Repository: alberduris/Reinforcement_Learning_AI_Video_Games
Branch: master
Commit: c00ea2fc233f
Files: 16
Total size: 4.2 MB
Directory structure:
gitextract_k986b96w/
├── README.md
├── Week 1/
│ ├── README.md
│ └── RL_Value_Iteration_Taxi.ipynb
├── Week 2/
│ ├── Monte_Carlo_Prediction.ipynb
│ └── README.md
├── Week 3/
│ ├── QLearning_MountainCar.ipynb
│ └── README.md
├── Week 4/
│ ├── Policy_Gradients_Pong.ipynb
│ └── README.md
├── Week 5/
│ ├── .ipynb_checkpoints/
│ │ └── Actor_Critic_Model_Pendulum-checkpoint.ipynb
│ ├── Actor_Critic_Model_Pendulum.ipynb
│ └── README.md
└── Week 6/
├── .ipynb_checkpoints/
│ └── Proximal_Policy_Optimization_Pendulum-checkpoint.ipynb
├── Proximal_Policy_Optimization_Pendulum.ipynb
├── README.md
└── log/
└── events.out.tfevents.1514553381.DESKTOP-1DR3ASA
================================================
FILE CONTENTS
================================================
================================================
FILE: README.md
================================================
# Reinforcement_Learning_AI_Video_Games
Code for each week's short video of Siraj Raval Course on Reinforcement Learning "AI for Video Games"
## Week 1 - Value iteration algorithm
Value iteration algorithm built for the Taxi-v1 environment by OpenAI Gym library.
## Week 2 - Monte Carlo Prediction algorithm
Monte Carlo Prediction algorithm built for the Blackjack-v0 environment by OpenAI Gym library.
## Week 3 - Q-Learning algorithm
Q-Learning algorithm built for the MountainCarContinuous-v0 environment by OpenAI Gym library.
## Week 4 - Policy Gradients algorithm
Policy Gradients algorithm built for the Pong-v0 environment by OpenAI Gym library.
## Week 5 - Actor-Critic model
Actor-Critic model built for the Pendulum-v0 environment by OpenAI Gym library.
## Week 6 - Proximal Policy Optimization
Proximal Policy Optimization algorithm built for the Pendulum-v0 environment by OpenAI Gym library.
================================================
FILE: Week 1/README.md
================================================
## Coding Challenge
This week's coding challenge is to implement Value iteration algorithm built for the Taxi-v1 environment by OpenAI Gym library. By Siraj Raval.
The submission file is: RL_Value_Iteration_Taxi.ipynb
## Dependencies for challenge
* numpy
* [OpenAI Gym](https://gym.openai.com/docs/)
## Environment
That's the least relevant thing but, as mentioned before, "Taxi" environment has been chosen.
It's a simple game. At least for humans :)
You have a Taxi. In a little "city". You also have the direction of a passenger and a destination. Of course, you have to go to the position of your passenger, pick them and go to the destination **in the shortest path**
Hope that with the image it's all clear :D
### References
[Siraj Raval - Youtube - Introduction to AI for Video Games](https://www.youtube.com/watch?v=i_McNBDP9Qs)
[Taxi - OpenAI Gym](https://gym.openai.com/envs/Taxi-v1/)
[An overview of MAXQ hierarchical reinforcement learning](https://link.springer.com/book/10.1007/3-540-44914-0#page=38)
[Deep Reinforcement Learning Demysitifed (Episode 2) — Policy Iteration, Value Iteration and Q-learning](https://medium.com/@m.alzantot/deep-reinforcement-learning-demysitifed-episode-2-policy-iteration-value-iteration-and-q-978f9e89ddaa)
================================================
FILE: Week 1/RL_Value_Iteration_Taxi.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1. Value Iteration algorithm applied to Taxi in OpenAI GYM\n",
"\n",
"This weeks coding challenge is to use the OpenAI Gym library to build a simple value iteration algorithm for any of the available environments. In this Notebook we're going to implement the algorithm to solve the **Taxi** game from Gym. More information here."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1.1 How to play the game?\n",
"\n",
"It's a simple game. At least for humans :) \n",
"\n",
"Let's see it. You have a Taxi. In a little \"city\". You also have the direction of a passenger and a destination. Of course, you have to go to the position of your passenger, pick them and go to the destination **in the shortest path**\n",
"\n",
"![](\"src/TheTaxiGame.png\")
\n",
"\n",
"Hope that with the image it's all clear :D\n",
"\n",
"### 1.1.1 Theory of the game\n",
"\n",
