_rtg_dna\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/lb_no_rtg_dna_CartPole-v0_24-01-2018_09-28-29/1\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t5000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\tfalse,\n\"reward_to_go\"\t:\tfalse,\n\"seed\"\t:\t1,\n\"size\"\t:\t32}"
},
{
"path": "hw2/data/lb_no_rtg_dna_CartPole-v0_24-01-2018_09-28-29/11/log.txt",
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{
"path": "hw2/data/lb_no_rtg_dna_CartPole-v0_24-01-2018_09-28-29/11/params.json",
"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"lb_no_rtg_dna\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/lb_no_rtg_dna_CartPole-v0_24-01-2018_09-28-29/11\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t5000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\tfalse,\n\"reward_to_go\"\t:\tfalse,\n\"seed\"\t:\t11,\n\"size\"\t:\t32}"
},
{
"path": "hw2/data/lb_no_rtg_dna_CartPole-v0_24-01-2018_09-28-29/21/log.txt",
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},
{
"path": "hw2/data/lb_no_rtg_dna_CartPole-v0_24-01-2018_09-28-29/21/params.json",
"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"lb_no_rtg_dna\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/lb_no_rtg_dna_CartPole-v0_24-01-2018_09-28-29/21\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t5000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\tfalse,\n\"reward_to_go\"\t:\tfalse,\n\"seed\"\t:\t21,\n\"size\"\t:\t32}"
},
{
"path": "hw2/data/lb_no_rtg_dna_CartPole-v0_24-01-2018_09-28-29/31/log.txt",
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},
{
"path": "hw2/data/lb_no_rtg_dna_CartPole-v0_24-01-2018_09-28-29/31/params.json",
"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"lb_no_rtg_dna\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/lb_no_rtg_dna_CartPole-v0_24-01-2018_09-28-29/31\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t5000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\tfalse,\n\"reward_to_go\"\t:\tfalse,\n\"seed\"\t:\t31,\n\"size\"\t:\t32}"
},
{
"path": "hw2/data/lb_no_rtg_dna_CartPole-v0_24-01-2018_09-28-29/41/log.txt",
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{
"path": "hw2/data/lb_no_rtg_dna_CartPole-v0_24-01-2018_09-28-29/41/params.json",
"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"lb_no_rtg_dna\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/lb_no_rtg_dna_CartPole-v0_24-01-2018_09-28-29/41\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t5000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\tfalse,\n\"reward_to_go\"\t:\tfalse,\n\"seed\"\t:\t41,\n\"size\"\t:\t32}"
},
{
"path": "hw2/data/lb_rtg_dna_CartPole-v0_24-01-2018_09-20-37/1/log.txt",
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{
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"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"lb_rtg_dna\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/lb_rtg_dna_CartPole-v0_24-01-2018_09-20-37/1\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t5000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\tfalse,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t1,\n\"size\"\t:\t32}"
},
{
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{
"path": "hw2/data/lb_rtg_dna_CartPole-v0_24-01-2018_09-20-37/11/params.json",
"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"lb_rtg_dna\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/lb_rtg_dna_CartPole-v0_24-01-2018_09-20-37/11\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t5000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\tfalse,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t11,\n\"size\"\t:\t32}"
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{
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{
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"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"lb_rtg_dna\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/lb_rtg_dna_CartPole-v0_24-01-2018_09-20-37/21\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t5000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\tfalse,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t21,\n\"size\"\t:\t32}"
},
{
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{
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"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"lb_rtg_dna\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/lb_rtg_dna_CartPole-v0_24-01-2018_09-20-37/31\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t5000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\tfalse,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t31,\n\"size\"\t:\t32}"
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{
"path": "hw2/data/lb_rtg_dna_CartPole-v0_24-01-2018_09-20-37/41/log.txt",
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{
"path": "hw2/data/lb_rtg_dna_CartPole-v0_24-01-2018_09-20-37/41/params.json",
"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"lb_rtg_dna\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/lb_rtg_dna_CartPole-v0_24-01-2018_09-20-37/41\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t5000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\tfalse,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t41,\n\"size\"\t:\t32}"
},
{
"path": "hw2/data/lb_rtg_na_CartPole-v0_24-01-2018_09-11-55/1/log.txt",
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{
"path": "hw2/data/lb_rtg_na_CartPole-v0_24-01-2018_09-11-55/1/params.json",
"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"lb_rtg_na\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/lb_rtg_na_CartPole-v0_24-01-2018_09-11-55/1\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t5000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\ttrue,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t1,\n\"size\"\t:\t32}"
},
{
"path": "hw2/data/lb_rtg_na_CartPole-v0_24-01-2018_09-11-55/11/log.txt",
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{
"path": "hw2/data/lb_rtg_na_CartPole-v0_24-01-2018_09-11-55/11/params.json",
"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"lb_rtg_na\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/lb_rtg_na_CartPole-v0_24-01-2018_09-11-55/11\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t5000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\ttrue,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t11,\n\"size\"\t:\t32}"
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{
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{
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"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"lb_rtg_na\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/lb_rtg_na_CartPole-v0_24-01-2018_09-11-55/21\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t5000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\ttrue,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t21,\n\"size\"\t:\t32}"
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"path": "hw2/data/lb_rtg_na_CartPole-v0_24-01-2018_09-11-55/31/log.txt",
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{
"path": "hw2/data/lb_rtg_na_CartPole-v0_24-01-2018_09-11-55/31/params.json",
"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"lb_rtg_na\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/lb_rtg_na_CartPole-v0_24-01-2018_09-11-55/31\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t5000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\ttrue,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t31,\n\"size\"\t:\t32}"
},
{
"path": "hw2/data/lb_rtg_na_CartPole-v0_24-01-2018_09-11-55/41/log.txt",
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{
"path": "hw2/data/lb_rtg_na_CartPole-v0_24-01-2018_09-11-55/41/params.json",
"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"lb_rtg_na\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/lb_rtg_na_CartPole-v0_24-01-2018_09-11-55/41\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t5000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\ttrue,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t41,\n\"size\"\t:\t32}"
},
{
"path": "hw2/data/sb_no_rtg_dna_CartPole-v0_24-01-2018_09-00-15/1/log.txt",
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{
"path": "hw2/data/sb_no_rtg_dna_CartPole-v0_24-01-2018_09-00-15/1/params.json",
"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"sb_no_rtg_dna\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/sb_no_rtg_dna_CartPole-v0_24-01-2018_09-00-15/1\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t1000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\tfalse,\n\"reward_to_go\"\t:\tfalse,\n\"seed\"\t:\t1,\n\"size\"\t:\t32}"
},
{
"path": "hw2/data/sb_no_rtg_dna_CartPole-v0_24-01-2018_09-00-15/11/log.txt",
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},
{
"path": "hw2/data/sb_no_rtg_dna_CartPole-v0_24-01-2018_09-00-15/11/params.json",
"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"sb_no_rtg_dna\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/sb_no_rtg_dna_CartPole-v0_24-01-2018_09-00-15/11\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t1000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\tfalse,\n\"reward_to_go\"\t:\tfalse,\n\"seed\"\t:\t11,\n\"size\"\t:\t32}"
},
{
"path": "hw2/data/sb_no_rtg_dna_CartPole-v0_24-01-2018_09-00-15/21/log.txt",
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{
"path": "hw2/data/sb_no_rtg_dna_CartPole-v0_24-01-2018_09-00-15/21/params.json",
"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"sb_no_rtg_dna\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/sb_no_rtg_dna_CartPole-v0_24-01-2018_09-00-15/21\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t1000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\tfalse,\n\"reward_to_go\"\t:\tfalse,\n\"seed\"\t:\t21,\n\"size\"\t:\t32}"
},
{
"path": "hw2/data/sb_no_rtg_dna_CartPole-v0_24-01-2018_09-00-15/31/log.txt",
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{
"path": "hw2/data/sb_no_rtg_dna_CartPole-v0_24-01-2018_09-00-15/31/params.json",
"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"sb_no_rtg_dna\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/sb_no_rtg_dna_CartPole-v0_24-01-2018_09-00-15/31\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t1000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\tfalse,\n\"reward_to_go\"\t:\tfalse,\n\"seed\"\t:\t31,\n\"size\"\t:\t32}"
},
{
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"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"sb_no_rtg_dna\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/sb_no_rtg_dna_CartPole-v0_24-01-2018_09-00-15/41\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t1000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\tfalse,\n\"reward_to_go\"\t:\tfalse,\n\"seed\"\t:\t41,\n\"size\"\t:\t32}"
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"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"sb_rtg_dna\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/sb_rtg_dna_CartPole-v0_24-01-2018_09-04-19/1\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t1000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\tfalse,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t1,\n\"size\"\t:\t32}"
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{
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"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"sb_rtg_dna\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/sb_rtg_dna_CartPole-v0_24-01-2018_09-04-19/11\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t1000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\tfalse,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t11,\n\"size\"\t:\t32}"
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"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"sb_rtg_dna\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/sb_rtg_dna_CartPole-v0_24-01-2018_09-04-19/21\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t1000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\tfalse,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t21,\n\"size\"\t:\t32}"
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{
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"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"sb_rtg_dna\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/sb_rtg_dna_CartPole-v0_24-01-2018_09-04-19/31\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t1000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\tfalse,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t31,\n\"size\"\t:\t32}"
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{
"path": "hw2/data/sb_rtg_dna_CartPole-v0_24-01-2018_09-04-19/41/params.json",
"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"sb_rtg_dna\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/sb_rtg_dna_CartPole-v0_24-01-2018_09-04-19/41\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t1000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\tfalse,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t41,\n\"size\"\t:\t32}"
},
{
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"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"sb_rtg_na\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/sb_rtg_na_CartPole-v0_24-01-2018_09-08-49/1\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t1000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\ttrue,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t1,\n\"size\"\t:\t32}"
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"path": "hw2/data/sb_rtg_na_CartPole-v0_24-01-2018_09-08-49/11/params.json",
"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"sb_rtg_na\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/sb_rtg_na_CartPole-v0_24-01-2018_09-08-49/11\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t1000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\ttrue,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t11,\n\"size\"\t:\t32}"
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"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"sb_rtg_na\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/sb_rtg_na_CartPole-v0_24-01-2018_09-08-49/21\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t1000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\ttrue,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t21,\n\"size\"\t:\t32}"
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"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"sb_rtg_na\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/sb_rtg_na_CartPole-v0_24-01-2018_09-08-49/31\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t1000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\ttrue,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t31,\n\"size\"\t:\t32}"
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"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"sb_rtg_na\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/sb_rtg_na_CartPole-v0_24-01-2018_09-08-49/41\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t1000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t1,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\ttrue,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t41,\n\"size\"\t:\t32}"
},
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"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"sb_rtg_na_l2\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/sb_rtg_na_l2_CartPole-v0_25-01-2018_09-22-07/1\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t1000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t2,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\ttrue,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t1,\n\"size\"\t:\t32}"
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{
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"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"sb_rtg_na_l2\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/sb_rtg_na_l2_CartPole-v0_25-01-2018_09-22-07/11\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t1000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t2,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\ttrue,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t11,\n\"size\"\t:\t32}"
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{
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"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"sb_rtg_na_l2\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/sb_rtg_na_l2_CartPole-v0_25-01-2018_09-22-07/21\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t1000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t2,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\ttrue,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t21,\n\"size\"\t:\t32}"
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"path": "hw2/data/sb_rtg_na_l2_CartPole-v0_25-01-2018_09-22-07/31/params.json",