"This task was introduced in [Dietterich2000] to illustrate some **issues in hierarchical reinforcement learning**. There are **4 locations** (labeled by different letters) and your job is to pick up the **passenger at one location** and **drop** him off **in another**. You receive **+20 points for a successful dropoff**, and **lose 1 point for every timestep** it takes. There is also a **10 point penalty for illegal pick-up and drop-off actions**."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. The RL problem in theory [3]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So we have:\n",
"\n",
"- An agent: The Taxi\n",
"- An environment: The \"city\"\n",
"- A reward: The points\n",
"- A set of states: The positions of the taxi and the passenger and destination\n",
"- A set of actions: At every time step, the directions the taxi can go\n",
"\n",
"We are going to apply the **value iteration** algorithm so, first of all it's important to know what is a **value function**\n",
"\n",
"#### Value function:\n",
"\n",
"Often denoted V(s), represent **how good** is a state for an agent to be in. The value function depends on the policy by which the agent picks actions to perform. So, if the agent uses a given policy 𝛑 to select actions, the corresponding value function is given by: \n",
"\n",
"![](\"src/value_function_1.png\")
\n",
"\n",
"Among all possible value-functions, there exist an optimal value function that has higher value than other functions for all states.\n",
"\n",
"![](\"src/optimal_value_function.png\")
\n",
"\n",
"The optimal policy 𝛑* is the policy that corresponds to optimal value function.\n",
"\n",
"![](\"src/optimal_policy.png\")
\n",
"\n",
"#### Q function:\n",
"\n",
"Now we introduce another function which is the **state-action pair Q function**.\n",
"\n",
"![](\"src/q_func_simple.png\")
\n",
"\n",
"Q(s, a) is an indication for how good it is for an agent to pick action a while being in state s.\n",
"\n",
"To find the optimal --> Q\\*(s,a)\n",
"\n",
"The Q\\*(s, a) is equal to the summation of immediate reward after performing action a while in state s and the discounted expected future reward after transition to a next state s'.\n",
"\n",
"![](\"src/q_optimal.png\")
\n",
"\n",
"This is the **Bellman equation**\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Value Iteration algorithm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Value iteration **computes the optimal state value function** by **iteratively improving the estimate of V(s)**.\n",
"\n",
"The algorithm **initialize V(s)** to arbitrary **random** values. It repeatedly **updates the Q(s, a) and V(s) values until they convergs**. Value iteration is **guranteed to converge to the optimal values**.\n",
"\n",
"![](\"src/value_iteration_pseudo.png\")
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Explore the game technically"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import gym\n",
"from gym import wrappers"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[2017-11-19 17:45:16,461] Making new env: Taxi-v2\n"
]
}
],
"source": [
"#Get the game environment\n",
"env_name = 'Taxi-v2'\n",
"env_wrapped = gym.make(env_name)\n",
"env = env_wrapped.unwrapped"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's explore the possible **states** and **actions**"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Action space A -> len(A): 6\n",
"States space S -> len(S): 500\n"
]
}
],
"source": [
"print('Action space A -> len(A): %d' % env.action_space.n)\n",
"print('States space S -> len(S): %d' % env.observation_space.n)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So we have a set of **6 possible actions**
\n",
"and a set of **500 possible states**\n",
"\n",
"Not bad for start :)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Reinforcement Learning for Taxi implementation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5.1 Value iteration"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- The value function will represent a VALUE for each STATE, initialize it to zeros.\n",
"\n",
"- We define a number of max iterations _(max_iterations)_ to converge and a minimum increasing value _(eps)_ to consider not improving.\n",
"\n",
"**In the training loop:**\n",
"\n",
"1. Store the last value function\n",
"2. For each STATE _s_ and For each ACTION _a_ \n",
"\n",
" 2.1 Iterate the NEXT STATES you can go from that determined state-action pair (_s_,_a_)
\n",
" 2.2 Store the reward from those NEXT STATES according to the formula: **probability * (reward + PreviousValueFunction[NEXT\\_STATE])**
\n",
"\n",
"3. **Sum** all those rewards from NEXT STATES
\n",
"4. **Choose the max** reward of _q(s,a)_ pairs and put it on the actual value function for STATE s\n",
"\n",
"5. If the actual value functions hasn't improved more than the improving threshold, convergence is reached."