"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"sb_rtg_na_l2\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/sb_rtg_na_l2_CartPole-v0_25-01-2018_09-22-07/31\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t1000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t2,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\ttrue,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t31,\n\"size\"\t:\t32}"
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"path": "hw2/data/sb_rtg_na_l2_CartPole-v0_25-01-2018_09-22-07/41/log.txt",
"content": "LossDelta\tTime\tIteration\tAverageReturn\tStdReturn\tMaxReturn\tMinReturn\tEpLenMean\tEpLenStd\tTimestepsThisBatch\tTimestepsSoFar\n0.0240665\t0.2724125385284424\t0\t21.9130434783\t12.1759315097\t61.0\t10.0\t21.9130434783\t12.1759315097\t1008\t1008\n0.0191814\t0.4369683265686035\t1\t23.4651162791\t12.8377148535\t63.0\t10.0\t23.4651162791\t12.8377148535\t1009\t2017\n0.0142603\t0.5918362140655518\t2\t41.0\t26.9414179285\t145.0\t11.0\t41.0\t26.9414179285\t1025\t3042\n0.0161849\t0.7480564117431641\t3\t43.8695652174\t18.4515920082\t86.0\t22.0\t43.8695652174\t18.4515920082\t1009\t4051\n0.00303223\t0.8936238288879395\t4\t66.8666666667\t38.3803190306\t169.0\t14.0\t66.8666666667\t38.3803190306\t1003\t5054\n0.00739272\t1.0534791946411133\t5\t78.2307692308\t28.0416005418\t125.0\t24.0\t78.2307692308\t28.0416005418\t1017\t6071\n0.00558814\t1.2160682678222656\t6\t86.9166666667\t53.3064385313\t181.0\t20.0\t86.9166666667\t53.3064385313\t1043\t7114\n0.00998791\t1.3846797943115234\t7\t84.6153846154\t36.4154203633\t135.0\t20.0\t84.6153846154\t36.4154203633\t1100\t8214\n0.00548161\t1.5466818809509277\t8\t137.875\t38.5662984353\t163.0\t40.0\t137.875\t38.5662984353\t1103\t9317\n0.00547876\t1.7158620357513428\t9\t168.666666667\t54.9777732866\t200.0\t47.0\t168.666666667\t54.9777732866\t1012\t10329\n0.00446923\t1.8750333786010742\t10\t133.75\t60.6666094322\t200.0\t28.0\t133.75\t60.6666094322\t1070\t11399\n0.00223307\t2.055856943130493\t11\t184.5\t15.9973956214\t200.0\t162.0\t184.5\t15.9973956214\t1107\t12506\n0.00134373\t2.2368900775909424\t12\t192.166666667\t17.5158658237\t200.0\t153.0\t192.166666667\t17.5158658237\t1153\t13659\n0.00124597\t2.4215517044067383\t13\t186.166666667\t19.8193227825\t200.0\t153.0\t186.166666667\t19.8193227825\t1117\t14776\n0.00145733\t2.6035101413726807\t14\t196.833333333\t5.177408189\t200.0\t186.0\t196.833333333\t5.177408189\t1181\t15957\n0.00251378\t2.7715864181518555\t15\t170.5\t41.8001196171\t200.0\t107.0\t170.5\t41.8001196171\t1023\t16980\n0.00138271\t2.952169895172119\t16\t188.666666667\t14.4645159692\t200.0\t161.0\t188.666666667\t14.4645159692\t1132\t18112\n-6.74652e-05\t3.145519733428955\t17\t192.833333333\t10.6209959776\t200.0\t173.0\t192.833333333\t10.6209959776\t1157\t19269\n0.00192012\t3.3173272609710693\t18\t185.833333333\t15.1703292281\t200.0\t159.0\t185.833333333\t15.1703292281\t1115\t20384\n0.00023939\t3.5140669345855713\t19\t200.0\t0.0\t200.0\t200.0\t200.0\t0.0\t1200\t21584\n-0.00037027\t3.6911509037017822\t20\t196.0\t8.94427191\t200.0\t176.0\t196.0\t8.94427191\t1176\t22760\n0.000997769\t3.8583834171295166\t21\t168.666666667\t44.3721634461\t200.0\t102.0\t168.666666667\t44.3721634461\t1012\t23772\n0.000196372\t4.026484489440918\t22\t179.166666667\t32.4366904737\t200.0\t114.0\t179.166666667\t32.4366904737\t1075\t24847\n0.000625191\t4.2131028175354\t23\t200.0\t0.0\t200.0\t200.0\t200.0\t0.0\t1200\t26047\n0.000741529\t4.3935956954956055\t24\t194.166666667\t13.0437298687\t200.0\t165.0\t194.166666667\t13.0437298687\t1165\t27212\n-0.000160483\t4.584748983383179\t25\t200.0\t0.0\t200.0\t200.0\t200.0\t0.0\t1200\t28412\n0.000807466\t4.767037630081177\t26\t197.166666667\t6.33552593625\t200.0\t183.0\t197.166666667\t6.33552593625\t1183\t29595\n0.000266545\t4.960889101028442\t27\t200.0\t0.0\t200.0\t200.0\t200.0\t0.0\t1200\t30795\n0.000694204\t5.147199869155884\t28\t200.0\t0.0\t200.0\t200.0\t200.0\t0.0\t1200\t31995\n0.00127902\t5.33878231048584\t29\t200.0\t0.0\t200.0\t200.0\t200.0\t0.0\t1200\t33195\n0.00135321\t5.524590969085693\t30\t200.0\t0.0\t200.0\t200.0\t200.0\t0.0\t1200\t34395\n-0.00037003\t5.709272861480713\t31\t200.0\t0.0\t200.0\t200.0\t200.0\t0.0\t1200\t35595\n0.000786842\t5.8937907218933105\t32\t200.0\t0.0\t200.0\t200.0\t200.0\t0.0\t1200\t36795\n0.00352819\t6.081728219985962\t33\t200.0\t0.0\t200.0\t200.0\t200.0\t0.0\t1200\t37995\n0.00458174\t6.260660171508789\t34\t195.833333333\t7.31247032283\t200.0\t180.0\t195.833333333\t7.31247032283\t1175\t39170\n0.0048685\t6.449543476104736\t35\t200.0\t0.0\t200.0\t200.0\t200.0\t0.0\t1200\t40370\n0.00105708\t6.6329967975616455\t36\t200.0\t0.0\t200.0\t200.0\t200.0\t0.0\t1200\t41570\n0.00131097\t6.807498216629028\t37\t190.666666667\t13.646326327\t200.0\t166.0\t190.666666667\t13.646326327\t1144\t42714\n0.00164302\t6.971754789352417\t38\t180.0\t27.5015151098\t200.0\t123.0\t180.0\t27.5015151098\t1080\t43794\n0.00182745\t7.147618055343628\t39\t161.857142857\t25.8204159058\t196.0\t119.0\t161.857142857\t25.8204159058\t1133\t44927\n0.000121372\t7.315327405929565\t40\t160.857142857\t23.942959427\t200.0\t135.0\t160.857142857\t23.942959427\t1126\t46053\n0.00120265\t7.48945426940918\t41\t157.142857143\t32.7694481903\t200.0\t105.0\t157.142857143\t32.7694481903\t1100\t47153\n0.00528999\t7.666618824005127\t42\t163.857142857\t24.4331658169\t200.0\t137.0\t163.857142857\t24.4331658169\t1147\t48300\n4.2649e-05\t7.850505113601685\t43\t196.0\t5.7735026919\t200.0\t186.0\t196.0\t5.7735026919\t1176\t49476\n0.00245808\t8.01331090927124\t44\t177.666666667\t20.4830553276\t200.0\t146.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18872833252\t95\t200.0\t0.0\t200.0\t200.0\t200.0\t0.0\t1200\t111110\n0.000815801\t17.783868312835693\t96\t200.0\t0.0\t200.0\t200.0\t200.0\t0.0\t1200\t112310\n0.000511491\t17.974011421203613\t97\t200.0\t0.0\t200.0\t200.0\t200.0\t0.0\t1200\t113510\n0.000476312\t18.16336750984192\t98\t200.0\t0.0\t200.0\t200.0\t200.0\t0.0\t1200\t114710\n-0.000571645\t18.351927757263184\t99\t200.0\t0.0\t200.0\t200.0\t200.0\t0.0\t1200\t115910\n"
},
{
"path": "hw2/data/sb_rtg_na_l2_CartPole-v0_25-01-2018_09-22-07/41/params.json",
"content": "{\"animate\"\t:\tfalse,\n\"env_name\"\t:\t\"CartPole-v0\",\n\"exp_name\"\t:\t\"sb_rtg_na_l2\",\n\"gamma\"\t:\t1.0,\n\"learning_rate\"\t:\t0.005,\n\"logdir\"\t:\t\"data/sb_rtg_na_l2_CartPole-v0_25-01-2018_09-22-07/41\",\n\"max_path_length\"\t:\tnull,\n\"min_timesteps_per_batch\"\t:\t1000,\n\"n_iter\"\t:\t100,\n\"n_layers\"\t:\t2,\n\"nn_baseline\"\t:\tfalse,\n\"normalize_advantages\"\t:\ttrue,\n\"reward_to_go\"\t:\ttrue,\n\"seed\"\t:\t41,\n\"size\"\t:\t32}"
},
{
"path": "hw2/logz.py",
"content": "import json\n\n\"\"\"\n\nSome simple logging functionality, inspired by rllab's logging.\nAssumes that each diagnostic gets logged each iteration\n\nCall logz.configure_output_dir() to start logging to a \ntab-separated-values file (some_folder_name/log.txt)\n\nTo load the learning curves, you can do, for example\n\nA = np.genfromtxt('/tmp/expt_1468984536/log.txt',delimiter='\\t',dtype=None, names=True)\nA['EpRewMean']\n\n\"\"\"\n\nimport os.path as osp, shutil, time, atexit, os, subprocess\nimport pickle\nimport tensorflow as tf\n\ncolor2num = dict(\n gray=30,\n red=31,\n green=32,\n yellow=33,\n blue=34,\n magenta=35,\n cyan=36,\n white=37,\n crimson=38\n)\n\ndef colorize(string, color, bold=False, highlight=False):\n attr = []\n num = color2num[color]\n if highlight: num += 10\n attr.append(str(num))\n if bold: attr.append('1')\n return '\\x1b[%sm%s\\x1b[0m' % (';'.join(attr), string)\n\nclass G:\n output_dir = None\n output_file = None\n first_row = True\n log_headers = []\n log_current_row = {}\n\ndef configure_output_dir(d=None):\n \"\"\"\n Set output directory to d, or to /tmp/somerandomnumber if d is None\n \"\"\"\n G.output_dir = d or \"/tmp/experiments/%i\"%int(time.time())\n assert not osp.exists(G.output_dir), \"Log dir %s already exists! Delete it first or use a different dir\"%G.output_dir\n os.makedirs(G.output_dir)\n G.output_file = open(osp.join(G.output_dir, \"log.txt\"), 'w')\n atexit.register(G.output_file.close)\n print(colorize(\"Logging data to %s\"%G.output_file.name, 'green', bold=True))\n\ndef log_tabular(key, val):\n \"\"\"\n Log a value of some diagnostic\n Call this once for each diagnostic quantity, each iteration\n \"\"\"\n if G.first_row:\n G.log_headers.append(key)\n else:\n assert key in G.log_headers, \"Trying to introduce a new key %s that you didn't include in the first iteration\"%key\n assert key not in G.log_current_row, \"You already set %s this iteration. Maybe you forgot to call dump_tabular()\"%key\n G.log_current_row[key] = val\n\ndef save_params(params):\n with open(osp.join(G.output_dir, \"params.json\"), 'w') as out:\n out.write(json.dumps(params, separators=(',\\n','\\t:\\t'), sort_keys=True))\n\ndef pickle_tf_vars(): \n \"\"\"\n Saves tensorflow variables\n Requires them to be initialized first, also a default session must exist\n \"\"\"\n _dict = {v.name : v.eval() for v in tf.global_variables()}\n with open(osp.join(G.output_dir, \"vars.pkl\"), 'wb') as f:\n pickle.dump(_dict, f)\n \n\ndef dump_tabular():\n \"\"\"\n Write all of the diagnostics from the current iteration\n \"\"\"\n vals = []\n key_lens = [len(key) for key in G.log_headers]\n max_key_len = max(15,max(key_lens))\n keystr = '%'+'%d'%max_key_len\n fmt = \"| \" + keystr + \"s | %15s |\"\n n_slashes = 22 + max_key_len\n print(\"-\"*n_slashes)\n for key in G.log_headers:\n val = G.log_current_row.get(key, \"\")\n if hasattr(val, \"__float__\"): valstr = \"%8.3g\"%val\n else: valstr = val\n print(fmt%(key, valstr))\n vals.append(val)\n print(\"-\"*n_slashes)\n if G.output_file is not None:\n if G.first_row:\n G.output_file.write(\"\\t\".join(G.log_headers))\n G.output_file.write(\"\\n\")\n G.output_file.write(\"\\t\".join(map(str,vals)))\n G.output_file.write(\"\\n\")\n G.output_file.flush()\n G.log_current_row.clear()\n G.first_row=False\n"
},
{
"path": "hw2/plot.py",
"content": "import seaborn as sns\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport json\nimport numpy as np\nimport os\n\n\"\"\"\nUsing the plotter:\n\nCall it from the command line, and supply it with logdirs to experiments.\nSuppose you ran an experiment with name 'test', and you ran 'test' for 10 \nrandom seeds. The runner code stored it in the directory structure\n\n data\n L test_EnvName_DateTime\n L 0\n L log.txt\n L params.json\n L 1\n L log.txt\n L params.json\n .\n .\n .\n L 9\n L log.txt\n L params.json\n\nTo plot learning curves from the experiment, averaged over all random\nseeds, call\n\n python plot.py data/test_EnvName_DateTime --value AverageReturn\n\nand voila. To see a different statistics, change what you put in for\nthe keyword --value. You can also enter /multiple/ values, and it will \nmake all of them in order.\n\n\nSuppose you ran two experiments: 'test1' and 'test2'. In 'test2' you tried\na different set of hyperparameters from 'test1', and now you would like \nto compare them -- see their learning curves side-by-side. Just call\n\n python plot.py data/test1 data/test2\n\nand it will plot them both! They will be given titles in the legend according\nto their exp_name parameters. If you want to use custom legend titles, use\nthe --legend flag and then provide a title for each logdir.\n\n\"\"\"\n\ndef plot_data(data, value=\"AverageReturn\"):\n data = [d[10:] for d in data]\n if isinstance(data, list):\n data = pd.concat(data, ignore_index=True)\n sns.set(style=\"darkgrid\", font_scale=1.5)\n sns.tsplot(data=data, time=\"Iteration\", value=value, unit=\"Unit\", condition=\"Condition\")\n plt.legend(loc='best').draggable()\n plt.show()\n\n\ndef get_datasets(fpath, condition=None):\n unit = 0\n datasets = []\n for root, dir, files in os.walk(fpath):\n if 'log.txt' in files:\n param_path = open(os.path.join(root,'params.json'))\n params = json.load(param_path)\n exp_name = params['exp_name']\n \n log_path = os.path.join(root,'log.txt')\n experiment_data = pd.read_table(log_path)\n\n experiment_data.insert(\n len(experiment_data.columns),\n 'Unit',\n unit\n )\n experiment_data.insert(\n len(experiment_data.columns),\n 'Condition',\n condition or exp_name\n )\n\n datasets.append(experiment_data)\n unit += 1\n\n return datasets\n\n\ndef main():\n import argparse\n parser = argparse.ArgumentParser()\n parser.add_argument('logdir', nargs='*')\n parser.add_argument('--legend', nargs='*')\n parser.add_argument('--value', default='AverageReturn', nargs='*')\n args = parser.parse_args()\n\n use_legend = False\n if args.legend is not None:\n assert len(args.legend) == len(args.logdir), \\\n \"Must give a legend title for each set of experiments.\"\n use_legend = True\n\n data = []\n if use_legend:\n for logdir, legend_title in zip(args.logdir, args.legend):\n data += get_datasets(logdir, legend_title)\n else:\n for logdir in args.logdir:\n data += get_datasets(logdir)\n\n if isinstance(args.value, list):\n values = args.value\n else:\n values = [args.value]\n for value in values:\n plot_data(data, value=value)\n\nif __name__ == \"__main__\":\n main()\n"
},
{
"path": "hw2/train_pg.py",
"content": "import numpy as np\nimport tensorflow as tf\nimport gym\nimport logz\nimport scipy.signal\nimport os\nimport time\nimport inspect\nfrom multiprocessing import Process\n\n#============================================================================================#\n# Utilities\n#============================================================================================#\n\ndef build_mlp(\n input_placeholder, \n output_size,\n scope, \n n_layers=2, \n size=64, \n activation=tf.tanh,\n output_activation=None\n ):\n #========================================================================================#\n # ----------SECTION 3----------\n # Network building\n #\n # Your code should make a feedforward neural network (also called a multilayer perceptron)\n # with 'n_layers' hidden layers of size 'size' units. \n # \n # The output layer should have size 'output_size' and activation 'output_activation'.\n #\n # Hint: use tf.layers.dense\n #========================================================================================#\n\n with tf.variable_scope(scope):\n # YOUR_CODE_HERE\n layer = input_placeholder\n for i in range(n_layers):\n layer = tf.layers.dense(layer, size, activation=activation, )\n out_layer = tf.layers.dense(layer, output_size, activation=output_activation, )\n return out_layer\n\ndef pathlength(path):\n return len(path[\"reward\"])\n\n\n\n#============================================================================================#\n# Policy Gradient\n#============================================================================================#\n\ndef train_PG(exp_name='',\n env_name='CartPole-v0',\n n_iter=100, \n gamma=1.0, \n min_timesteps_per_batch=1000, \n max_path_length=None,\n learning_rate=5e-3, \n reward_to_go=True, \n animate=True, \n logdir=None, \n normalize_advantages=True,\n nn_baseline=False, \n seed=0,\n # network arguments\n n_layers=1,\n size=32\n ):\n\n start = time.time()\n\n # Configure output directory for logging\n logz.configure_output_dir(logdir)\n\n # Log experimental parameters\n args = inspect.getargspec(train_PG)[0]\n locals_ = locals()\n params = {k: locals_[k] if k in locals_ else None for k in args}\n logz.save_params(params)\n\n # Set random seeds\n tf.set_random_seed(seed)\n np.random.seed(seed)\n\n # Make the gym environment\n env = gym.make(env_name)\n \n # Is this env continuous, or discrete?\n discrete = isinstance(env.action_space, gym.spaces.Discrete)\n\n # Maximum length for episodes\n max_path_length = max_path_length or env.spec.max_episode_steps\n\n #========================================================================================#\n # Notes on notation:\n # \n # Symbolic variables have the prefix sy_, to distinguish them from the numerical values\n # that are computed later in the function\n # \n # Prefixes and suffixes:\n # ob - observation \n # ac - action\n # _no - this tensor should have shape (batch size /n/, observation dim)\n # _na - this tensor should have shape (batch size /n/, action dim)\n # _n - this tensor should have shape (batch size /n/)\n # \n # Note: batch size /n/ is defined at runtime, and until then, the shape for that axis\n # is None\n #========================================================================================#\n\n # Observation and action sizes\n ob_dim = env.observation_space.shape[0]\n ac_dim = env.action_space.n if discrete else env.action_space.shape[0]\n\n #========================================================================================#\n # ----------SECTION 4----------\n # Placeholders\n # \n # Need these for batch observations / actions / advantages in policy gradient loss function.\n #========================================================================================#\n\n sy_ob_no = tf.placeholder(shape=[None, ob_dim], name=\"ob\", dtype=tf.float32)\n if discrete:\n sy_ac_na = tf.placeholder(shape=[None], name=\"ac\", dtype=tf.int32) \n else:\n sy_ac_na = tf.placeholder(shape=[None, ac_dim], name=\"ac\", dtype=tf.float32) \n\n # Define a placeholder for advantages\n sy_adv_n = tf.placeholder(shape=[None], name=\"adv\", dtype=tf.float32)\n\n\n #========================================================================================#\n # ----------SECTION 4----------\n # Networks\n # \n # Make symbolic operations for\n # 1. Policy network outputs which describe the policy distribution.