]
},
{
"cell_type": "code",
"execution_count": 227,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def value_iteration(env, gamma = 1.0):\n",
"\n",
" '''\n",
" Value-iteration algorithm\n",
" '''\n",
" #The value function will represent a VALUE for each STATE, initialize it to zeros.\n",
" v = np.zeros(env.observation_space.n)\n",
" max_iterations = 10000\n",
" display_freq = max_iterations // 10\n",
" eps = 1e-20\n",
" lastDif = float('inf')\n",
" \n",
" #Start training loop\n",
" print('Starting training loop...')\n",
" for i in range(max_iterations):\n",
" if(i % display_freq == 0):\n",
" print('25 first elements of actual value function. An array of number of possible states (%d) elements:\\n%s'\n",
" %(env.observation_space.n,v[0:25]))\n",
" \n",
" prev_v = np.copy(v) #Store the last value function\n",
" for s in range(env.observation_space.n):#For each STATE s\n",
" q_sa = []\n",
" for a in range(env.action_space.n): #For each ACTION a\n",
" next_states_rewards = []\n",
" for next_sr in env.P[s][a]: #Iterate the states you can go from determined state-action pair (s,a)\n",
" p, s_, r, _ = next_sr #(probability, next_state, reward, done) of the states you can go from (s,a)\n",
" next_states_rewards.append((p*(r + prev_v[s_]))) #Apply the formula to get the rewards\n",
" q_sa.append(np.sum(next_states_rewards)) #Store the sum of rewards for each pair (s,a)\n",
" v[s] = max(q_sa) #Choose the max reward of (s,a) pairs and put it on the actual value function for STATE s\n",
" \n",
" if(np.abs(np.abs(np.sum(prev_v - v)) - lastDif) < eps): #Check convergence\n",
" print('Value-iteration converged at iteration %d' % (i+1))\n",
" break\n",
" lastDif = np.abs(np.sum(prev_v - v))\n",
" return v "
]
},
{
"cell_type": "code",
"execution_count": 228,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Starting training loop...\n",
"25 first elements of actual value function. An array of number of possible states (500) elements:\n",
"[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0. 0.]\n",
"Value-iteration converged at iteration 19\n"
]
}
],
"source": [
"optimal_value_function = value_iteration(env=env,gamma=1.0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How does the optimal value functions looks like?"