\n # a. For the discrete case, just logits for each action.\n #\n # b. For the continuous case, the mean / log std of a Gaussian distribution over \n # actions.\n #\n # Hint: use the 'build_mlp' function you defined in utilities.\n #\n # Note: these ops should be functions of the placeholder 'sy_ob_no'\n #\n # 2. Producing samples stochastically from the policy distribution.\n # a. For the discrete case, an op that takes in logits and produces actions.\n #\n # Should have shape [None]\n #\n # b. For the continuous case, use the reparameterization trick:\n # The output from a Gaussian distribution with mean 'mu' and std 'sigma' is\n #\n # mu + sigma * z, z ~ N(0, I)\n #\n # This reduces the problem to just sampling z. (Hint: use tf.random_normal!)\n #\n # Should have shape [None, ac_dim]\n #\n # Note: these ops should be functions of the policy network output ops.\n #\n # 3. Computing the log probability of a set of actions that were actually taken, \n # according to the policy.\n #\n # Note: these ops should be functions of the placeholder 'sy_ac_na', and the \n # policy network output ops.\n # \n #========================================================================================#\n\n if discrete:\n # YOUR_CODE_HERE\n sy_logits_na = build_mlp(sy_ob_no, ac_dim, \"discrete\", n_layers, size) #logit\n # noticed tf.multinomial return [batch, numsample] here is [batch, 1], should reshape to [batch]\n sy_sampled_ac = tf.reshape(tf.multinomial(sy_logits_na, 1), [-1]) # Hint: Use the tf.multinomial op, choose action by logit(prob)\n sy_logprob_n = -tf.nn.sparse_softmax_cross_entropy_with_logits(labels=sy_ac_na, logits=sy_logits_na) # -CE\n\n else:\n # YOUR_CODE_HERE\n sy_mean = build_mlp(sy_ob_no, ac_dim, \"continuous\", n_layers, size) #logit\n\n sy_logstd = tf.get_variable(\"std\", [ac_dim], dtype=tf.float32) # logstd should just be a trainable variable, not a network output.\n sy_sampled_ac = tf.random_normal(shape=tf.shape(sy_mean), mean=sy_mean, stddev=tf.exp(sy_logstd))\n sy_logprob_n = tf.contrib.distributions.MultivariateNormalDiag(loc=sy_mean, scale_diag=tf.exp(sy_logstd)).log_prob(sy_ac_na) # Hint: Use the log probability under a multivariate gaussian.\n\n\n\n #========================================================================================#\n # ----------SECTION 4----------\n # Loss Function and Training Operation\n #========================================================================================#\n\n loss = tf.reduce_mean(-sy_logprob_n * sy_adv_n)# Loss function that we'll differentiate to get the policy gradient.\n update_op = tf.train.AdamOptimizer(learning_rate).minimize(loss)\n\n\n #========================================================================================#\n # ----------SECTION 5----------\n # Optional Baseline\n #========================================================================================#\n\n if nn_baseline:\n baseline_prediction = tf.squeeze(build_mlp(\n sy_ob_no, \n 1, \n \"nn_baseline\",\n n_layers=n_layers,\n size=size))\n # Define placeholders for targets, a loss function and an update op for fitting a \n # neural network baseline. These will be used to fit the neural network baseline. \n # YOUR_CODE_HERE\n # baseline_targets = norm_q_n\n # here loss = l2 (bn - norm_q_n)\n baseline_targets = tf.placeholder(shape=[None], name=\"baseline\", dtype=tf.float32)\n baseline_loss = tf.nn.l2_loss(baseline_prediction - baseline_targets)\n baseline_update_op = tf.train.AdamOptimizer(learning_rate).minimize(baseline_loss)\n\n\n #========================================================================================#\n # Tensorflow Engineering: Config, Session, Variable initialization\n #========================================================================================#\n\n tf_config = tf.ConfigProto(inter_op_parallelism_threads=1, intra_op_parallelism_threads=1) \n\n sess = tf.Session(config=tf_config)\n sess.__enter__() # equivalent to `with sess:`\n tf.global_variables_initializer().run() #pylint: disable=E1101\n\n\n\n #========================================================================================#\n # Training Loop\n #========================================================================================#\n\n total_timesteps = 0\n\n for itr in range(n_iter):\n print(\"********** Iteration %i ************\"%itr)\n\n # Collect paths until we have enough timesteps\n timesteps_this_batch = 0\n paths = []\n while True:\n ob = env.reset()\n obs, acs, rewards = [], [], []\n animate_this_episode=(len(paths) == 0 and (itr % 10 == 0) and animate)\n steps = 0\n while True:\n if animate_this_episode:\n env.render()\n time.sleep(0.05)\n obs.append(ob)\n ac = sess.run(sy_sampled_ac, feed_dict={sy_ob_no: ob[None]})\n ac = ac[0]\n acs.append(ac)\n ob, rew, done, _ = env.step(ac)\n rewards.append(rew)\n steps += 1\n if done or steps > max_path_length:\n break\n path = {\"observation\": np.array(obs),\n \"reward\": np.array(rewards),\n \"action\": np.array(acs)}\n paths.append(path)\n timesteps_this_batch += pathlength(path)\n if timesteps_this_batch > min_timesteps_per_batch:\n break\n total_timesteps += timesteps_this_batch\n\n # Build arrays for observation, action for the policy gradient update by concatenating \n # across paths\n ob_no = np.concatenate([path[\"observation\"] for path in paths])\n ac_na = np.concatenate([path[\"action\"] for path in paths])\n\n # 我们直接把q_n当做target，用神经网络拟合/hat v(s)，最终的adv=q_n-拟合的v(s)，然后更新actor梯度\n # 而V(s)即critic的梯度更新也是q_n-拟合的v(s)(其实这个是TDerror，这个作业里不明显)\n #====================================================================================#\n # ----------SECTION 4----------\n # Computing Q-values\n #\n # Your code should construct numpy arrays for Q-values which will be used to compute\n # advantages (which will in turn be fed to the placeholder you defined above). \n #\n # Recall that the expression for the policy gradient PG is\n #\n # PG = E_{tau} [sum_{t=0}^T grad log pi(a_t|s_t) * (Q_t - b_t )]\n #\n # where \n #\n # tau=(s_0, a_0, ...) is a trajectory,\n # Q_t is the Q-value at time t, Q^{pi}(s_t, a_t),\n # and b_t is a baseline which may depend on s_t. \n #\n # You will write code for two cases, controlled by the flag 'reward_to_go':\n #\n # Case 1: trajectory-based PG \n #\n # (reward_to_go = False)\n #\n # Instead of Q^{pi}(s_t, a_t), we use the total discounted reward summed over \n # entire trajectory (regardless of which time step the Q-value should be for). \n #\n # For this case, the policy gradient estimator is\n #\n # E_{tau} [sum_{t=0}^T grad log pi(a_t|s_t) * Ret(tau)]\n #\n # where\n #\n # Ret(tau) = sum_{t'=0}^T gamma^t' r_{t'}.\n #\n # Thus, you should compute\n #\n # Q_t = Ret(tau)\n #\n # Case 2: reward-to-go PG \n #\n # (reward_to_go = True)\n #\n # Here, you estimate Q^{pi}(s_t, a_t) by the discounted sum of rewards starting\n # from time step t. Thus, you should compute\n #\n # Q_t = sum_{t'=t}^T gamma^(t'-t) * r_{t'}\n #\n #\n # Store the Q-values for all timesteps and all trajectories in a variable 'q_n',\n # like the 'ob_no' and 'ac_na' above. \n #\n #====================================================================================#\n\n # YOUR_CODE_HERE\n q_n = []\n for path in paths:\n r = path[\"reward\"]\n max_step = len(r)\n if reward_to_go:\n q = [np.sum(np.power(gamma, np.arange(max_step - t)) * r[t:]) for t in range(max_step)]\n else:\n q = [np.sum(np.power(gamma, np.arange(max_step)) * r) for t in range(max_step)]\n q_n.extend(q)\n\n #====================================================================================#\n # ----------SECTION 5----------\n # Computing Baselines\n #====================================================================================#\n\n if nn_baseline:\n # If nn_baseline is True, use your neural network to predict reward-to-go\n # at each timestep for each trajectory, and save the result in a variable 'b_n'\n # like 'ob_no', 'ac_na', and 'q_n'.\n #\n # Hint #bl1: rescale the output from the nn_baseline to match the statistics\n # (mean and std) of the current or previous batch of Q-values. (Goes with Hint\n # #bl2 below.)\n b_n = sess.run(baseline_prediction, feed_dict={sy_ob_no: ob_no})\n # b_n_norm = b_n - np.mean(b_n, axis=0) / (np.std(b_n, axis=0) + 1e-7)\n b_n = b_n * np.std(q_n, axis=0) + np.mean(q_n, axis=0)\n adv_n = q_n - b_n\n else:\n adv_n = q_n.copy()\n\n #====================================================================================#\n # ----------SECTION 4----------\n # Advantage Normalization\n #====================================================================================#\n\n if normalize_advantages:\n # On the next line, implement a trick which is known empirically to reduce variance\n # in policy gradient methods: normalize adv_n to have mean zero and std=1. \n # YOUR_CODE_HERE\n adv_mean = np.mean(adv_n, axis=0)\n adv_std = np.std(adv_n, axis=0)\n adv_n = (adv_n - adv_mean) / (adv_std + 1e-7)\n\n #====================================================================================#\n # ----------SECTION 5----------\n # Optimizing Neural Network Baseline\n #====================================================================================#\n if nn_baseline:\n # ----------SECTION 5----------\n # If a neural network baseline is used, set up the targets and the inputs for the \n # baseline. \n # \n # Fit it to the current batch in order to use for the next iteration. Use the \n # baseline_update_op you defined earlier.\n #\n # Hint #bl2: Instead of trying to target raw Q-values directly, rescale the \n # targets to have mean zero and std=1. (Goes with Hint #bl1 above.)\n\n # YOUR_CODE_HERE\n q_n_mean = np.mean(q_n, axis=0)\n q_n_std = np.std(q_n, axis=0)\n q_n = (q_n - q_n_mean) / (q_n_std + 1e-7)\n # rew_n = np.concatenate([path[\"reward\"] for path in paths])\n # trg_n = rew_n + b_n\n # trg_n = (trg_n - np.mean(trg_n)) / np.std(trg_n)\n sess.run(baseline_update_op, feed_dict={sy_ob_no: ob_no, baseline_targets: q_n})\n\n #====================================================================================#\n # ----------SECTION 4----------\n # Performing the Policy Update\n #====================================================================================#\n\n # Call the update operation necessary to perform the policy gradient update based on \n # the current batch of rollouts.\n # \n # For debug purposes, you may wish to save the value of the loss function before\n # and after an update, and then log them below. \n\n # YOUR_CODE_HERE\n feed_dict = {sy_ob_no: ob_no, sy_ac_na: ac_na, sy_adv_n: adv_n}\n loss_1 = sess.run(loss, feed_dict)\n sess.run(update_op, feed_dict)\n loss_2 = sess.run(loss, feed_dict)\n\n # Log diagnostics\n returns = [path[\"reward\"].sum() for path in paths]\n ep_lengths = [pathlength(path) for path in paths]\n logz.log_tabular(\"LossDelta\", loss_1 - loss_2)\n logz.log_tabular(\"Time\", time.time() - start)\n logz.log_tabular(\"Iteration\", itr)\n logz.log_tabular(\"AverageReturn\", np.mean(returns))\n logz.log_tabular(\"StdReturn\", np.std(returns))\n logz.log_tabular(\"MaxReturn\", np.max(returns))\n logz.log_tabular(\"MinReturn\", np.min(returns))\n logz.log_tabular(\"EpLenMean\", np.mean(ep_lengths))\n logz.log_tabular(\"EpLenStd\", np.std(ep_lengths))\n logz.log_tabular(\"TimestepsThisBatch\", timesteps_this_batch)\n logz.log_tabular(\"TimestepsSoFar\", total_timesteps)\n logz.dump_tabular()\n logz.pickle_tf_vars()\n\n\ndef main():\n import argparse\n parser = argparse.ArgumentParser()\n parser.add_argument('env_name', type=str)\n parser.add_argument('--exp_name', type=str, default='vpg')\n parser.add_argument('--render', action='store_true')\n parser.add_argument('--discount', type=float, default=1.0)\n parser.add_argument('--n_iter', '-n', type=int, default=100)\n parser.add_argument('--batch_size', '-b', type=int, default=1000)\n parser.add_argument('--ep_len', '-ep', type=float, default=-1.)\n parser.add_argument('--learning_rate', '-lr', type=float, default=5e-3)\n parser.add_argument('--reward_to_go', '-rtg', action='store_true')\n parser.add_argument('--dont_normalize_advantages', '-dna', action='store_true')\n parser.add_argument('--nn_baseline', '-bl', action='store_true')\n parser.add_argument('--seed', type=int, default=1)\n parser.add_argument('--n_experiments', '-e', type=int, default=1)\n parser.add_argument('--n_layers', '-l', type=int, default=1)\n parser.add_argument('--size', '-s', type=int, default=32)\n args = parser.parse_args()\n\n if not(os.path.exists('data')):\n os.makedirs('data')\n logdir = args.exp_name + '_' + args.env_name + '_' + time.strftime(\"%d-%m-%Y_%H-%M-%S\")\n logdir = os.path.join('data', logdir)\n if not(os.path.exists(logdir)):\n os.makedirs(logdir)\n\n max_path_length = args.ep_len if args.ep_len > 0 else None\n\n for e in range(args.n_experiments):\n seed = args.seed + 10*e\n print('Running experiment with seed %d'%seed)\n def train_func():\n train_PG(\n exp_name=args.exp_name,\n env_name=args.env_name,\n n_iter=args.n_iter,\n gamma=args.discount,\n min_timesteps_per_batch=args.batch_size,\n max_path_length=max_path_length,\n learning_rate=args.learning_rate,\n reward_to_go=args.reward_to_go,\n animate=args.render,\n logdir=os.path.join(logdir,'%d'%seed),\n normalize_advantages=not(args.dont_normalize_advantages),\n nn_baseline=args.nn_baseline, \n seed=seed,\n n_layers=args.n_layers,\n size=args.size\n )\n # Awkward hacky process runs, because Tensorflow does not like\n # repeatedly calling train_PG in the same thread.\n p = Process(target=train_func, args=tuple())\n p.start()\n p.join()\n \n\nif __name__ == \"__main__\":\n main()\n"
},
{
"path": "hw3/.idea/hw3.iml",
"content": "\n\n \n \n \n \n \n \n \n \n \n"
},
{
"path": "hw3/.idea/misc.xml",
"content": "\n\n \n"
},
{
"path": "hw3/.idea/modules.xml",
"content": "\n\n \n \n \n \n \n"
},
{
"path": "hw3/.idea/workspace.xml",
"content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n model_initialized\n num_param_updates\n AVAILABLE GPUS\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 1516844406618\n \n \n 1516844406618\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n"
},
{
"path": "hw3/README",
"content": "See http://rll.berkeley.edu/deeprlcourse/f17docs/hw3.pdf for instructions\n\nThe starter code was based on an implementation of Q-learning for Atari\ngenerously provided by Szymon Sidor from OpenAI\n\n"
},
{
"path": "hw3/atari_wrappers.py",
"content": "import cv2\nimport numpy as np\nfrom collections import deque\nimport gym\nfrom gym import spaces\n\n\nclass NoopResetEnv(gym.Wrapper):\n def __init__(self, env=None, noop_max=30):\n \"\"\"Sample initial states by taking random number of no-ops on reset.\n No-op is assumed to be action 0.\n \"\"\"\n super(NoopResetEnv, self).__init__(env)\n self.noop_max = noop_max\n assert env.unwrapped.get_action_meanings()[0] == 'NOOP'\n\n def _reset(self):\n \"\"\" Do no-op action for a number of steps in [1, noop_max].\"\"\"\n self.env.reset()\n noops = np.random.randint(1, self.noop_max + 1)\n for _ in range(noops):\n obs, _, _, _ = self.env.step(0)\n return obs\n\nclass FireResetEnv(gym.Wrapper):\n def __init__(self, env=None):\n \"\"\"Take action on reset for environments that are fixed until firing.\"\"\"\n super(FireResetEnv, self).__init__(env)\n assert env.unwrapped.get_action_meanings()[1] == 'FIRE'\n assert len(env.unwrapped.get_action_meanings()) >= 3\n\n def _reset(self):\n self.env.reset()\n obs, _, _, _ = self.env.step(1)\n obs, _, _, _ = self.env.step(2)\n return obs\n\nclass EpisodicLifeEnv(gym.Wrapper):\n def __init__(self, env=None):\n \"\"\"Make end-of-life == end-of-episode, but only reset on true game over.\n Done by DeepMind for the DQN and co. since it helps value estimation.\n \"\"\"\n super(EpisodicLifeEnv, self).__init__(env)\n self.lives = 0\n self.was_real_done = True\n self.was_real_reset = False\n\n def _step(self, action):\n obs, reward, done, info = self.env.step(action)\n self.was_real_done = done\n # check current lives, make loss of life terminal,\n # then update lives to handle bonus lives\n lives = self.env.unwrapped.ale.lives()\n if lives < self.lives and lives > 0:\n # for Qbert somtimes we stay in lives == 0 condtion for a few frames\n # so its important to keep lives > 0, so that we only reset once\n # the environment advertises done.\n done = True\n self.lives = lives\n return obs, reward, done, info\n\n def _reset(self):\n \"\"\"Reset only when lives are exhausted.\n This way all states are still reachable even though lives are episodic,\n and the learner need not know about any of this behind-the-scenes.\n \"\"\"\n if self.was_real_done:\n obs = self.env.reset()\n self.was_real_reset = True\n else:\n # no-op step to advance from terminal/lost life state\n obs, _, _, _ = self.env.step(0)\n self.was_real_reset = False\n self.lives = self.env.unwrapped.ale.lives()\n return obs\n\nclass MaxAndSkipEnv(gym.Wrapper):\n def __init__(self, env=None, skip=4):\n \"\"\"Return only every `skip`-th frame\"\"\"\n super(MaxAndSkipEnv, self).__init__(env)\n # most recent raw observations (for max pooling across time steps)\n self._obs_buffer = deque(maxlen=2)\n self._skip = skip\n\n def _step(self, action):\n total_reward = 0.0\n done = None\n for _ in range(self._skip):\n obs, reward, done, info = self.env.step(action)\n self._obs_buffer.append(obs)\n total_reward += reward\n if done:\n break\n\n max_frame = np.max(np.stack(self._obs_buffer), axis=0)\n\n return max_frame, total_reward, done, info\n\n def _reset(self):\n \"\"\"Clear past frame buffer and init. to first obs. from inner env.\"\"\"\n self._obs_buffer.clear()\n obs = self.env.reset()\n self._obs_buffer.append(obs)\n return obs\n\ndef _process_frame84(frame):\n img = np.reshape(frame, [210, 160, 3]).astype(np.float32)\n img = img[:, :, 0] * 0.299 + img[:, :, 1] * 0.587 + img[:, :, 2] * 0.114\n resized_screen = cv2.resize(img, (84, 110), interpolation=cv2.INTER_LINEAR)\n x_t = resized_screen[18:102, :]\n x_t = np.reshape(x_t, [84, 84, 1])\n return x_t.astype(np.uint8)\n\nclass ProcessFrame84(gym.Wrapper):\n def __init__(self, env=None):\n super(ProcessFrame84, self).__init__(env)\n self.observation_space = spaces.Box(low=0, high=255, shape=(84, 84, 1))\n\n def _step(self, action):\n obs, reward, done, info = self.env.step(action)\n return _process_frame84(obs), reward, done, info\n\n def _reset(self):\n return _process_frame84(self.env.reset())\n\nclass ClippedRewardsWrapper(gym.Wrapper):\n def _step(self, action):\n obs, reward, done, info = self.env.step(action)\n return obs, np.sign(reward), done, info\n\ndef wrap_deepmind_ram(env):\n env = EpisodicLifeEnv(env)\n env = NoopResetEnv(env, noop_max=30)\n env = MaxAndSkipEnv(env, skip=4)\n if 'FIRE' in env.unwrapped.get_action_meanings():\n env = FireResetEnv(env)\n env = ClippedRewardsWrapper(env)\n return env\n\ndef wrap_deepmind(env):\n assert 'NoFrameskip' in env.spec.id\n env = EpisodicLifeEnv(env)\n env = NoopResetEnv(env, noop_max=30)\n env = MaxAndSkipEnv(env, skip=4)\n if 'FIRE' in env.unwrapped.get_action_meanings():\n env = FireResetEnv(env)\n env = ProcessFrame84(env)\n env = ClippedRewardsWrapper(env)\n return env\n"