]
},
{
"cell_type": "code",
"execution_count": 235,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Value function shape: (500,)\n",
"First 25 values of the value functions:\n",
"[ 359. 233. 275. 212. 107. 233. 65. 128. 191. 107. 275. 128.\n",
" 65. 107. 65. 212. 380. 254. 296. 233. 338. 212. 254. 191.\n",
" 128.]\n"
]
}
],
"source": [
"print('Value function shape: %s\\nFirst 25 values of the value functions:\\n%s' \n",
" % (optimal_value_function.shape,optimal_value_function[0:25]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So we have a real number for each possible state, seems right."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.1 Extract policy\n",
"\n",
"Because we want our agent to **do something** and our agents **behaves according** to the **policy**, the **policy** could be **random actions** or a **value function**...\n",
"\n",
"Now that we have a (optimal) function value we can **extract** the associated (optimal) policy.\n",
"\n",
"**How:**\n",
"Just get the argument that maximize (argmax) the value function for each possible state"
]
},
{
"cell_type": "code",
"execution_count": 230,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def extract_policy(v, gamma = 1.0):\n",
" '''\n",
" Extract the policy given a value-function\n",
" '''\n",
" \n",
" policy = np.zeros(env.observation_space.n) #Policy : array of 0s with as many elements as possible states\n",
" for s in range(env.observation_space.n):\n",
" q_sa = np.zeros(env.action_space.n) # q_sa: array of 0s with as many elements as possible actions\n",
" for a in range(env.action_space.n):\n",
" for next_sr in env.P[s][a]: #Iterate the states you can go from determined state-action pair\n",
" #next_sr is a tuple of (probability, next_state, reward, done)\n",
" p, s_, r, _ = next_sr\n",
" q_sa[a] += (p * (r + gamma * v[s_]))\n",
" policy[s] = np.argmax(q_sa)\n",
" \n",
" return policy"
]
},
{
"cell_type": "code",
"execution_count": 231,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Extract the policy for the optimal value function that we've just computed with the value iteration algorithm\n",
"optimal_policy = extract_policy(v=optimal_value_function, gamma=1.0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How does the optimal value functions looks like?"
]
},
{
"cell_type": "code",
"execution_count": 237,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Value function shape: (500,)\n",
"First 25 values of the value functions:\n",
"[ 4. 4. 4. 4. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 5. 0.\n",
" 0. 0. 3. 3. 3. 3. 0.]\n"
]
}
],
"source": [
"print('Value function shape: %s\\nFirst 25 values of the value functions:\\n%s' \n",
" % (optimal_value_function.shape,optimal_policy[0:25]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So we have a number in [0,5] for each state. Guess it!\n",
"\n",
"Yes, it's the (optimal) action to take in each state."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.2 Evaluate policy\n",
"\n",
"In fact it's playing the game.\n",
"\n",
"1. Get in a starting state (reset the environment)\n",
"2. Take the (optimal) action according to the state --> Ask the policy!\n",
"3. Do it\n",
"4. If it's ended. Stop.\n",
"\n",
"\\*find each element of this numbered list in the code by the index ;)"
]
},
{
"cell_type": "code",
"execution_count": 293,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def run_episode(env, policy, gamma = 1.0, verbose=True,render = False):\n",
" '''\n",
" Evaluates a policy by using it to run an episode and finding it's total reward\n",
" \n",
" returns: \n",
" total reward: real value of the total reward received by the agent under policy\n",
" '''\n",
" \n",
" state = env.reset() #1. Reset the environment, starting state.\n",
" total_reward = 0\n",
" step_idx = 0\n",
" \n",
" frames = []\n",
" while True:\n",
" \n",
" if render:\n",
" env.render(mode = 'human')\n",
" \n",
" action = int(policy[state]) #2. Ask the policy for the optimal action!\n",
" state, reward, done, info = env.step(action) #3. Do it!\n",
" if verbose:\n",
" print('State: %d - Reward: %d - Done: %d' %(state, reward, done))\n",
" \n",
" total_reward = total_reward + (gamma ** step_idx * reward)\n",
" step_idx += 1\n",
" \n",
" if done: #4. If it's ended stop\n",
" break\n",
"\n",
" return total_reward"
]
},
{
"cell_type": "code",
"execution_count": 294,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+---------+\n",
"|\u001b[34;1mR\u001b[0m: | : :\u001b[35mG\u001b[0m|\n",
"| : : : : |\n",
"| : : : : |\n",
"|\u001b[43m \u001b[0m| : | : |\n",
"|Y| : |B: |\n",
"+---------+\n",
"\n",
"State: 201 - Reward: -1 - Done: 0\n",
"+---------+\n",
"|\u001b[34;1mR\u001b[0m: | : :\u001b[35mG\u001b[0m|\n",
"| : : : : |\n",
"|\u001b[43m \u001b[0m: : : : |\n",
"| | : | : |\n",
"|Y| : |B: |\n",
"+---------+\n",
" (North)\n",
"State: 101 - Reward: -1 - Done: 0\n",
"+---------+\n",
"|\u001b[34;1mR\u001b[0m: | : :\u001b[35mG\u001b[0m|\n",
"|\u001b[43m \u001b[0m: : : : |\n",
"| : : : : |\n",
"| | : | : |\n",
"|Y| : |B: |\n",
"+---------+\n",
" (North)\n",
"State: 1 - Reward: -1 - Done: 0\n",
"+---------+\n",
"|\u001b[34;1m\u001b[43mR\u001b[0m\u001b[0m: | : :\u001b[35mG\u001b[0m|\n",
"| : : : : |\n",
"| : : : : |\n",
"| | : | : |\n",
"|Y| : |B: |\n",
"+---------+\n",
" (North)\n",
"State: 17 - Reward: -1 - Done: 0\n",
"+---------+\n",
"|\u001b[42mR\u001b[0m: | : :\u001b[35mG\u001b[0m|\n",
"| : : : : |\n",
"| : : : : |\n",
"| | : | : |\n",
"|Y| : |B: |\n",
"+---------+\n",
" (Pickup)\n",
"State: 117 - Reward: -1 - Done: 0\n",
"+---------+\n",
"|R: | : :\u001b[35mG\u001b[0m|\n",
"|\u001b[42m_\u001b[0m: : : : |\n",
"| : : : : |\n",
"| | : | : |\n",
"|Y| : |B: |\n",
"+---------+\n",
" (South)\n",
"State: 137 - Reward: -1 - Done: 0\n",
"+---------+\n",
"|R: | : :\u001b[35mG\u001b[0m|\n",
"| :\u001b[42m_\u001b[0m: : : |\n",
"| : : : : |\n",
"| | : | : |\n",
"|Y| : |B: |\n",
"+---------+\n",
" (East)\n",
"State: 157 - Reward: -1 - Done: 0\n",
"+---------+\n",
"|R: | : :\u001b[35mG\u001b[0m|\n",
"| : :\u001b[42m_\u001b[0m: : |\n",
"| : : : : |\n",
"| | : | : |\n",
"|Y| : |B: |\n",
"+---------+\n",
" (East)\n",
"State: 57 - Reward: -1 - Done: 0\n",
"+---------+\n",
"|R: |\u001b[42m_\u001b[0m: :\u001b[35mG\u001b[0m|\n",
"| : : : : |\n",
"| : : : : |\n",
"| | : | : |\n",
"|Y| : |B: |\n",
"+---------+\n",
" (North)\n",
"State: 77 - Reward: -1 - Done: 0\n",
"+---------+\n",
"|R: | :\u001b[42m_\u001b[0m:\u001b[35mG\u001b[0m|\n",
"| : : : : |\n",
"| : : : : |\n",
"| | : | : |\n",
"|Y| : |B: |\n",
"+---------+\n",
" (East)\n",
"State: 97 - Reward: -1 - Done: 0\n",
"+---------+\n",
"|R: | : :\u001b[35m\u001b[42mG\u001b[0m\u001b[0m|\n",
"| : : : : |\n",
"| : : : : |\n",
"| | : | : |\n",
"|Y| : |B: |\n",
"+---------+\n",
" (East)\n",
"State: 97 - Reward: 20 - Done: 1\n"
]
},
{
"data": {
"text/plain": [
"10.0"
]
},
"execution_count": 294,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"run_episode(env,optimal_policy,verbose=True,render=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Do it a lot of times for evaluating"
]
},
{
"cell_type": "code",
"execution_count": 295,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def evaluate_policy(env, policy, gamma = 1.0, n = 100):\n",
" '''\n",
" Evaluates a policy by running it n times\n",
" \n",
" returns:\n",
" average total reward\n",
" '''\n",
" scores = [run_episode(env,policy,gamma,False,False) for _ in range(n)]\n",
" return np.mean(scores)"
]
},
{
"cell_type": "code",
"execution_count": 258,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"We have got 10.20 average points in 10 games\n"
]
}
],
"source": [
"n = 10\n",
"points = evaluate_policy(env,optimal_policy,gamma=1.0,n=n)\n",