},
{
"path": "hw3/dqn.py",
"content": "import sys\nimport gym.spaces\nimport itertools\nimport numpy as np\nimport random\nimport tensorflow as tf\nimport tensorflow.contrib.layers as layers\nfrom collections import namedtuple\nfrom dqn_utils import *\nimport logz\n\nOptimizerSpec = namedtuple(\"OptimizerSpec\", [\"constructor\", \"kwargs\", \"lr_schedule\"])\n\ndef learn(env,\n q_func,\n optimizer_spec,\n session,\n exploration=LinearSchedule(1000000, 0.1),\n stopping_criterion=None,\n replay_buffer_size=1000000,\n batch_size=32,\n gamma=0.99,\n learning_starts=50000,\n learning_freq=4,\n frame_history_len=4,\n target_update_freq=10000,\n grad_norm_clipping=10):\n \"\"\"Run Deep Q-learning algorithm.\n\n You can specify your own convnet using q_func.\n\n All schedules are w.r.t. total number of steps taken in the environment.\n\n Parameters\n ----------\n env: gym.Env\n gym environment to train on.\n q_func: function\n Model to use for computing the q function. It should accept the\n following named arguments:\n img_in: tf.Tensor\n tensorflow tensor representing the input image\n num_actions: int\n number of actions\n scope: str\n scope in which all the model related variables\n should be created\n reuse: bool\n whether previously created variables should be reused.\n optimizer_spec: OptimizerSpec\n Specifying the constructor and kwargs, as well as learning rate schedule\n for the optimizer\n session: tf.Session\n tensorflow session to use.\n exploration: rl_algs.deepq.utils.schedules.Schedule\n schedule for probability of chosing random action.\n stopping_criterion: (env, t) -> bool\n should return true when it's ok for the RL algorithm to stop.\n takes in env and the number of steps executed so far.\n replay_buffer_size: int\n How many memories to store in the replay buffer.\n batch_size: int\n How many transitions to sample each time experience is replayed.\n gamma: float\n Discount Factor\n learning_starts: int\n After how many environment steps to start replaying experiences\n learning_freq: int\n How many steps of environment to take between every experience replay\n frame_history_len: int\n How many past frames to include as input to the model.\n target_update_freq: int\n How many experience replay rounds (not steps!) to perform between\n each update to the target Q network\n grad_norm_clipping: float or None\n If not None gradients' norms are clipped to this value.\n \"\"\"\n assert type(env.observation_space) == gym.spaces.Box\n assert type(env.action_space) == gym.spaces.Discrete\n\n ###############\n # BUILD MODEL #\n ###############\n\n if len(env.observation_space.shape) == 1:\n # This means we are running on low-dimensional observations (e.g. RAM)\n input_shape = env.observation_space.shape\n else:\n img_h, img_w, img_c = env.observation_space.shape\n input_shape = (img_h, img_w, frame_history_len * img_c)\n num_actions = env.action_space.n\n\n # set up placeholders\n # placeholder for current observation (or state)\n obs_t_ph = tf.placeholder(tf.uint8, [None] + list(input_shape))\n # placeholder for current action\n act_t_ph = tf.placeholder(tf.int32, [None])\n # placeholder for current reward\n rew_t_ph = tf.placeholder(tf.float32, [None])\n # placeholder for next observation (or state)\n obs_tp1_ph = tf.placeholder(tf.uint8, [None] + list(input_shape))\n # placeholder for end of episode mask\n # this value is 1 if the next state corresponds to the end of an episode,\n # in which case there is no Q-value at the next state; at the end of an\n # episode, only the current state reward contributes to the target, not the\n # next state Q-value (i.e. target is just rew_t_ph, not rew_t_ph + gamma * q_tp1)\n done_mask_ph = tf.placeholder(tf.float32, [None])\n\n # casting to float on GPU ensures lower data transfer times.\n obs_t_float = tf.cast(obs_t_ph, tf.float32) / 255.0\n obs_tp1_float = tf.cast(obs_tp1_ph, tf.float32) / 255.0\n\n # Here, you should fill in your own code to compute the Bellman error. This requires\n # evaluating the current and next Q-values and constructing the corresponding error.\n # TensorFlow will differentiate this error for you, you just need to pass it to the\n # optimizer. See assignment text for details.\n # Your code should produce one scalar-valued tensor: total_error\n # This will be passed to the optimizer in the provided code below.\n # Your code should also produce two collections of variables:\n # q_func_vars\n # target_q_func_vars\n # These should hold all of the variables of the Q-function network and target network,\n # respectively. A convenient way to get these is to make use of TF's \"scope\" feature.\n # For example, you can create your Q-function network with the scope \"q_func\" like this:\n # = q_func(obs_t_float, num_actions, scope=\"q_func\", reuse=False)\n # And then you can obtain the variables like this:\n # q_func_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='q_func')\n # Older versions of TensorFlow may require using \"VARIABLES\" instead of \"GLOBAL_VARIABLES\"\n ######\n \n # YOUR CODE HERE\n q = q_func(obs_t_float, num_actions, scope=\"q_func\", reuse=False)\n target_q = q_func(obs_tp1_float, num_actions, scope=\"target_q_func\", reuse=False)\n Q_samp = rew_t_ph + (1 - done_mask_ph) * gamma * tf.reduce_max(target_q, axis=1)\n Q_s = tf.reduce_sum(q * tf.one_hot(act_t_ph, num_actions), axis=1)\n total_error = tf.reduce_mean(tf.square(Q_samp - Q_s))\n q_func_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='q_func')\n target_q_func_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='target_q_func')\n\n ######\n\n # construct optimization op (with gradient clipping)\n learning_rate = tf.placeholder(tf.float32, (), name=\"learning_rate\")\n optimizer = optimizer_spec.constructor(learning_rate=learning_rate, **optimizer_spec.kwargs)\n train_fn = minimize_and_clip(optimizer, total_error,\n var_list=q_func_vars, clip_val=grad_norm_clipping)\n\n # update_target_fn will be called periodically to copy Q network to target Q network\n update_target_fn = []\n for var, var_target in zip(sorted(q_func_vars, key=lambda v: v.name),\n sorted(target_q_func_vars, key=lambda v: v.name)):\n update_target_fn.append(var_target.assign(var))\n update_target_fn = tf.group(*update_target_fn)\n\n # construct the replay buffer\n replay_buffer = ReplayBuffer(replay_buffer_size, frame_history_len)\n\n ###############\n # RUN ENV #\n ###############\n model_initialized = False\n num_param_updates = 0\n mean_episode_reward = -float('nan')\n best_mean_episode_reward = -float('inf')\n last_obs = env.reset()\n LOG_EVERY_N_STEPS = 10000\n\n for t in itertools.count():\n ### 1. Check stopping criterion\n if stopping_criterion is not None and stopping_criterion(env, t):\n break\n\n ### 2. Step the env and store the transition\n # At this point, \"last_obs\" contains the latest observation that was\n # recorded from the simulator. Here, your code needs to store this\n # observation and its outcome (reward, next observation, etc.) into\n # the replay buffer while stepping the simulator forward one step.\n # At the end of this block of code, the simulator should have been\n # advanced one step, and the replay buffer should contain one more\n # transition.\n # Specifically, last_obs must point to the new latest observation.\n # Useful functions you'll need to call:\n # obs, reward, done, info = env.step(action)\n # this steps the environment forward one step\n # obs = env.reset()\n # this resets the environment if you reached an episode boundary.\n # Don't forget to call env.reset() to get a new observation if done\n # is true!!\n # Note that you cannot use \"last_obs\" directly as input\n # into your network, since it needs to be processed to include context\n # from previous frames. You should check out the replay buffer\n # implementation in dqn_utils.py to see what functionality the replay\n # buffer exposes. The replay buffer has a function called\n # encode_recent_observation that will take the latest observation\n # that you pushed into the buffer and compute the corresponding\n # input that should be given to a Q network by appending some\n # previous frames.\n # Don't forget to include epsilon greedy exploration!\n # And remember that the first time you enter this loop, the model\n # may not yet have been initialized (but of course, the first step\n # might as well be random, since you haven't trained your net...)\n\n #####\n \n # YOUR CODE HERE\n # replay memory stuff\n idx = replay_buffer.store_frame(last_obs)\n q_input = replay_buffer.encode_recent_observation()\n\n if (np.random.random() < exploration.value(t)) or not model_initialized:\n action = env.action_space.sample()\n else:\n # chose action according to current Q and exploration\n action_values = session.run(q, feed_dict={obs_t_ph: [q_input]})[0]\n action = np.argmax(action_values)\n\n # perform action in env\n new_state, reward, done, info = env.step(action)\n\n # store the transition\n replay_buffer.store_effect(idx, action, reward, done)\n last_obs = new_state\n\n if done:\n last_obs = env.reset()\n\n #####\n\n # at this point, the environment should have been advanced one step (and\n # reset if done was true), and last_obs should point to the new latest\n # observation\n\n ### 3. Perform experience replay and train the network.\n # note that this is only done if the replay buffer contains enough samples\n # for us to learn something useful -- until then, the model will not be\n # initialized and random actions should be taken\n if (t > learning_starts and\n t % learning_freq == 0 and\n replay_buffer.can_sample(batch_size)):\n # Here, you should perform training. Training consists of four steps:\n # 3.a: use the replay buffer to sample a batch of transitions (see the\n # replay buffer code for function definition, each batch that you sample\n # should consist of current observations, current actions, rewards,\n # next observations, and done indicator).\n # 3.b: initialize the model if it has not been initialized yet; to do\n # that, call\n # initialize_interdependent_variables(session, tf.global_variables(), {\n # obs_t_ph: obs_t_batch,\n # obs_tp1_ph: obs_tp1_batch,\n # })\n # where obs_t_batch and obs_tp1_batch are the batches of observations at\n # the current and next time step. The boolean variable model_initialized\n # indicates whether or not the model has been initialized.\n # Remember that you have to update the target network too (see 3.d)!\n # 3.c: train the model. To do this, you'll need to use the train_fn and\n # total_error ops that were created earlier: total_error is what you\n # created to compute the total Bellman error in a batch, and train_fn\n # will actually perform a gradient step and update the network parameters\n # to reduce total_error. When calling session.run on these you'll need to\n # populate the following placeholders:\n # obs_t_ph\n # act_t_ph\n # rew_t_ph\n # obs_tp1_ph\n # done_mask_ph\n # (this is needed for computing total_error)\n # learning_rate -- you can get this from optimizer_spec.lr_schedule.value(t)\n # (this is needed by the optimizer to choose the learning rate)\n # 3.d: periodically update the target network by calling\n # session.run(update_target_fn)\n # you should update every target_update_freq steps, and you may find the\n # variable num_param_updates useful for this (it was initialized to 0)\n #####\n \n # YOUR CODE HERE\n s_batch, a_batch, r_batch, sp_batch, done_mask_batch = replay_buffer.sample(batch_size)\n\n if not model_initialized:\n initialize_interdependent_variables(session, tf.global_variables(),\n {obs_t_ph: s_batch, obs_tp1_ph: sp_batch, })\n model_initialized = True\n\n feed_dict = {obs_t_ph: s_batch,\n act_t_ph: a_batch,\n rew_t_ph: r_batch,\n obs_tp1_ph: sp_batch,\n done_mask_ph: done_mask_batch,\n learning_rate: optimizer_spec.lr_schedule.value(t)}\n session.run(train_fn, feed_dict=feed_dict)\n\n num_param_updates += 1\n if num_param_updates % target_update_freq == 0:\n session.run(update_target_fn)\n num_param_updates = 0\n\n #####\n\n ### 4. Log progress\n episode_rewards = get_wrapper_by_name(env, \"Monitor\").get_episode_rewards()\n if len(episode_rewards) > 0:\n mean_episode_reward = np.mean(episode_rewards[-100:])\n if len(episode_rewards) > 100:\n best_mean_episode_reward = max(best_mean_episode_reward, mean_episode_reward)\n if t % LOG_EVERY_N_STEPS == 0 and model_initialized:\n # print(\"Timestep %d\" % (t,))\n # print(\"mean reward (100 episodes) %f\" % mean_episode_reward)\n # print(\"best mean reward %f\" % best_mean_episode_reward)\n # print(\"episodes %d\" % len(episode_rewards))\n # print(\"exploration %f\" % exploration.value(t))\n # print(\"learning_rate %f\" % optimizer_spec.lr_schedule.value(t))\n logz.log_tabular('Timestep', t)\n logz.log_tabular('MeanReward', mean_episode_reward)\n logz.log_tabular('BestMeanReward', best_mean_episode_reward)\n logz.log_tabular('episodes', len(episode_rewards))\n logz.log_tabular('exploration', exploration.value(t))\n logz.log_tabular('learning_rate', optimizer_spec.lr_schedule.value(t))\n logz.dump_tabular()\n sys.stdout.flush()\n"
},
{
"path": "hw3/dqn_utils.py",
"content": "\"\"\"This file includes a collection of utility functions that are useful for\nimplementing DQN.\"\"\"\nimport gym\nimport tensorflow as tf\nimport numpy as np\nimport random\n\ndef huber_loss(x, delta=1.0):\n # https://en.wikipedia.org/wiki/Huber_loss\n return tf.select(\n tf.abs(x) < delta,\n tf.square(x) * 0.5,\n delta * (tf.abs(x) - 0.5 * delta)\n )\n\ndef sample_n_unique(sampling_f, n):\n \"\"\"Helper function. Given a function `sampling_f` that returns\n comparable objects, sample n such unique objects.\n \"\"\"\n res = []\n while len(res) < n:\n candidate = sampling_f()\n if candidate not in res:\n res.append(candidate)\n return res\n\nclass Schedule(object):\n def value(self, t):\n \"\"\"Value of the schedule at time t\"\"\"\n raise NotImplementedError()\n\nclass ConstantSchedule(object):\n def __init__(self, value):\n \"\"\"Value remains constant over time.\n Parameters\n ----------\n value: float\n Constant value of the schedule\n \"\"\"\n self._v = value\n\n def value(self, t):\n \"\"\"See Schedule.value\"\"\"\n return self._v\n\ndef linear_interpolation(l, r, alpha):\n return l + alpha * (r - l)\n\nclass PiecewiseSchedule(object):\n def __init__(self, endpoints, interpolation=linear_interpolation, outside_value=None):\n \"\"\"Piecewise schedule.\n endpoints: [(int, int)]\n list of pairs `(time, value)` meanining that schedule should output\n `value` when `t==time`. All the values for time must be sorted in\n an increasing order. When t is between two times, e.g. `(time_a, value_a)`\n and `(time_b, value_b)`, such that `time_a <= t < time_b` then value outputs\n `interpolation(value_a, value_b, alpha)` where alpha is a fraction of\n time passed between `time_a` and `time_b` for time `t`.\n interpolation: lambda float, float, float: float\n a function that takes value to the left and to the right of t according\n to the `endpoints`. Alpha is the fraction of distance from left endpoint to\n right endpoint that t has covered. See linear_interpolation for example.\n outside_value: float\n if the value is requested outside of all the intervals sepecified in\n `endpoints` this value is returned. If None then AssertionError is\n raised when outside value is requested.\n \"\"\"\n idxes = [e[0] for e in endpoints]\n assert idxes == sorted(idxes)\n self._interpolation = interpolation\n self._outside_value = outside_value\n self._endpoints = endpoints\n\n def value(self, t):\n \"\"\"See Schedule.value\"\"\"\n for (l_t, l), (r_t, r) in zip(self._endpoints[:-1], self._endpoints[1:]):\n if l_t <= t and t < r_t:\n alpha = float(t - l_t) / (r_t - l_t)\n return self._interpolation(l, r, alpha)\n\n # t does not belong to any of the pieces, so doom.\n assert self._outside_value is not None\n return self._outside_value\n\nclass LinearSchedule(object):\n def __init__(self, schedule_timesteps, final_p, initial_p=1.0):\n \"\"\"Linear interpolation between initial_p and final_p over\n schedule_timesteps. After this many timesteps pass final_p is\n returned.\n Parameters\n ----------\n schedule_timesteps: int\n Number of timesteps for which to linearly anneal initial_p\n to final_p\n initial_p: float\n initial output value\n final_p: float\n final output value\n \"\"\"\n self.schedule_timesteps = schedule_timesteps\n self.final_p = final_p\n self.initial_p = initial_p\n\n def value(self, t):\n \"\"\"See Schedule.value\"\"\"\n fraction = min(float(t) / self.schedule_timesteps, 1.0)\n return self.initial_p + fraction * (self.final_p - self.initial_p)\n\ndef compute_exponential_averages(variables, decay):\n \"\"\"Given a list of tensorflow scalar variables\n create ops corresponding to their exponential\n averages\n Parameters\n ----------\n variables: [tf.Tensor]\n List of scalar tensors.\n Returns\n -------\n averages: [tf.Tensor]\n List of scalar tensors corresponding to averages\n of al the `variables` (in order)\n apply_op: tf.runnable\n Op to be run to update the averages with current value\n of variables.\n \"\"\"\n averager = tf.train.ExponentialMovingAverage(decay=decay)\n apply_op = averager.apply(variables)\n return [averager.average(v) for v in variables], apply_op\n\ndef minimize_and_clip(optimizer, objective, var_list, clip_val=10):\n \"\"\"Minimized `objective` using `optimizer` w.r.t. variables in\n `var_list` while ensure the norm of the gradients for each\n variable is clipped to `clip_val`\n \"\"\"\n gradients = optimizer.compute_gradients(objective, var_list=var_list)\n for i, (grad, var) in enumerate(gradients):\n if grad is not None:\n gradients[i] = (tf.clip_by_norm(grad, clip_val), var)\n return optimizer.apply_gradients(gradients)\n\ndef initialize_interdependent_variables(session, vars_list, feed_dict):\n \"\"\"Initialize a list of variables one at a time, which is useful if\n initialization of some variables depends on initialization of the others.\n \"\"\"\n vars_left = vars_list\n while len(vars_left) > 0:\n new_vars_left = []\n for v in vars_left:\n try:\n # If using an older version of TensorFlow, uncomment the line\n # below and comment out the line after it.\n\t\t#session.run(tf.initialize_variables([v]), feed_dict)\n session.run(tf.variables_initializer([v]), feed_dict)\n except tf.errors.FailedPreconditionError:\n new_vars_left.append(v)\n if len(new_vars_left) >= len(vars_left):\n # This can happend if the variables all depend on each other, or more likely if there's\n # another variable outside of the list, that still needs to be initialized. This could be\n # detected here, but life's finite.\n raise Exception(\"Cycle in variable dependencies, or extenrnal precondition unsatisfied.\")\n else:\n vars_left = new_vars_left\n\ndef get_wrapper_by_name(env, classname):\n currentenv = env\n while True:\n if classname in currentenv.__class__.__name__:\n return currentenv\n elif isinstance(env, gym.Wrapper):\n currentenv = currentenv.env\n else:\n raise ValueError(\"Couldn't find wrapper named %s\"%classname)\n\nclass ReplayBuffer(object):\n def __init__(self, size, frame_history_len):\n \"\"\"This is a memory efficient implementation of the replay buffer.\n\n The sepecific memory optimizations use here are:\n - only store each frame once rather than k times\n even if every observation normally consists of k last frames\n - store frames as np.uint8 (actually it is most time-performance\n to cast them back to float32 on GPU to minimize memory transfer\n time)\n - store frame_t and frame_(t+1) in the same buffer.\n\n For the tipical use case in Atari Deep RL buffer with 1M frames the total\n memory footprint of this buffer is 10^6 * 84 * 84 bytes ~= 7 gigabytes\n\n Warning! Assumes that returning frame of zeros at the beginning\n of the episode, when there is less frames than `frame_history_len`,\n is acceptable.\n\n Parameters\n ----------\n size: int\n Max number of transitions to store in the buffer. When the buffer\n overflows the old memories are dropped.\n frame_history_len: int\n Number of memories to be retried for each observation.\n \"\"\"\n self.size = size\n self.frame_history_len = frame_history_len\n\n self.next_idx = 0\n self.num_in_buffer = 0\n\n self.obs = None\n self.action = None\n self.reward = None\n self.done = None\n\n def can_sample(self, batch_size):\n \"\"\"Returns true if `batch_size` different transitions can be sampled from the buffer.\"\"\"\n return batch_size + 1 <= self.num_in_buffer\n\n def _encode_sample(self, idxes):\n obs_batch = np.concatenate([self._encode_observation(idx)[None] for idx in idxes], 0)\n act_batch = self.action[idxes]\n rew_batch = self.reward[idxes]\n next_obs_batch = np.concatenate([self._encode_observation(idx + 1)[None] for idx in idxes], 0)\n done_mask = np.array([1.0 if self.done[idx] else 0.0 for idx in idxes], dtype=np.float32)\n\n return obs_batch, act_batch, rew_batch, next_obs_batch, done_mask\n\n\n def sample(self, batch_size):\n \"\"\"Sample `batch_size` different transitions.\n\n i-th sample transition is the following:\n\n when observing `obs_batch[i]`, action `act_batch[i]` was taken,\n after which reward `rew_batch[i]` was received and subsequent\n observation next_obs_batch[i] was observed, unless the epsiode\n was done which is represented by `done_mask[i]` which is equal\n to 1 if episode has ended as a result of that action.\n\n Parameters\n ----------\n batch_size: int\n How many transitions to sample.\n\n Returns\n -------\n obs_batch: np.array\n Array of shape\n (batch_size, img_h, img_w, img_c * frame_history_len)\n and dtype np.uint8\n act_batch: np.array\n Array of shape (batch_size,) and dtype np.int32\n rew_batch: np.array\n Array of shape (batch_size,) and dtype np.float32\n next_obs_batch: np.array\n Array of shape\n (batch_size, img_h, img_w, img_c * frame_history_len)\n and dtype np.uint8\n done_mask: np.array\n Array of shape (batch_size,) and dtype np.float32\n \"\"\"\n assert self.can_sample(batch_size)\n idxes = sample_n_unique(lambda: random.randint(0, self.num_in_buffer - 2), batch_size)\n return self._encode_sample(idxes)\n\n def encode_recent_observation(self):\n \"\"\"Return the most recent `frame_history_len` frames.\n\n Returns\n -------\n observation: np.array\n Array of shape (img_h, img_w, img_c * frame_history_len)\n and dtype np.uint8, where observation[:, :, i*img_c:(i+1)*img_c]\n encodes frame at time `t - frame_history_len + i`\n \"\"\"\n assert self.num_in_buffer > 0\n return self._encode_observation((self.next_idx - 1) % self.size)\n\n def _encode_observation(self, idx):\n end_idx = idx + 1 # make noninclusive\n start_idx = end_idx - self.frame_history_len\n # this checks if we are using low-dimensional observations, such as RAM\n # state, in which case we just directly return the latest RAM.\n if len(self.obs.shape) == 2:\n return self.obs[end_idx-1]\n # if there weren't enough frames ever in the buffer for context\n if start_idx < 0 and self.num_in_buffer != self.size:\n start_idx = 0\n for idx in range(start_idx, end_idx - 1):\n if self.done[idx % self.size]:\n start_idx = idx + 1\n missing_context = self.frame_history_len - (end_idx - start_idx)\n # if zero padding is needed for missing context\n # or we are on the boundry of the buffer\n if start_idx < 0 or missing_context > 0:\n frames = [np.zeros_like(self.obs[0]) for _ in range(missing_context)]\n for idx in range(start_idx, end_idx):\n frames.append(self.obs[idx % self.size])\n return np.concatenate(frames, 2)\n else:\n # this optimization has potential to saves about 30% compute time \\o/\n img_h, img_w = self.obs.shape[1], self.obs.shape[2]\n return self.obs[start_idx:end_idx].transpose(1, 2, 0, 3).reshape(img_h, img_w, -1)\n\n def store_frame(self, frame):\n \"\"\"Store a single frame in the buffer at the next available index, overwriting\n old frames if necessary.\n\n Parameters\n ----------\n frame: np.array\n Array of shape (img_h, img_w, img_c) and dtype np.uint8\n the frame to be stored\n\n Returns\n -------\n idx: int\n Index at which the frame is stored. To be used for `store_effect` later.\n \"\"\"\n if self.obs is None:\n self.obs = np.empty([self.size] + list(frame.shape), dtype=np.uint8)\n self.action = np.empty([self.size], dtype=np.int32)\n self.reward = np.empty([self.size], dtype=np.float32)\n self.done = np.empty([self.size], dtype=np.bool)\n self.obs[self.next_idx] = frame\n\n ret = self.next_idx\n self.next_idx = (self.next_idx + 1) % self.size\n self.num_in_buffer = min(self.size, self.num_in_buffer + 1)\n\n return ret\n\n def store_effect(self, idx, action, reward, done):\n \"\"\"Store effects of action taken after obeserving frame stored\n at index idx. The reason `store_frame` and `store_effect` is broken\n up into two functions is so that once can call `encode_recent_observation`\n in between.\n\n Paramters\n ---------\n idx: int\n Index in buffer of recently observed frame (returned by `store_frame`).\n action: int\n Action that was performed upon observing this frame.\n reward: float\n Reward that was received when the actions was performed.\n done: bool\n True if episode was finished after performing that action.\n \"\"\"\n self.action[idx] = action\n self.reward[idx] = reward\n self.done[idx] = done\n\n"
},
{
"path": "hw3/log/_RAM_30-01-2018_15-20-56/log.txt",
"content": "Timestep\tMeanReward\tBestMeanReward\tepisodes\texploration\tlearning_rate\n60000\t-20.69\t-20.57\t207\t0.194\t0.0001\n70000\t-20.72\t-20.57\t243\t0.193\t0.0001\n80000\t-20.79\t-20.57\t279\t0.192\t0.0001\n90000\t-20.79\t-20.57\t315\t0.191\t0.0001\n100000\t-20.76\t-20.57\t350\t0.19\t0.0001\n110000\t-20.71\t-20.57\t386\t0.189\t0.0001\n120000\t-20.72\t-20.57\t424\t0.188\t0.0001\n130000\t-20.76\t-20.57\t460\t0.187\t0.0001\n140000\t-20.84\t-20.57\t496\t0.186\t0.0001\n150000\t-20.72\t-20.57\t531\t0.185\t0.0001\n160000\t-20.71\t-20.57\t565\t0.184\t0.0001\n170000\t-20.59\t-20.57\t598\t0.183\t0.0001\n180000\t-20.56\t-20.56\t631\t0.18200000000000002\t0.0001\n190000\t-20.33\t-20.33\t663\t0.181\t0.0001\n200000\t-20.08\t-20.07\t693\t0.18\t0.0001\n210000\t-19.95\t-19.95\t724\t0.17900000000000002\t0.0001\n220000\t-19.94\t-19.94\t756\t0.17800000000000002\t0.0001\n230000\t-19.94\t-19.83\t786\t0.17700000000000002\t0.0001\n240000\t-19.86\t-19.82\t815\t0.17600000000000002\t0.0001\n250000\t-19.57\t-19.57\t843\t0.17500000000000002\t0.0001\n260000\t-19.55\t-19.48\t871\t0.17400000000000002\t0.0001\n270000\t-19.37\t-19.37\t897\t0.17300000000000001\t0.0001\n280000\t-19.4\t-19.36\t926\t0.17200000000000001\t0.0001\n290000\t-19.32\t-19.29\t951\t0.171\t0.0001\n300000\t-19.16\t-19.16\t978\t0.17\t0.0001\n310000\t-19.11\t-19.09\t1002\t0.169\t0.0001\n320000\t-19.02\t-19.01\t1027\t0.168\t0.0001\n330000\t-19.01\t-18.95\t1051\t0.167\t0.0001\n340000\t-18.81\t-18.78\t1074\t0.166\t0.0001\n350000\t-18.65\t-18.65\t1098\t0.165\t0.0001\n360000\t-18.56\t-18.54\t1122\t0.164\t0.0001\n370000\t-18.55\t-18.49\t1146\t0.163\t0.0001\n380000\t-18.58\t-18.42\t1168\t0.162\t0.0001\n390000\t-18.46\t-18.42\t1190\t0.161\t0.0001\n400000\t-18.54\t-18.42\t1211\t0.16\t0.0001\n410000\t-18.4\t-18.4\t1234\t0.159\t0.0001\n420000\t-18.17\t-18.17\t1255\t0.158\t0.0001\n430000\t-18.18\t-18.15\t1275\t0.157\t0.0001\n440000\t-18.16\t-18.06\t1296\t0.156\t0.0001\n450000\t-17.92\t-17.88\t1316\t0.155\t0.0001\n460000\t-17.74\t-17.74\t1336\t0.154\t0.0001\n470000\t-17.64\t-17.61\t1356\t0.15300000000000002\t0.0001\n480000\t-17.57\t-17.57\t1378\t0.15200000000000002\t0.0001\n490000\t-17.53\t-17.49\t1398\t0.15100000000000002\t0.0001\n500000\t-17.6\t-17.45\t1419\t0.15000000000000002\t0.0001\n510000\t-17.64\t-17.45\t1440\t0.14900000000000002\t0.0001\n520000\t-17.79\t-17.45\t1460\t0.14800000000000002\t0.0001\n530000\t-17.57\t-17.45\t1479\t0.14700000000000002\t0.0001\n540000\t-17.64\t-17.45\t1500\t0.14600000000000002\t0.0001\n550000\t-17.39\t-17.39\t1518\t0.14500000000000002\t0.0001\n560000\t-17.32\t-17.32\t1536\t0.14400000000000002\t0.0001\n570000\t-17.18\t-17.13\t1555\t0.14300000000000002\t0.0001\n580000\t-17.29\t-17.08\t1574\t0.14200000000000002\t0.0001\n590000\t-17.08\t-17.08\t1591\t0.14100000000000001\t0.0001\n600000\t-17.08\t-16.99\t1610\t0.14\t0.0001\n610000\t-17.1\t-16.99\t1628\t0.139\t0.0001\n620000\t-17.01\t-16.94\t1647\t0.138\t0.0001\n630000\t-17.1\t-16.94\t1667\t0.137\t0.0001\n640000\t-17.0\t-16.94\t1687\t0.136\t0.0001\n650000\t-17.12\t-16.94\t1706\t0.135\t0.0001\n660000\t-17.07\t-16.94\t1724\t0.134\t0.0001\n670000\t-17.1\t-16.94\t1744\t0.133\t0.0001\n680000\t-17.02\t-16.94\t1763\t0.132\t0.0001\n690000\t-17.16\t-16.94\t1783\t0.131\t0.0001\n700000\t-17.2\t-16.94\t1802\t0.13\t0.0001\n710000\t-17.47\t-16.94\t1823\t0.129\t0.0001\n720000\t-17.56\t-16.94\t1842\t0.128\t0.0001\n730000\t-17.6\t-16.94\t1862\t0.127\t0.0001\n740000\t-17.5\t-16.94\t1881\t0.126\t0.0001\n750000\t-17.45\t-16.94\t1900\t0.125\t0.0001\n760000\t-17.2\t-16.94\t1919\t0.124\t0.0001\n770000\t-17.12\t-16.94\t1938\t0.123\t0.0001\n780000\t-17.2\t-16.94\t1957\t0.122\t0.0001\n790000\t-17.26\t-16.94\t1976\t0.121\t0.0001\n800000\t-17.21\t-16.94\t1993\t0.12\t0.0001\n810000\t-17.17\t-16.94\t2011\t0.119\t0.0001\n820000\t-17.1\t-16.94\t2028\t0.11800000000000001\t0.0001\n830000\t-16.99\t-16.94\t2045\t0.117\t0.0001\n840000\t-16.7\t-16.7\t2062\t0.116\t0.0001\n850000\t-16.52\t-16.52\t2078\t0.115\t0.0001\n860000\t-16.59\t-16.49\t2095\t0.114\t0.0001\n870000\t-16.69\t-16.49\t2113\t0.113\t0.0001\n880000\t-16.69\t-16.49\t2130\t0.112\t0.0001\n890000\t-16.79\t-16.49\t2146\t0.111\t0.0001\n900000\t-16.9\t-16.49\t2163\t0.11\t0.0001\n910000\t-17.09\t-16.49\t2181\t0.109\t0.0001\n920000\t-17.05\t-16.49\t2198\t0.108\t0.0001\n930000\t-16.97\t-16.49\t2214\t0.107\t0.0001\n940000\t-17.13\t-16.49\t2232\t0.10600000000000001\t0.0001\n950000\t-17.19\t-16.49\t2249\t0.10500000000000001\t0.0001\n960000\t-17.01\t-16.49\t2265\t0.10400000000000001\t0.0001\n970000\t-16.83\t-16.49\t2280\t0.10300000000000001\t0.0001\n980000\t-16.9\t-16.49\t2298\t0.10200000000000001\t0.0001\n990000\t-16.79\t-16.49\t2314\t0.101\t0.0001\n1000000\t-16.73\t-16.49\t2330\t0.1\t0.0001\n1010000\t-16.69\t-16.49\t2348\t0.099775\t9.9875e-05\n1020000\t-16.92\t-16.49\t2364\t0.09955\t9.975e-05\n1030000\t-16.98\t-16.49\t2381\t0.09932500000000001\t9.962500000000001e-05\n1040000\t-16.96\t-16.49\t2396\t0.09910000000000001\t9.95e-05\n1050000\t-17.1\t-16.49\t2413\t0.098875\t9.9375e-05\n1060000\t-16.74\t-1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{
"path": "hw3/log/_RAM_30-01-2018_22-29-12/log.txt",