"print('We have got %.2f average points in %d games' %(points,n))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.2.1 Formal evaluation\n",
"\n",
"According to the official documentation, **Taxi-v1 defines \"solving\" as getting average reward of 9.7 over 100 consecutive trials.** "
]
},
{
"cell_type": "code",
"execution_count": 298,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"We have got 8.50 average points in 100 games\n"
]
}
],
"source": [
"n = 100\n",
"points = evaluate_policy(env,optimal_policy,gamma=1.0,n=n)\n",
"print('We have got %.2f average points in %d games' %(points,n))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After all...\n",
"\n",
"![](\"src/playing_with_neural_nets.jpg\")
\n",
"\n",
"Seriously, check it in the render mode, the taxi driver is awesome! Always takes the shortest path! Don't know what else to do :O"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Future work\n",
"\n",
"- Tweak _gamma_ values and see what happens"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## References\n",
"\n",
"[1] Taxi - OpenAI Gym - https://gym.openai.com/envs/Taxi-v1/
\n",
"[2] An overview of MAXQ hierarchical reinforcement learning - https://link.springer.com/book/10.1007/3-540-44914-0#page=38
\n",
"[3] Deep Reinforcement Learning Demysitifed (Episode 2) — Policy Iteration, Value Iteration and Q-learning - https://medium.com/@m.alzantot/deep-reinforcement-learning-demysitifed-episode-2-policy-iteration-value-iteration-and-q-978f9e89ddaa"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
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},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: Week 2/Monte_Carlo_Prediction.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Monte Carlo Methods"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we do not assume complete knowledge of the environment. Monte Carlo methods require only experience—sample sequences of states, actions, and rewards from actual or simulated interaction with an environment.\n",
"\n",
"Monte Carlo methods are ways of solving the reinforcement learning problem based on averaging sample returns. To ensure that well-defined returns are available, here we define Monte Carlo methods only for episodic tasks. That is, we assume experience is divided into episodes, and that all episodes eventually terminate no matter what actions are selected. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Monte Carlo Prediction for Blackjack"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We begin by considering Monte Carlo methods for learning the state-value function for a given policy. Recall that the value of a state is the expected return—expected cumulative future discounted reward—starting from that state. An obvious way to\n",
"estimate it from experience, then, is simply to average the returns observed after visits to that state. As more returns are observed, the average should converge to the expected value. This idea underlies all Monte Carlo methods.\n",
"\n",
"**First-visit:**
\n",
"The first-visit MC method estimates _vπ(s)_ as the average of the returns following first visits to _s_\n",
"\n",
"![](\"src/first_visit_algorithm.png\")
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Implementation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import gym\n",
"import matplotlib\n",
"from matplotlib import pyplot as plt\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"import numpy as np\n",
"import sys\n",
"\n",
"from collections import defaultdict\n",
"\n",
"matplotlib.style.use('ggplot')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Util functions"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def plot_value_function(V, title='Value Function'):\n",
" min_x = min(k[0] for k in V.keys())\n",