"content": "Timestep\tMeanReward\tBestMeanReward\tepisodes\texploration\tlearning_rate\n60000\t-20.73\t-20.57\t208\t0.194\t0.0001\n70000\t-20.8\t-20.57\t247\t0.193\t0.0001\n80000\t-20.82\t-20.57\t285\t0.192\t0.0001\n90000\t-20.86\t-20.57\t325\t0.191\t0.0001\n100000\t-20.93\t-20.57\t365\t0.19\t0.0001\n110000\t-20.93\t-20.57\t404\t0.189\t0.0001\n120000\t-20.94\t-20.57\t444\t0.188\t0.0001\n130000\t-20.97\t-20.57\t483\t0.187\t0.0001\n140000\t-20.93\t-20.57\t523\t0.186\t0.0001\n150000\t-20.91\t-20.57\t562\t0.185\t0.0001\n160000\t-20.91\t-20.57\t602\t0.184\t0.0001\n170000\t-20.96\t-20.57\t641\t0.183\t0.0001\n180000\t-20.96\t-20.57\t681\t0.18200000000000002\t0.0001\n190000\t-20.99\t-20.57\t721\t0.181\t0.0001\n200000\t-20.97\t-20.57\t761\t0.18\t0.0001\n210000\t-20.95\t-20.57\t800\t0.17900000000000002\t0.0001\n220000\t-20.95\t-20.57\t840\t0.17800000000000002\t0.0001\n230000\t-20.98\t-20.57\t880\t0.17700000000000002\t0.0001\n240000\t-20.98\t-20.57\t920\t0.17600000000000002\t0.0001\n250000\t-20.97\t-20.57\t960\t0.17500000000000002\t0.0001\n260000\t-20.98\t-20.57\t1000\t0.17400000000000002\t0.0001\n270000\t-20.97\t-20.57\t1039\t0.17300000000000001\t0.0001\n280000\t-20.97\t-20.57\t1079\t0.17200000000000001\t0.0001\n290000\t-20.96\t-20.57\t1119\t0.171\t0.0001\n300000\t-20.98\t-20.57\t1159\t0.17\t0.0001\n310000\t-20.99\t-20.57\t1199\t0.169\t0.0001\n320000\t-20.98\t-20.57\t1238\t0.168\t0.0001\n330000\t-20.99\t-20.57\t1278\t0.167\t0.0001\n340000\t-21.0\t-20.57\t1318\t0.166\t0.0001\n350000\t-21.0\t-20.57\t1358\t0.165\t0.0001\n360000\t-21.0\t-20.57\t1398\t0.164\t0.0001\n370000\t-20.99\t-20.57\t1438\t0.163\t0.0001\n380000\t-20.98\t-20.57\t1478\t0.162\t0.0001\n390000\t-20.98\t-20.57\t1518\t0.161\t0.0001\n400000\t-20.96\t-20.57\t1557\t0.16\t0.0001\n410000\t-20.96\t-20.57\t1597\t0.159\t0.0001\n420000\t-20.95\t-20.57\t1636\t0.158\t0.0001\n430000\t-20.98\t-20.57\t1676\t0.157\t0.0001\n440000\t-20.98\t-20.57\t1716\t0.156\t0.0001\n450000\t-20.95\t-20.57\t1755\t0.155\t0.0001\n460000\t-20.96\t-20.57\t1795\t0.154\t0.0001\n470000\t-20.94\t-20.57\t1832\t0.15300000000000002\t0.0001\n480000\t-20.93\t-20.57\t1871\t0.15200000000000002\t0.0001\n490000\t-20.9\t-20.57\t1910\t0.15100000000000002\t0.0001\n500000\t-20.96\t-20.57\t1949\t0.15000000000000002\t0.0001\n510000\t-20.94\t-20.57\t1988\t0.14900000000000002\t0.0001\n520000\t-20.92\t-20.57\t2027\t0.14800000000000002\t0.0001\n530000\t-20.93\t-20.57\t2066\t0.14700000000000002\t0.0001\n540000\t-20.97\t-20.57\t2106\t0.14600000000000002\t0.0001\n550000\t-20.97\t-20.57\t2145\t0.14500000000000002\t0.0001\n560000\t-20.94\t-20.57\t2184\t0.14400000000000002\t0.0001\n570000\t-20.92\t-20.57\t2222\t0.14300000000000002\t0.0001\n580000\t-20.91\t-20.57\t2261\t0.14200000000000002\t0.0001\n590000\t-20.89\t-20.57\t2298\t0.14100000000000001\t0.0001\n600000\t-20.89\t-20.57\t2337\t0.14\t0.0001\n610000\t-20.92\t-20.57\t2376\t0.139\t0.0001\n620000\t-20.94\t-20.57\t2415\t0.138\t0.0001\n630000\t-20.94\t-20.57\t2454\t0.137\t0.0001\n640000\t-20.97\t-20.57\t2493\t0.136\t0.0001\n650000\t-20.93\t-20.57\t2532\t0.135\t0.0001\n660000\t-20.84\t-20.57\t2568\t0.134\t0.0001\n670000\t-20.75\t-20.57\t2604\t0.133\t0.0001\n680000\t-20.73\t-20.57\t2637\t0.132\t0.0001\n690000\t-20.74\t-20.57\t2669\t0.131\t0.0001\n700000\t-20.76\t-20.57\t2701\t0.13\t0.0001\n710000\t-20.76\t-20.57\t2733\t0.129\t0.0001\n720000\t-20.78\t-20.57\t2764\t0.128\t0.0001\n730000\t-20.73\t-20.57\t2793\t0.127\t0.0001\n740000\t-20.7\t-20.57\t2825\t0.126\t0.0001\n750000\t-20.67\t-20.57\t2857\t0.125\t0.0001\n760000\t-20.67\t-20.57\t2886\t0.124\t0.0001\n770000\t-20.72\t-20.57\t2918\t0.123\t0.0001\n780000\t-20.76\t-20.57\t2949\t0.122\t0.0001\n790000\t-20.82\t-20.57\t2980\t0.121\t0.0001\n800000\t-20.82\t-20.57\t3009\t0.12\t0.0001\n810000\t-20.71\t-20.57\t3039\t0.119\t0.0001\n820000\t-20.68\t-20.57\t3070\t0.11800000000000001\t0.0001\n830000\t-20.63\t-20.57\t3097\t0.117\t0.0001\n840000\t-20.72\t-20.57\t3128\t0.116\t0.0001\n850000\t-20.77\t-20.57\t3158\t0.115\t0.0001\n860000\t-20.82\t-20.57\t3186\t0.114\t0.0001\n870000\t-20.75\t-20.57\t3212\t0.113\t0.0001\n880000\t-20.65\t-20.57\t3238\t0.112\t0.0001\n890000\t-20.52\t-20.52\t3263\t0.111\t0.0001\n900000\t-20.47\t-20.47\t3290\t0.11\t0.0001\n910000\t-20.4\t-20.38\t3313\t0.109\t0.0001\n920000\t-20.35\t-20.32\t3337\t0.108\t0.0001\n930000\t-20.32\t-20.31\t3360\t0.107\t0.0001\n940000\t-20.29\t-20.29\t3384\t0.10600000000000001\t0.0001\n950000\t-20.26\t-20.25\t3405\t0.10500000000000001\t0.0001\n960000\t-20.22\t-20.16\t3429\t0.10400000000000001\t0.0001\n970000\t-20.2\t-20.16\t3452\t0.10300000000000001\t0.0001\n980000\t-19.99\t-19.99\t3474\t0.10200000000000001\t0.0001\n990000\t-19.88\t-19.87\t3496\t0.101\t0.0001\n1000000\t-19.87\t-19.84\t3518\t0.1\t0.0001\n1010000\t-19.73\t-19.72\t3539\t0.099775\t9.9875e-05\n1020000\t-19.63\t-19.61\t3560\t0.09955\t9.975e-05\n1030000\t-19.72\t-19.61\t3583\t0.09932500000000001\t9.962500000000001e-05\n1040000\t-19.72\t-19.61\t3607\t0.09910000000000001\t9.95e-05\n1050000\t-19.95\t-19.61\t3629\t0.098875\t9.9375e-05\n106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},
{
"path": "hw3/log/_RAM_31-01-2018_08-28-28/log.txt",
"content": "Timestep\tMeanReward\tBestMeanReward\tepisodes\texploration\tlearning_rate\n60000\t-20.72\t-20.57\t207\t0.194\t0.0001\n70000\t-20.74\t-20.57\t242\t0.193\t0.0001\n80000\t-20.71\t-20.57\t276\t0.192\t0.0001\n90000\t-20.53\t-20.52\t308\t0.191\t0.0001\n100000\t-20.4\t-20.4\t341\t0.19\t0.0001\n110000\t-20.22\t-20.22\t372\t0.189\t0.0001\n120000\t-20.2\t-20.18\t405\t0.188\t0.0001\n130000\t-20.11\t-20.11\t436\t0.187\t0.0001\n140000\t-20.19\t-20.09\t467\t0.186\t0.0001\n150000\t-20.02\t-20.02\t496\t0.185\t0.0001\n160000\t-19.88\t-19.86\t526\t0.184\t0.0001\n170000\t-19.73\t-19.73\t554\t0.183\t0.0001\n180000\t-19.44\t-19.44\t581\t0.18200000000000002\t0.0001\n190000\t-19.34\t-19.34\t607\t0.181\t0.0001\n200000\t-19.13\t-19.13\t633\t0.18\t0.0001\n210000\t-19.14\t-19.0\t661\t0.17900000000000002\t0.0001\n220000\t-19.27\t-19.0\t688\t0.17800000000000002\t0.0001\n230000\t-19.08\t-19.0\t714\t0.17700000000000002\t0.0001\n240000\t-18.94\t-18.94\t739\t0.17600000000000002\t0.0001\n250000\t-18.89\t-18.85\t765\t0.17500000000000002\t0.0001\n260000\t-18.55\t-18.55\t789\t0.17400000000000002\t0.0001\n270000\t-18.63\t-18.54\t813\t0.17300000000000001\t0.0001\n280000\t-18.52\t-18.45\t837\t0.17200000000000001\t0.0001\n290000\t-18.13\t-18.13\t860\t0.171\t0.0001\n300000\t-18.24\t-18.01\t886\t0.17\t0.0001\n310000\t-18.09\t-18.01\t909\t0.169\t0.0001\n320000\t-17.91\t-17.86\t932\t0.168\t0.0001\n330000\t-17.86\t-17.86\t955\t0.167\t0.0001\n340000\t-17.67\t-17.67\t977\t0.166\t0.0001\n350000\t-17.68\t-17.51\t1000\t0.165\t0.0001\n360000\t-18.05\t-17.51\t1025\t0.164\t0.0001\n370000\t-17.99\t-17.51\t1048\t0.163\t0.0001\n380000\t-18.11\t-17.51\t1071\t0.162\t0.0001\n390000\t-18.05\t-17.51\t1094\t0.161\t0.0001\n400000\t-17.78\t-17.51\t1116\t0.16\t0.0001\n410000\t-17.59\t-17.51\t1139\t0.159\t0.0001\n420000\t-17.55\t-17.51\t1161\t0.158\t0.0001\n430000\t-17.51\t-17.43\t1183\t0.157\t0.0001\n440000\t-17.6\t-17.43\t1206\t0.156\t0.0001\n450000\t-17.37\t-17.32\t1228\t0.155\t0.0001\n460000\t-17.53\t-17.32\t1251\t0.154\t0.0001\n470000\t-17.55\t-17.32\t1273\t0.15300000000000002\t0.0001\n480000\t-17.4\t-17.32\t1296\t0.15200000000000002\t0.0001\n490000\t-17.33\t-17.32\t1318\t0.15100000000000002\t0.0001\n500000\t-17.27\t-17.26\t1339\t0.15000000000000002\t0.0001\n510000\t-17.17\t-17.04\t1360\t0.14900000000000002\t0.0001\n520000\t-17.1\t-16.9\t1382\t0.14800000000000002\t0.0001\n530000\t-16.95\t-16.9\t1404\t0.14700000000000002\t0.0001\n540000\t-17.21\t-16.86\t1427\t0.14600000000000002\t0.0001\n550000\t-17.23\t-16.86\t1449\t0.14500000000000002\t0.0001\n560000\t-17.38\t-16.86\t1471\t0.14400000000000002\t0.0001\n570000\t-17.44\t-16.86\t1495\t0.14300000000000002\t0.0001\n580000\t-17.33\t-16.86\t1516\t0.14200000000000002\t0.0001\n590000\t-17.03\t-16.86\t1537\t0.14100000000000001\t0.0001\n600000\t-17.02\t-16.86\t1559\t0.14\t0.0001\n610000\t-17.12\t-16.86\t1582\t0.139\t0.0001\n620000\t-17.18\t-16.86\t1604\t0.138\t0.0001\n630000\t-17.14\t-16.86\t1625\t0.137\t0.0001\n640000\t-17.23\t-16.86\t1647\t0.136\t0.0001\n650000\t-17.24\t-16.86\t1669\t0.135\t0.0001\n660000\t-17.09\t-16.86\t1689\t0.134\t0.0001\n670000\t-17.02\t-16.86\t1711\t0.133\t0.0001\n680000\t-17.04\t-16.86\t1732\t0.132\t0.0001\n690000\t-17.06\t-16.86\t1753\t0.131\t0.0001\n700000\t-16.99\t-16.86\t1775\t0.13\t0.0001\n710000\t-17.03\t-16.8\t1796\t0.129\t0.0001\n720000\t-17.02\t-16.8\t1818\t0.128\t0.0001\n730000\t-16.97\t-16.8\t1838\t0.127\t0.0001\n740000\t-16.75\t-16.75\t1858\t0.126\t0.0001\n750000\t-16.83\t-16.6\t1879\t0.125\t0.0001\n760000\t-16.64\t-16.6\t1900\t0.124\t0.0001\n770000\t-16.81\t-16.6\t1922\t0.123\t0.0001\n780000\t-16.89\t-16.6\t1942\t0.122\t0.0001\n790000\t-16.93\t-16.6\t1963\t0.121\t0.0001\n800000\t-16.82\t-16.6\t1983\t0.12\t0.0001\n810000\t-16.78\t-16.6\t2004\t0.119\t0.0001\n820000\t-16.4\t-16.4\t2023\t0.11800000000000001\t0.0001\n830000\t-16.41\t-16.36\t2043\t0.117\t0.0001\n840000\t-16.49\t-16.34\t2064\t0.116\t0.0001\n850000\t-16.32\t-16.3\t2082\t0.115\t0.0001\n860000\t-16.18\t-16.16\t2102\t0.114\t0.0001\n870000\t-16.43\t-16.12\t2121\t0.113\t0.0001\n880000\t-16.24\t-16.12\t2141\t0.112\t0.0001\n890000\t-16.25\t-16.1\t2160\t0.111\t0.0001\n900000\t-16.45\t-16.1\t2180\t0.11\t0.0001\n910000\t-16.41\t-16.1\t2198\t0.109\t0.0001\n920000\t-16.37\t-16.1\t2217\t0.108\t0.0001\n930000\t-16.33\t-16.1\t2235\t0.107\t0.0001\n940000\t-16.46\t-16.1\t2255\t0.10600000000000001\t0.0001\n950000\t-16.1\t-16.04\t2274\t0.10500000000000001\t0.0001\n960000\t-16.15\t-16.03\t2293\t0.10400000000000001\t0.0001\n970000\t-16.2\t-16.03\t2313\t0.10300000000000001\t0.0001\n980000\t-16.17\t-16.01\t2333\t0.10200000000000001\t0.0001\n990000\t-15.9\t-15.9\t2352\t0.101\t0.0001\n1000000\t-16.14\t-15.88\t2371\t0.1\t0.0001\n1010000\t-16.09\t-15.88\t2391\t0.099775\t9.9875e-05\n1020000\t-16.07\t-15.88\t2410\t0.09955\t9.975e-05\n1030000\t-16.1\t-15.88\t2430\t0.09932500000000001\t9.962500000000001e-05\n1040000\t-15.91\t-15.88\t2448\t0.09910000000000001\t9.95e-05\n1050000\t-15.84\t-15.79\t2468\t0.098875\t9.9375e-05\n1060000\t-15.71\t-15.71\t2486\t0.09865\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},
{
"path": "hw3/logz.py",
"content": "import json\n\n\"\"\"\n\nSome simple logging functionality, inspired by rllab's logging.\nAssumes that each diagnostic gets logged each iteration\n\nCall logz.configure_output_dir() to start logging to a \ntab-separated-values file (some_folder_name/log.txt)\n\nTo load the learning curves, you can do, for example\n\nA = np.genfromtxt('/tmp/expt_1468984536/log.txt',delimiter='\\t',dtype=None, names=True)\nA['EpRewMean']\n\n\"\"\"\n\nimport os.path as osp, shutil, time, atexit, os, subprocess\nimport pickle\nimport tensorflow as tf\n\ncolor2num = dict(\n gray=30,\n red=31,\n green=32,\n yellow=33,\n blue=34,\n magenta=35,\n cyan=36,\n white=37,\n crimson=38\n)\n\ndef colorize(string, color, bold=False, highlight=False):\n attr = []\n num = color2num[color]\n if highlight: num += 10\n attr.append(str(num))\n if bold: attr.append('1')\n return '\\x1b[%sm%s\\x1b[0m' % (';'.join(attr), string)\n\nclass G:\n output_dir = None\n output_file = None\n first_row = True\n log_headers = []\n log_current_row = {}\n\ndef configure_output_dir(d=None):\n \"\"\"\n Set output directory to d, or to /tmp/somerandomnumber if d is None\n \"\"\"\n G.output_dir = d or \"/tmp/experiments/%i\"%int(time.time())\n assert not osp.exists(G.output_dir), \"Log dir %s already exists! Delete it first or use a different dir\"%G.output_dir\n os.makedirs(G.output_dir)\n G.output_file = open(osp.join(G.output_dir, \"log.txt\"), 'w')\n atexit.register(G.output_file.close)\n print(colorize(\"Logging data to %s\"%G.output_file.name, 'green', bold=True))\n\ndef log_tabular(key, val):\n \"\"\"\n Log a value of some diagnostic\n Call this once for each diagnostic quantity, each iteration\n \"\"\"\n if G.first_row:\n G.log_headers.append(key)\n else:\n assert key in G.log_headers, \"Trying to introduce a new key %s that you didn't include in the first iteration\"%key\n assert key not in G.log_current_row, \"You already set %s this iteration. Maybe you forgot to call dump_tabular()\"%key\n G.log_current_row[key] = val\n\ndef save_params(params):\n with open(osp.join(G.output_dir, \"params.json\"), 'w') as out:\n out.write(json.dumps(params, separators=(',\\n','\\t:\\t'), sort_keys=True))\n\ndef pickle_tf_vars(): \n \"\"\"\n Saves tensorflow variables\n Requires them to be initialized first, also a default session must exist\n \"\"\"\n _dict = {v.name : v.eval() for v in tf.global_variables()}\n with open(osp.join(G.output_dir, \"vars.pkl\"), 'wb') as f:\n pickle.dump(_dict, f)\n \n\ndef dump_tabular():\n \"\"\"\n Write all of the diagnostics from the current iteration\n \"\"\"\n vals = []\n key_lens = [len(key) for key in G.log_headers]\n max_key_len = max(15,max(key_lens))\n keystr = '%'+'%d'%max_key_len\n fmt = \"| \" + keystr + \"s | %15s |\"\n n_slashes = 22 + max_key_len\n print(\"-\"*n_slashes)\n for key in G.log_headers:\n val = G.log_current_row.get(key, \"\")\n if hasattr(val, \"__float__\"): valstr = \"%8.3g\"%val\n else: valstr = val\n print(fmt%(key, valstr))\n vals.append(val)\n print(\"-\"*n_slashes)\n if G.output_file is not None:\n if G.first_row:\n G.output_file.write(\"\\t\".join(G.log_headers))\n G.output_file.write(\"\\n\")\n G.output_file.write(\"\\t\".join(map(str,vals)))\n G.output_file.write(\"\\n\")\n G.output_file.flush()\n G.log_current_row.clear()\n G.first_row=False\n"
},
{
"path": "hw3/plot.py",
"content": "import seaborn as sns\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport json\nimport os\n\n\"\"\"\nUsing the plotter:\n\nCall it from the command line, and supply it with logdirs to experiments.\nSuppose you ran an experiment with name 'test', and you ran 'test' for 10 \nrandom seeds. The runner code stored it in the directory structure\n\n data\n L test_EnvName_DateTime\n L 0\n L log.txt\n L params.json\n L 1\n L log.txt\n L params.json\n .\n .\n .\n L 9\n L log.txt\n L params.json\n\nTo plot learning curves from the experiment, averaged over all random\nseeds, call\n\n python plot.py data/test_EnvName_DateTime --value AverageReturn\n\nand voila. To see a different statistics, change what you put in for\nthe keyword --value. You can also enter /multiple/ values, and it will \nmake all of them in order.\n\n\nSuppose you ran two experiments: 'test1' and 'test2'. In 'test2' you tried\na different set of hyperparameters from 'test1', and now you would like \nto compare them -- see their learning curves side-by-side. Just call\n\n python plot.py data/test1 data/test2\n\nand it will plot them both! They will be given titles in the legend according\nto their exp_name parameters. If you want to use custom legend titles, use\nthe --legend flag and then provide a title for each logdir.\n\n\"\"\"\n\ndef plot_data(data, value=\"MeanReward\"):\n if isinstance(data, list):\n data = pd.concat(data, ignore_index=True)\n sns.set(style=\"darkgrid\", font_scale=1.5)\n sns.tsplot(data=data, time=\"Timestep\", value=value, unit=\"Unit\", condition=\"Condition\")\n sns.tsplot(data=data, time=\"Timestep\", value=\"BestMeanReward\", unit=\"Unit\", condition=\"Condition\")\n plt.legend(loc='best').draggable()\n plt.show()\n\n\ndef get_datasets(fpath, condition=None):\n unit = 0\n datasets = []\n for root, dir, files in os.walk(fpath):\n if 'log.txt' in files:\n # param_path = open(os.path.join(root,'params.json'))\n # params = json.load(param_path)\n exp_name = fpath\n \n log_path = os.path.join(root,'log.txt')\n experiment_data = pd.read_table(log_path)\n\n experiment_data.insert(\n len(experiment_data.columns),\n 'Unit',\n unit\n )\n experiment_data.insert(\n len(experiment_data.columns),\n 'Condition',\n condition or exp_name\n )\n\n datasets.append(experiment_data)\n unit += 1\n\n return datasets\n\n\ndef main():\n import argparse\n parser = argparse.ArgumentParser()\n parser.add_argument('logdir', nargs='*')\n parser.add_argument('--legend', nargs='*')\n parser.add_argument('--value', default='MeanReward', nargs='*')\n args = parser.parse_args()\n\n use_legend = False\n if args.legend is not None:\n assert len(args.legend) == len(args.logdir), \\\n \"Must give a legend title for each set of experiments.\"\n use_legend = True\n\n data = []\n if use_legend:\n for logdir, legend_title in zip(args.logdir, args.legend):\n data += get_datasets(logdir, legend_title)\n else:\n for logdir in args.logdir:\n data += get_datasets(logdir)\n\n if isinstance(args.value, list):\n values = args.value\n else:\n values = [args.value]\n for value in values:\n plot_data(data, value=value)\n\nif __name__ == \"__main__\":\n main()\n"
},
{
"path": "hw3/run_dqn_atari.py",
"content": "import argparse\nimport gym\nfrom gym import wrappers\nimport os.path as osp\nimport random\nimport numpy as np\nimport tensorflow as tf\nimport logz\nimport os\nimport time\nimport tensorflow.contrib.layers as layers\n\nimport dqn\nfrom dqn_utils import *\nfrom atari_wrappers import *\n\n\ndef atari_model(img_in, num_actions, scope, reuse=False):\n # as described in https://storage.googleapis.com/deepmind-data/assets/papers/DeepMindNature14236Paper.pdf\n with tf.variable_scope(scope, reuse=reuse):\n out = img_in\n with tf.variable_scope(\"convnet\"):\n # original architecture\n out = layers.convolution2d(out, num_outputs=32, kernel_size=8, stride=4, activation_fn=tf.nn.relu)\n out = layers.convolution2d(out, num_outputs=64, kernel_size=4, stride=2, activation_fn=tf.nn.relu)\n out = layers.convolution2d(out, num_outputs=64, kernel_size=3, stride=1, activation_fn=tf.nn.relu)\n out = layers.flatten(out)\n with tf.variable_scope(\"action_value\"):\n out = layers.fully_connected(out, num_outputs=512, activation_fn=tf.nn.relu)\n out = layers.fully_connected(out, num_outputs=num_actions, activation_fn=None)\n\n return out\n\ndef atari_learn(env,\n session,\n num_timesteps):\n # This is just a rough estimate\n num_iterations = float(num_timesteps) / 4.0\n\n lr_multiplier = 1.0\n lr_schedule = PiecewiseSchedule([\n (0, 1e-4 * lr_multiplier),\n (num_iterations / 10, 1e-4 * lr_multiplier),\n (num_iterations / 2, 5e-5 * lr_multiplier),\n ],\n outside_value=5e-5 * lr_multiplier)\n optimizer = dqn.OptimizerSpec(\n constructor=tf.train.AdamOptimizer,\n kwargs=dict(epsilon=1e-4),\n lr_schedule=lr_schedule\n )\n\n def stopping_criterion(env, t):\n # notice that here t is the number of steps of the wrapped env,\n # which is different from the number of steps in the underlying env\n return get_wrapper_by_name(env, \"Monitor\").get_total_steps() >= num_timesteps\n\n exploration_schedule = PiecewiseSchedule(\n [\n (0, 1.0),\n (1e6, 0.1),\n (num_iterations / 2, 0.01),\n ], outside_value=0.01\n )\n\n dqn.learn(\n env,\n q_func=atari_model,\n optimizer_spec=optimizer,\n session=session,\n exploration=exploration_schedule,\n stopping_criterion=stopping_criterion,\n replay_buffer_size=1000000,\n batch_size=32,\n gamma=0.99,\n learning_starts=50000,\n learning_freq=4,\n frame_history_len=4,\n target_update_freq=10000,\n grad_norm_clipping=10\n )\n env.close()\n\ndef get_available_gpus():\n from tensorflow.python.client import device_lib\n local_device_protos = device_lib.list_local_devices()\n return [x.physical_device_desc for x in local_device_protos if x.device_type == 'GPU']\n\ndef set_global_seeds(i):\n try:\n import tensorflow as tf\n except ImportError:\n pass\n else:\n tf.set_random_seed(i) \n np.random.seed(i)\n random.seed(i)\n\ndef get_session():\n tf.reset_default_graph()\n tf_config = tf.ConfigProto(\n inter_op_parallelism_threads=1,\n intra_op_parallelism_threads=1)\n session = tf.Session(config=tf_config)\n print(\"AVAILABLE GPUS: \", get_available_gpus())\n return session\n\ndef get_env(task, seed):\n env_id = task.env_id\n\n env = gym.make(env_id)\n\n set_global_seeds(seed)\n env.seed(seed)\n\n expt_dir = '/tmp/hw3_vid_dir2/'\n env = wrappers.Monitor(env, osp.join(expt_dir, \"gym\"), force=True)\n env = wrap_deepmind(env)\n\n return env\n\ndef main():\n # Get Atari games.\n benchmark = gym.benchmark_spec('Atari40M')\n\n # Change the index to select a different game.\n task = benchmark.tasks[3]\n PROJECT_ROOT = os.path.dirname(os.path.realpath(__file__))\n logz.configure_output_dir(os.path.join(PROJECT_ROOT, \"log/\"+\"_RAM_\"+time.strftime(\"%d-%m-%Y_%H-%M-%S\")))\n\n # Run training\n seed = 0 # Use a seed of zero (you may want to randomize the seed!)\n env = get_env(task, seed)\n session = get_session()\n atari_learn(env, session, num_timesteps=task.max_timesteps)\n\nif __name__ == \"__main__\":\n main()\n"
},
{
"path": "hw3/run_dqn_ram.py",
"content": "import argparse\nimport gym\nfrom gym import wrappers\nimport os.path as osp\nimport random\nimport numpy as np\nimport tensorflow as tf\nimport tensorflow.contrib.layers as layers\nimport logz\nimport os\nimport time\nimport dqn\nfrom dqn_utils import *\nfrom atari_wrappers import *\n\n\ndef atari_model(ram_in, num_actions, scope, reuse=False):\n with tf.variable_scope(scope, reuse=reuse):\n out = ram_in\n #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]))\n with tf.variable_scope(\"action_value\"):\n out = layers.fully_connected(out, num_outputs=256, activation_fn=tf.nn.relu)\n out = layers.fully_connected(out, num_outputs=128, activation_fn=tf.nn.relu)\n out = layers.fully_connected(out, num_outputs=64, activation_fn=tf.nn.relu)\n out = layers.fully_connected(out, num_outputs=num_actions, activation_fn=None)\n\n return out\n\ndef atari_learn(env,\n session,\n num_timesteps):\n # This is just a rough estimate\n num_iterations = float(num_timesteps) / 4.0\n\n lr_multiplier = 1.0 \n lr_schedule = PiecewiseSchedule([\n (0, 1e-4 * lr_multiplier),\n (num_iterations / 10, 1e-4 * lr_multiplier),\n (num_iterations / 2, 5e-5 * lr_multiplier),\n ],\n outside_value=5e-5 * lr_multiplier)\n optimizer = dqn.OptimizerSpec(\n constructor=tf.train.AdamOptimizer,\n kwargs=dict(epsilon=1e-4),\n lr_schedule=lr_schedule\n )\n\n def stopping_criterion(env, t):\n # notice that here t is the number of steps of the wrapped env,\n # which is different from the number of steps in the underlying env\n return get_wrapper_by_name(env, \"Monitor\").get_total_steps() >= num_timesteps\n\n exploration_schedule = PiecewiseSchedule(\n [\n (0, 0.2),\n (1e6, 0.1),\n (num_iterations / 2, 0.01),\n ], outside_value=0.01\n )\n\n dqn.learn(\n env,\n q_func=atari_model,\n optimizer_spec=optimizer,\n session=session,\n exploration=exploration_schedule,\n stopping_criterion=stopping_criterion,\n replay_buffer_size=1000000,\n batch_size=32,\n gamma=0.99,\n learning_starts=50000,\n learning_freq=4,\n frame_history_len=1,\n target_update_freq=10000,#10000\n grad_norm_clipping=10\n )\n env.close()\n\ndef get_available_gpus():\n from tensorflow.python.client import device_lib\n local_device_protos = device_lib.list_local_devices()\n return [x.physical_device_desc for x in local_device_protos if x.device_type == 'GPU']\n\ndef set_global_seeds(i):\n try:\n import tensorflow as tf\n except ImportError:\n pass\n else:\n tf.set_random_seed(i) \n np.random.seed(i)\n random.seed(i)\n\ndef get_session():\n tf.reset_default_graph()\n tf_config = tf.ConfigProto(\n inter_op_parallelism_threads=1,\n intra_op_parallelism_threads=1)\n session = tf.Session(config=tf_config)\n print(\"AVAILABLE GPUS: \", get_available_gpus())\n return session\n\ndef get_env(seed):\n env = gym.make('Pong-ram-v0')\n\n set_global_seeds(seed)\n env.seed(seed)\n\n expt_dir = '/tmp/hw3_vid_dir/'\n env = wrappers.Monitor(env, osp.join(expt_dir, \"gym\"), force=True)\n env = wrap_deepmind_ram(env)\n\n return env\n\ndef main():\n PROJECT_ROOT = os.path.dirname(os.path.realpath(__file__))\n logz.configure_output_dir(os.path.join(PROJECT_ROOT, \"log/\"+\"_RAM_\"+time.strftime(\"%d-%m-%Y_%H-%M-%S\")))\n\n # Run training\n seed = 0 # Use a seed of zero (you may want to randomize the seed!)\n env = get_env(seed)\n session = get_session()\n atari_learn(env, session, num_timesteps=int(4e7))\n\n\nif __name__ == \"__main__\":\n main()\n"
},
{
"path": "hw4/.idea/hw4.iml",
"content": "\n\n \n \n \n \n \n \n \n \n \n"
},
{
"path": "hw4/.idea/misc.xml",
"content": "\n\n \n"
},
{
"path": "hw4/.idea/modules.xml",
"content": "\n\n \n \n \n \n \n"
},
{
"path": "hw4/.idea/workspace.xml",
"content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n num_simulated_paths\n plot_comparison\n path_cost\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 1516928009112\n \n \n 1516928009112\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n"
},
{
"path": "hw4/cheetah_env.py",
"content": "import numpy as np\nfrom gym import utils\nfrom gym.envs.mujoco import mujoco_env\n\nclass HalfCheetahEnvNew(mujoco_env.MujocoEnv, utils.EzPickle):\n def __init__(self):\n mujoco_env.MujocoEnv.__init__(self, 'half_cheetah.xml', 1)\n utils.EzPickle.__init__(self)\n\n def _step(self, action):\n xposbefore = self.model.data.qpos[0, 0]\n self.do_simulation(action, self.frame_skip)\n xposafter = self.model.data.qpos[0, 0]\n ob = self._get_obs()\n reward_ctrl = - 0.1 * np.square(action).sum()\n reward_run = (xposafter - xposbefore)/self.dt\n reward = reward_ctrl + reward_run\n done = False\n return ob, reward, done, dict(reward_run=reward_run, reward_ctrl=reward_ctrl)\n\n def _get_obs(self):\n return np.concatenate([\n self.model.data.qpos.flat[1:],\n self.model.data.qvel.flat,\n self.get_body_com(\"torso\").flat,\n # self.get_body_comvel(\"torso\").flat,\n ])\n\n def reset_model(self):\n qpos = self.init_qpos + self.np_random.uniform(low=-.1, high=.1, size=self.model.nq)\n qvel = self.init_qvel + self.np_random.randn(self.model.nv) * .1\n self.set_state(qpos, qvel)\n return self._get_obs()\n\n def viewer_setup(self):\n self.viewer.cam.distance = self.model.stat.extent * 0.5"
},
{
"path": "hw4/controllers.py",
"content": "import numpy as np\nfrom cost_functions import trajectory_cost_fn\nimport time\n\n\nclass Controller():\n def __init__(self):\n pass\n\n# Get the appropriate action(s) for this state(s)\n def get_action(self, state):\n pass\n\n\nclass RandomController(Controller):\n def __init__(self, env):\n \"\"\" YOUR CODE HERE \"\"\"\n self.env = env\n\n def get_action(self, state):\n \"\"\" YOUR CODE HERE \"\"\"\n \"\"\" Your code should randomly sample an action uniformly from the action space \"\"\"\n return self.env.action_space.sample()\n\n\nclass MPCcontroller(Controller):\n \"\"\" Controller built using the MPC method outlined in https://arxiv.org/abs/1708.02596 \"\"\"\n def __init__(self,\n env,\n dyn_model,\n horizon=5,\n cost_fn=None,\n num_simulated_paths=10,\n ):\n self.env = env\n self.dyn_model = dyn_model\n self.horizon = horizon\n self.cost_fn = cost_fn\n self.num_simulated_paths = num_simulated_paths\n\n def get_action(self, state):\n \"\"\" YOUR CODE HERE \"\"\"\n \"\"\" Note: be careful to batch your simulations through the model for speed \"\"\"\n ob, obs, next_obs, acs, costs = [], [], [], [], [] #(horizon, num_simulated_paths, n_dim)\n [ob.append(state) for _ in range(self.num_simulated_paths)]\n for _ in range(self.horizon):\n ac = []\n obs.append(ob)\n [ac.append(self.env.action_space.sample()) for _ in range(self.num_simulated_paths)]\n acs.append(ac)\n ob = self.dyn_model.predict(np.array(ob), np.array(ac))\n next_obs.append(ob)\n costs = trajectory_cost_fn(self.cost_fn, np.array(obs), np.array(acs), np.array(next_obs))\n j = np.argmin(costs, )\n\n # no batch\n # paths, costs = [], []\n # for _ in range(self.num_simulated_paths):\n # ob = state\n # obs, next_obs, acs, rewards = [], [], [], []\n # steps = 0\n # while True:\n # obs.append(ob)\n # ac = self.env.action_space.sample()\n # acs.append(ac)\n # ob, rew, done, _ = self.dyn_model.predict(ob, ac)\n # next_obs.append(ob)\n # rewards.append(rew)\n # steps += 1\n # if done or steps >= self.horizon:\n # break\n # path = {\"state\": np.array(obs),\n # \"next_state\": np.array(obs),\n # \"reward\": np.array(rewards),\n # \"action\": np.array(acs)}\n # paths.append(path)\n # cost = trajectory_cost_fn(self.cost_fn, path['state'], path['action'], path['next_state'])\n # costs.append(cost)\n # j = np.argmin(costs)\n\n return acs[0][j]\n"
},
{
"path": "hw4/cost_functions.py",
"content": "import numpy as np\n\n\n#========================================================\n# \n# Environment-specific cost functions:\n#\n\ndef cheetah_cost_fn(state, action, next_state):\n if len(state.shape) > 1:\n\n heading_penalty_factor=10\n scores=np.zeros((state.shape[0],))\n\n #dont move front shin back so far that you tilt forward\n front_leg = state[:,5]\n my_range = 0.2\n scores[front_leg>=my_range] += heading_penalty_factor\n\n front_shin = state[:,6]\n my_range = 0\n scores[front_shin>=my_range] += heading_penalty_factor\n\n front_foot = state[:,7]\n my_range = 0\n scores[front_foot>=my_range] += heading_penalty_factor\n\n scores-= (next_state[:,17] - state[:,17]) / 0.01 #+ 0.1 * (np.sum(action**2, axis=1))\n return scores\n\n heading_penalty_factor=10\n score = 0\n\n #dont move front shin back so far that you tilt forward\n front_leg = state[5]\n my_range = 0.2\n if front_leg>=my_range:\n score += heading_penalty_factor\n\n front_shin = state[6]\n my_range = 0\n if front_shin>=my_range:\n score += heading_penalty_factor\n\n front_foot = state[7]\n my_range = 0\n if front_foot>=my_range:\n score += heading_penalty_factor\n\n score -= (next_state[17] - state[17]) / 0.01 #+ 0.1 * (np.sum(action**2))\n return score\n\n#========================================================\n# \n# Cost function for a whole trajectory:\n#\n\ndef trajectory_cost_fn(cost_fn, states, actions, next_states):\n trajectory_cost = 0\n for i in range(len(actions)):\n trajectory_cost += cost_fn(states[i], actions[i], next_states[i])\n return trajectory_cost"
},
{
"path": "hw4/data/mb_mpc_HalfCheetah-v1_28-01-2018_16-06-09/log.txt",
"content": "Iteration\tAverageCost\tStdCost\tMinimumCost\tMaximumCost\tAverageReturn\tStdReturn\tMinimumReturn\tMaximumReturn\n0\t-493.548176631\t43.3934557274\t-593.771995098\t-446.453530487\t381.547678709\t30.8821057067\t327.377496982\t446.736081347\n1\t-1122.13385831\t92.4521714872\t-1241.09257746\t-955.072426711\t987.157963115\t60.9017195608\t886.845044731\t1081.49728055\n2\t-1300.21775916\t80.8117771081\t-1433.16902329\t-1156.4153666\t1146.6968579\t70.6912337745\t1028.76144524\t1274.93113441\n3\t-1463.68921632\t77.8571301214\t-1587.3212915\t-1282.32051172\t1311.04014914\t60.172262318\t1165.03142264\t1386.06158506\n4\t-1476.02959445\t32.4188386698\t-1527.74978247\t-1405.82416798\t1288.18978053\t39.6180404285\t1210.32037046\t1346.56234414\n5\t-1517.83157369\t61.4665722173\t-1636.92939389\t-1435.94686672\t1355.52121266\t59.9004458616\t1268.38298121\t1471.72099915\n6\t-1553.95070075\t58.6826645157\t-1639.75364295\t-1444.31385474\t1360.97561667\t53.6538321192\t1262.00850239\t1447.2751234\n7\t-1440.04615024\t62.6379689956\t-1514.05029943\t-1318.2470093\t1259.52198589\t60.5724545091\t1161.29520242\t1362.44675516\n8\t-1565.48427033\t73.6200664929\t-1679.88953422\t-1424.21737389\t1390.75665276\t71.4632575969\t1284.51299866\t1497.2432442\n9\t-1526.40374135\t44.2392699116\t-1621.9985455\t-1477.82096817\t1345.65692918\t52.1386068504\t1279.72212546\t1467.99717653\n10\t-1567.6126466\t62.2437455739\t-1652.81324375\t-1444.15659761\t1381.93103697\t51.0084790884\t1272.9300205\t1466.36174963\n11\t-1552.71880645\t59.8835661429\t-1683.74058116\t-1494.38416825\t1364.29074007\t49.1043679333\t1296.16305732\t1469.05953512\n12\t-1558.07898826\t49.83210052\t-1661.20893805\t-1500.25770034\t1369.93761766\t38.7408817538\t1327.68256649\t1447.39093546\n13\t-1562.93104764\t51.6961976147\t-1648.15166542\t-1484.96541419\t1379.22587931\t53.9335577895\t1305.51329604\t1449.73495292\n14\t-1560.4870295\t70.7240293753\t-1699.44325916\t-1463.04704177\t1392.28268965\t65.9248910375\t1304.92694065\t1543.58923526\n"
},
{
"path": "hw4/data/mb_mpc_HalfCheetah-v1_30-01-2018_09-57-32/log.txt",
"content": "Iteration\tAverageCost\tStdCost\tMinimumCost\tMaximumCost\tAverageReturn\tStdReturn\tMinimumReturn\tMaximumReturn\n0\t4949.79745285\t453.149442091\t4282.24348841\t5735.42121368\t-298.382698523\t42.1700957452\t-361.180878133\t-242.391480029\n1\t5050.84893516\t416.530164393\t4532.90956364\t6141.09304317\t-330.565307336\t56.486442547\t-415.539781266\t-246.507590857\n2\t4720.02877593\t291.187503589\t4100.24270572\t5162.98814851\t-304.287676323\t32.2860273802\t-386.544293617\t-264.726499382\n3\t5034.82373518\t609.365940603\t3970.99737528\t5826.00594316\t-317.494030264\t30.4199472001\t-365.815166656\t-267.338896537\n4\t4689.05148158\t728.115810166\t3832.29040058\t6013.80284274\t-327.543069799\t37.2575805598\t-404.067005742\t-256.297654531\n5\t4831.99571644\t619.345407852\t3972.45077954\t5955.09964603\t-289.566155941\t48.7936840782\t-404.687035509\t-228.079151228\n6\t5077.58641913\t698.442977551\t4153.20282174\t6267.99347905\t-302.725763508\t49.223106154\t-380.752748132\t-224.809517519\n7\t4978.07708721\t534.183447632\t4402.42415024\t6138.98491456\t-292.007686768\t56.9235769007\t-392.666633624\t-171.6443602\n8\t4838.49610145\t700.464466825\t3928.06734868\t6192.64803688\t-276.025055481\t42.8181533794\t-343.188931076\t-208.650096202\n9\t4970.52271587\t435.772408928\t3904.25988857\t5521.5594261\t-293.446026856\t34.4784023021\t-376.085533893\t-246.487169567\n10\t4849.95127063\t622.354268978\t3580.11480253\t5732.10442759\t-330.634072683\t48.6780231753\t-409.908273143\t-262.044010366\n11\t5016.56026179\t527.089903537\t4120.94143981\t6174.41402623\t-326.16189379\t50.812950359\t-415.432326094\t-260.38651901\n12\t5059.26805686\t528.251075229\t4082.32523399\t5837.04557822\t-300.184704371\t53.2948616978\t-389.544028777\t-190.448380527\n13\t4966.92083039\t751.814179874\t3357.70264399\t6045.51872278\t-269.849164451\t24.1524963698\t-304.742422678\t-218.401559671\n14\t5287.27749713\t650.222696746\t4235.02082359\t6410.28150186\t-291.539466822\t36.5069700378\t-363.549645146\t-256.879452328\n"
},
{
"path": "hw4/dynamics.py",
"content": "import tensorflow as tf\nimport numpy as np\nimport math\n# Predefined function to build a feedforward neural network\ndef build_mlp(input_placeholder, \n output_size,\n scope, \n n_layers=2, \n size=500, \n activation=tf.tanh,\n output_activation=None\n ):\n out = input_placeholder\n with tf.variable_scope(scope):\n for _ in range(n_layers):\n out = tf.layers.dense(out, size, activation=activation)\n out = tf.layers.dense(out, output_size, activation=output_activation)\n return out\n\nclass NNDynamicsModel():\n def __init__(self, \n env, \n n_layers,\n size, \n activation, \n output_activation, \n normalization,\n batch_size,\n iterations,\n learning_rate,\n sess\n ):\n \"\"\" YOUR CODE HERE \"\"\"\n \"\"\" Note: Be careful about normalization \"\"\"\n self.mean_s, self.std_s, self.mean_deltas, self.std_deltas, self.mean_a, self.std_a = normalization\n self.sess = sess\n self.batch_size = batch_size\n self.iter = iterations\n self.s_dim = env.observation_space.shape[0]\n self.a_dim = env.action_space.shape[0]\n\n self.s_a = tf.placeholder(shape=[None, self.s_dim + self.a_dim], name=\"s_a\", dtype=tf.float32)\n self.deltas = tf.placeholder(shape=[None, self.s_dim], name=\"deltas\", dtype=tf.float32)\n self.deltas_predict = build_mlp(self.s_a, self.s_dim, \"NND\", n_layers=n_layers, size=size,\n activation=activation, output_activation=output_activation)\n\n self.loss = tf.reduce_mean(tf.square(self.deltas_predict - self.deltas))\n self.train_op = tf.train.AdamOptimizer(learning_rate).minimize(self.loss)\n\n #train deltas(s-sp) perdict deltas\n def fit(self, data):\n \"\"\"\n Write a function to take in a dataset of (unnormalized)states,\n (unnormalized)actions, (unnormalized)next_states and fit the dynamics model\n going from normalized states, normalized actions to normalized state differences\n (s_t+1 - s_t)\n \"\"\"\n\n \"\"\"YOUR CODE HERE \"\"\"\n s = np.concatenate([d[\"state\"] for d in data])\n sp = np.concatenate([d[\"next_state\"] for d in data])\n a = np.concatenate([d[\"action\"] for d in data])\n N = s.shape[0]\n train_indicies = np.arange(N)\n\n #normalize\n s_norm = (s - self.mean_s) / (self.std_s + 1e-7)\n a_norm = (a - self.mean_a) / (self.std_a + 1e-7)\n s_a = np.concatenate((s_norm, a_norm), axis=1)\n deltas_norm = ((sp - s) - self.mean_deltas) / (self.std_deltas + 1e-7)\n\n #train\n for j in range(self.iter):\n np.random.shuffle(train_indicies)\n for i in range(int(math.ceil(N / self.batch_size))):\n start_idx = i * self.batch_size % N\n idx = train_indicies[start_idx:start_idx + self.batch_size]\n batch_x = s_a[idx, :]\n batch_y = deltas_norm[idx, :]\n self.sess.run([self.train_op], feed_dict={self.s_a: batch_x, self.deltas: batch_y})\n\n def predict(self, states, actions):\n \"\"\" Write a function to take in a batch of (unnormalized) states\n and (unnormalized) actions and return the (unnormalized) next states\n as predicted by using the model \"\"\"\n \"\"\" YOUR CODE HERE \"\"\"\n #normalize\n s_norm = (states - self.mean_s) / (self.std_s + 1e-7)\n a_norm = (actions - self.mean_a) / (self.std_a + 1e-7)\n s_a = np.concatenate((s_norm, a_norm), axis=1)\n\n delta = self.sess.run(self.deltas_predict, feed_dict={self.s_a: s_a})\n\n #denormalize\n return delta * self.std_deltas + self.mean_deltas + states\n\n\n\n"
},
{
"path": "hw4/logz.py",
"content": "import json\n\n\"\"\"\n\nSome simple logging functionality, inspired by rllab's logging.\nAssumes that each diagnostic gets logged each iteration\n\nCall logz.configure_output_dir() to start logging to a \ntab-separated-values file (some_folder_name/log.txt)\n\nTo load the learning curves, you can do, for example\n\nA = np.genfromtxt('/tmp/expt_1468984536/log.txt',delimiter='\\t',dtype=None, names=True)\nA['EpRewMean']\n\n\"\"\"\n\nimport os.path as osp, shutil, time, atexit, os, subprocess\nimport pickle\nimport tensorflow as tf\n\ncolor2num = dict(\n gray=30,\n red=31,\n green=32,\n yellow=33,\n blue=34,\n magenta=35,\n cyan=36,\n white=37,\n crimson=38\n)\n\ndef colorize(string, color, bold=False, highlight=False):\n attr = []\n num = color2num[color]\n if highlight: num += 10\n attr.append(str(num))\n if bold: attr.append('1')\n return '\\x1b[%sm%s\\x1b[0m' % (';'.join(attr), string)\n\nclass G:\n output_dir = None\n output_file = None\n first_row = True\n log_headers = []\n log_current_row = {}\n\ndef configure_output_dir(d=None):\n \"\"\"\n Set output directory to d, or to /tmp/somerandomnumber if d is None\n \"\"\"\n G.output_dir = d or \"/tmp/experiments/%i\"%int(time.time())\n if osp.exists(G.output_dir):\n print(\"Log dir %s already exists! Delete it first or use a different dir\"%G.output_dir)\n else:\n os.makedirs(G.output_dir)\n G.output_file = open(osp.join(G.output_dir, \"log.txt\"), 'w')\n atexit.register(G.output_file.close)\n print(colorize(\"Logging data to %s\"%G.output_file.name, 'green', bold=True))\n\ndef log_tabular(key, val):\n \"\"\"\n Log a value of some diagnostic\n Call this once for each diagnostic quantity, each iteration\n \"\"\"\n if G.first_row:\n G.log_headers.append(key)\n else:\n assert key in G.log_headers, \"Trying to introduce a new key %s that you didn't include in the first iteration\"%key\n assert key not in G.log_current_row, \"You already set %s this iteration. Maybe you forgot to call dump_tabular()\"%key\n G.log_current_row[key] = val\n\ndef save_params(params):\n with open(osp.join(G.output_dir, \"params.json\"), 'w') as out:\n out.write(json.dumps(params, separators=(',\\n','\\t:\\t'), sort_keys=True))\n\ndef pickle_tf_vars(): \n \"\"\"\n Saves tensorflow variables\n Requires them to be initialized first, also a default session must exist\n \"\"\"\n _dict = {v.name : v.eval() for v in tf.global_variables()}\n with open(osp.join(G.output_dir, \"vars.pkl\"), 'wb') as f:\n pickle.dump(_dict, f)\n \n\ndef dump_tabular():\n \"\"\"\n Write all of the diagnostics from the current iteration\n \"\"\"\n vals = []\n key_lens = [len(key) for key in G.log_headers]\n max_key_len = max(15,max(key_lens))\n keystr = '%'+'%d'%max_key_len\n fmt = \"| \" + keystr + \"s | %15s |\"\n n_slashes = 22 + max_key_len\n print(\"-\"*n_slashes)\n for key in G.log_headers:\n val = G.log_current_row.get(key, \"\")\n if hasattr(val, \"__float__\"): valstr = \"%8.3g\"%val\n else: valstr = val\n print(fmt%(key, valstr))\n vals.append(val)\n print(\"-\"*n_slashes)\n if G.output_file is not None:\n if G.first_row:\n G.output_file.write(\"\\t\".join(G.log_headers))\n G.output_file.write(\"\\n\")\n G.output_file.write(\"\\t\".join(map(str,vals)))\n G.output_file.write(\"\\n\")\n G.output_file.flush()\n G.log_current_row.clear()\n G.first_row=False\n"
},
{
"path": "hw4/main.py",
"content": "import numpy as np\nimport tensorflow as tf\nimport gym\nfrom dynamics import NNDynamicsModel\nfrom controllers import MPCcontroller, RandomController\nfrom cost_functions import cheetah_cost_fn, trajectory_cost_fn\nimport time\nimport logz\nimport os\nimport tqdm\nimport copy\nimport matplotlib.pyplot as plt\nfrom cheetah_env import HalfCheetahEnvNew\n\ndef sample(env, \n controller, \n num_paths=10, \n horizon=1000, \n render=False,\n verbose=False):\n \"\"\"\n Write a sampler function which takes in an environment, a controller (either random or the MPC controller), \n and returns rollouts by running on the env. \n Each path can have elements for observations, next_observations, rewards, returns, actions, etc.\n \"\"\"\n paths = []\n \"\"\" YOUR CODE HERE \"\"\"\n for _ in tqdm.tqdm(range(num_paths)):\n ob = env.reset()\n obs, next_obs, acs, rewards, costs = [], [], [], [], []\n steps = 0\n while True:\n obs.append(ob)\n ac = controller.get_action(ob)\n acs.append(ac)\n ob, rew, done, _ = env.step(ac)\n next_obs.append(ob)\n rewards.append(rew)\n steps += 1\n if done or steps >= horizon:\n break\n path = {\"state\": np.array(obs),\n \"next_state\": np.array(next_obs),\n \"reward\": np.array(rewards),\n \"action\": np.array(acs)}\n paths.append(path)\n\n return paths\n\n# Utility to compute cost a path for a given cost function\ndef path_cost(cost_fn, path):\n return trajectory_cost_fn(cost_fn, path['state'], path['action'], path['next_state'])\n\ndef compute_normalization(data):\n \"\"\"\n Write a function to take in a dataset and compute the means, and stds.\n Return 6 elements: mean of s_t, std of s_t, mean of (s_t+1 - s_t), std of (s_t+1 - s_t), mean of actions, std of actions\n \"\"\"\n\n \"\"\" YOUR CODE HERE \"\"\"\n s = np.concatenate([d[\"state\"] for d in data])\n sp = np.concatenate([d[\"next_state\"] for d in data])\n a = np.concatenate([d[\"action\"] for d in data])\n\n mean_obs = np.mean(s, axis=0)\n mean_deltas = np.mean(sp - s, axis=0)\n mean_action = np.mean(a, axis=0)\n\n std_obs = np.std(s, axis=0)\n std_deltas = np.std(sp - s, axis=0)\n std_action = np.std(a, axis=0)\n\n return mean_obs, std_obs, mean_deltas, std_deltas, mean_action, std_action\n\n\ndef plot_comparison(env, dyn_model):\n \"\"\"\n Write a function to generate plots comparing the behavior of the model predictions\n for each element of the state to the actual ground truth, using randomly sampled actions.\n \"\"\"\n \"\"\" YOUR CODE HERE \"\"\"\n pass\n\ndef train(env, \n cost_fn,\n logdir=None,\n render=False,\n learning_rate=1e-3,\n onpol_iters=10,\n dynamics_iters=60,\n batch_size=512,\n num_paths_random=10, \n num_paths_onpol=10, \n num_simulated_paths=10000,\n env_horizon=1000, \n mpc_horizon=15,\n n_layers=2,\n size=500,\n activation=tf.nn.relu,\n output_activation=None\n ):\n\n \"\"\"\n\n Arguments:\n\n onpol_iters Number of iterations of onpolicy aggregation for the loop to run. \n\n dynamics_iters Number of iterations of training for the dynamics model\n |_ which happen per iteration of the aggregation loop.\n\n batch_size Batch size for dynamics training.\n\n num_paths_random Number of paths/trajectories/rollouts generated \n | by a random agent. We use these to train our \n |_ initial dynamics model.\n \n num_paths_onpol Number of paths to collect at each iteration of\n |_ aggregation, using the Model Predictive Control policy.\n\n num_simulated_paths How many fictitious rollouts the MPC policy\n | should generate each time it is asked for an\n |_ action.\n\n env_horizon Number of timesteps in each path.\n\n mpc_horizon The MPC policy generates actions by imagining \n | fictitious rollouts, and picking the first action\n | of the best fictitious rollout. This argument is\n | how many timesteps should be in each fictitious\n |_ rollout.\n\n n_layers/size/activations Neural network architecture arguments. \n\n \"\"\"\n\n logz.configure_output_dir(logdir)\n\n #========================================================\n # \n # First, we need a lot of data generated by a random\n # agent, with which we'll begin to train our dynamics\n # model.\n\n random_controller = RandomController(env)\n\n \"\"\" YOUR CODE HERE \"\"\"\n data = sample(env, random_controller, num_paths_random, env_horizon)\n\n #========================================================\n # \n # The random data will be used to get statistics (mean\n # and std) for the observations, actions, and deltas\n # (where deltas are o_{t+1} - o_t). These will be used\n # for normalizing inputs and denormalizing outputs\n # from the dynamics network. \n #\n \"\"\" YOUR CODE HERE \"\"\"\n normalization = compute_normalization(data)\n\n\n #========================================================\n # \n # Build dynamics model and MPC controllers.\n # \n sess = tf.Session()\n\n dyn_model = NNDynamicsModel(env=env, \n n_layers=n_layers, \n size=size, \n activation=activation, \n output_activation=output_activation, \n normalization=normalization,\n batch_size=batch_size,\n iterations=dynamics_iters,\n learning_rate=learning_rate,\n sess=sess)\n\n mpc_controller = MPCcontroller(env=env, \n dyn_model=dyn_model, \n horizon=mpc_horizon, \n cost_fn=cost_fn, \n num_simulated_paths=num_simulated_paths)\n\n\n #========================================================\n # \n # Tensorflow session building.\n # \n sess.__enter__()\n tf.global_variables_initializer().run()\n\n #========================================================\n # \n # Take multiple iterations of onpolicy aggregation at each iteration\n # refitting the dynamics model to current dataset and then\n # taking onpolicy samples and aggregating to the dataset.\n # Note: You don't need to use a mixing ratio in this assignment for\n # new and old data as described in https://arxiv.org/abs/1708.02596\n # \n for itr in range(onpol_iters):\n \"\"\" YOUR CODE HERE \"\"\"\n dyn_model.fit(data)\n paths = sample(env, mpc_controller, num_paths_onpol, env_horizon)\n data = np.concatenate((data, paths))\n\n returns = [np.sum(path[\"reward\"]) for path in paths]\n costs = [path_cost(cost_fn, path) for path in paths]\n\n # LOGGING\n # Statistics for performance of MPC policy using\n # our learned dynamics model\n logz.log_tabular('Iteration', itr)\n # In terms of cost function which your MPC controller uses to plan\n logz.log_tabular('AverageCost', np.mean(costs))\n logz.log_tabular('StdCost', np.std(costs))\n logz.log_tabular('MinimumCost', np.min(costs))\n logz.log_tabular('MaximumCost', np.max(costs))\n # In terms of true environment reward of your rolled out trajectory using the MPC controller\n logz.log_tabular('AverageReturn', np.mean(returns))\n logz.log_tabular('StdReturn', np.std(returns))\n logz.log_tabular('MinimumReturn', np.min(returns))\n logz.log_tabular('MaximumReturn', np.max(returns))\n\n logz.dump_tabular()\n\ndef main():\n\n import argparse\n parser = argparse.ArgumentParser()\n parser.add_argument('--env_name', type=str, default='HalfCheetah-v1')\n # Experiment meta-params\n parser.add_argument('--exp_name', type=str, default='mb_mpc')\n parser.add_argument('--seed', type=int, default=3)\n parser.add_argument('--render', action='store_true')\n # Training args\n parser.add_argument('--learning_rate', '-lr', type=float, default=1e-3)\n parser.add_argument('--onpol_iters', '-n', type=int, default=1)\n parser.add_argument('--dyn_iters', '-nd', type=int, default=60)\n parser.add_argument('--batch_size', '-b', type=int, default=512)\n # Data collection\n parser.add_argument('--random_paths', '-r', type=int, default=10)\n parser.add_argument('--onpol_paths', '-d', type=int, default=10)#10\n parser.add_argument('--simulated_paths', '-sp', type=int, default=1000)#1000\n parser.add_argument('--ep_len', '-ep', type=int, default=1000)\n # Neural network architecture args\n parser.add_argument('--n_layers', '-l', type=int, default=2)\n parser.add_argument('--size', '-s', type=int, default=500)\n # MPC Controller\n parser.add_argument('--mpc_horizon', '-m', type=int, default=15)\n args = parser.parse_args()\n\n # Set seed\n np.random.seed(args.seed)\n tf.set_random_seed(args.seed)\n\n # Make data directory if it does not already exist\n if not(os.path.exists('data')):\n os.makedirs('data')\n logdir = args.exp_name + '_' + args.env_name + '_' + time.strftime(\"%d-%m-%Y_%H-%M-%S\")\n logdir = os.path.join('data', logdir)\n if not(os.path.exists(logdir)):\n os.makedirs(logdir)\n\n # Make env\n if args.env_name is \"HalfCheetah-v1\":\n env = HalfCheetahEnvNew()\n cost_fn = cheetah_cost_fn\n train(env=env, \n cost_fn=cost_fn,\n logdir=logdir,\n render=args.render,\n learning_rate=args.learning_rate,\n onpol_iters=args.onpol_iters,\n dynamics_iters=args.dyn_iters,\n batch_size=args.batch_size,\n num_paths_random=args.random_paths, \n num_paths_onpol=args.onpol_paths, \n num_simulated_paths=args.simulated_paths,\n env_horizon=args.ep_len, \n mpc_horizon=args.mpc_horizon,\n n_layers = args.n_layers,\n size=args.size,\n activation=tf.nn.relu,\n output_activation=None,\n )\n\nif __name__ == \"__main__\":\n main()\n"
},
{
"path": "hw4/plot.py",
"content": "import seaborn as sns\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport json\nimport os\n\n\"\"\"\nUsing the plotter:\n\nCall it from the command line, and supply it with logdirs to experiments.\nSuppose you ran an experiment with name 'test', and you ran 'test' for 10 \nrandom seeds. The runner code stored it in the directory structure\n\n data\n L test_EnvName_DateTime\n L 0\n L log.txt\n L params.json\n L 1\n L log.txt\n L params.json\n .\n .\n .\n L 9\n L log.txt\n L params.json\n\nTo plot learning curves from the experiment, averaged over all random\nseeds, call\n\n python plot.py data/test_EnvName_DateTime --value AverageReturn\n\nand voila. To see a different statistics, change what you put in for\nthe keyword --value. You can also enter /multiple/ values, and it will \nmake all of them in order.\n\n\nSuppose you ran two experiments: 'test1' and 'test2'. In 'test2' you tried\na different set of hyperparameters from 'test1', and now you would like \nto compare them -- see their learning curves side-by-side. Just call\n\n python plot.py data/test1 data/test2\n\nand it will plot them both! They will be given titles in the legend according\nto their exp_name parameters. If you want to use custom legend titles, use\nthe --legend flag and then provide a title for each logdir.\n\n\"\"\"\n\ndef plot_data(data, value=\"AverageReturn\"):\n if isinstance(data, list):\n data = pd.concat(data, ignore_index=True)\n sns.set(style=\"darkgrid\", font_scale=1.5)\n sns.tsplot(data=data, time=\"Iteration\", value=value, unit=\"Unit\", condition=\"Condition\")\n plt.legend(loc='best').draggable()\n plt.show()\n\n\ndef get_datasets(fpath, condition=None):\n unit = 0\n datasets = []\n for root, dir, files in os.walk(fpath):\n if 'log.txt' in files:\n # param_path = open(os.path.join(root,'params.json'))\n # params = json.load(param_path)\n # exp_name = params['exp_name']\n exp_name = fpath\n log_path = os.path.join(root,'log.txt')\n experiment_data = pd.read_table(log_path)\n\n experiment_data.insert(\n len(experiment_data.columns),\n 'Unit',\n unit\n )\n experiment_data.insert(\n len(experiment_data.columns),\n 'Condition',\n condition or exp_name\n )\n\n datasets.append(experiment_data)\n unit += 1\n\n return datasets\n\n\ndef main():\n import argparse\n parser = argparse.ArgumentParser()\n parser.add_argument('logdir', nargs='*')\n parser.add_argument('--legend', nargs='*')\n parser.add_argument('--value', default='AverageReturn', nargs='*')\n args = parser.parse_args()\n\n use_legend = False\n if args.legend is not None:\n assert len(args.legend) == len(args.logdir), \\\n \"Must give a legend title for each set of experiments.\"\n use_legend = True\n\n data = []\n if use_legend:\n for logdir, legend_title in zip(args.logdir, args.legend):\n data += get_datasets(logdir, legend_title)\n else:\n for logdir in args.logdir:\n data += get_datasets(logdir)\n\n if isinstance(args.value, list):\n values = args.value\n else:\n values = [args.value]\n for value in values:\n plot_data(data, value=value)\n\nif __name__ == \"__main__\":\n main()\n"
}
]