" max_x = max(k[0] for k in V.keys())\n",
" min_y = min(k[1] for k in V.keys())\n",
" max_y = max(k[1] for k in V.keys())\n",
" \n",
" x_range = np.arange(min_x, max_x + 1)\n",
" y_range = np.arange(min_y, max_y + 1)\n",
" X, Y = np.meshgrid(x_range, y_range)\n",
" \n",
" Z_noace = np.apply_along_axis(lambda _: V[(_[0],_[1],False)], 2, np.dstack([X,Y]))\n",
" Z_ace = np.apply_along_axis(lambda _: V[(_[0],_[1],True)], 2, np.dstack([X,Y]))\n",
" \n",
" def plot_surface(X, Y, Z, title):\n",
" fig = plt.figure(figsize=(20,10))\n",
" ax = fig.add_subplot(111, projection='3d')\n",
" surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=matplotlib.cm.coolwarm, vmin=-1.0, vmax=1.0)\n",
" ax.set_xlabel('Player Sum')\n",
" ax.set_ylabel('Dealer Showing')\n",
" ax.set_zlabel('Value')\n",
" ax.set_title(title)\n",
" ax.view_init(ax.elev, -120)\n",
" fig.colorbar(surf)\n",
" plt.show()\n",
" \n",
" plot_surface(X, Y, Z_noace, '(No Usable Ace) '+title)\n",
" plot_surface(X, Y, Z_ace, '(Usable Ace) '+title)\n",
" \n",
" \n",
"def sample_policy(observation):\n",
" score, dealer_score, usable_ace = observation\n",
" return 0 if score >= 20 else 1\n",
"\n",
"def print_observation(observation):\n",
" score, dealer_score, usable_ace = observation\n",
" print('Player score: %d | Usable Ace: %s | Dealer score: %d' %(score,usable_ace,dealer_score))\n",
" \n",
"def test_environment(env,n_episodes=10):\n",
" for i_episodes in range(n_episodes):\n",
" observation = env.reset()\n",
" while True: #Forever\n",
" print_observation(observation)\n",
" action = sample_policy(observation)\n",
" print('Taking action: %s' %([\"Stick\",\"Hit\"][action]))\n",
" observation, reward, done, _ = env.step(action)\n",
" if done:\n",
" print(observation)\n",
" print('Game end. Reward: %.1f\\n' % reward)\n",
" break"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Test environment"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[2017-11-26 18:08:42,913] Making new env: Blackjack-v0\n"
]
}
],
"source": [
"env = gym.make('Blackjack-v0')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Player score: 21 | Usable Ace: True | Dealer score: 10\n",
"Taking action: Stick\n",
"(21, 10, True)\n",
"Game end. Reward: 1.0\n",
"\n",
"Player score: 14 | Usable Ace: False | Dealer score: 7\n",
"Taking action: Hit\n",
"(23, 7, False)\n",
"Game end. Reward: -1.0\n",
"\n",
"Player score: 12 | Usable Ace: False | Dealer score: 4\n",
"Taking action: Hit\n",
"Player score: 16 | Usable Ace: False | Dealer score: 4\n",
"Taking action: Hit\n",
"Player score: 19 | Usable Ace: False | Dealer score: 4\n",
"Taking action: Hit\n",
"(24, 4, False)\n",
"Game end. Reward: -1.0\n",
"\n",
"Player score: 12 | Usable Ace: False | Dealer score: 4\n",
"Taking action: Hit\n",
"(22, 4, False)\n",
"Game end. Reward: -1.0\n",
"\n",
"Player score: 17 | Usable Ace: False | Dealer score: 10\n",
"Taking action: Hit\n",
"(23, 10, False)\n",
"Game end. Reward: -1.0\n",
"\n",
"Player score: 13 | Usable Ace: False | Dealer score: 6\n",
"Taking action: Hit\n",
"Player score: 14 | Usable Ace: False | Dealer score: 6\n",
"Taking action: Hit\n",
"Player score: 21 | Usable Ace: False | Dealer score: 6\n",
"Taking action: Stick\n",
"(21, 6, False)\n",
"Game end. Reward: 1.0\n",
"\n",
"Player score: 15 | Usable Ace: False | Dealer score: 2\n",
"Taking action: Hit\n",
"Player score: 17 | Usable Ace: False | Dealer score: 2\n",
"Taking action: Hit\n",
"Player score: 21 | Usable Ace: False | Dealer score: 2\n",
"Taking action: Stick\n",
"(21, 2, False)\n",
"Game end. Reward: 0.0\n",
"\n",
"Player score: 19 | Usable Ace: True | Dealer score: 10\n",
"Taking action: Hit\n",
"Player score: 13 | Usable Ace: False | Dealer score: 10\n",
"Taking action: Hit\n",
"(23, 10, False)\n",
"Game end. Reward: -1.0\n",
"\n",
"Player score: 14 | Usable Ace: False | Dealer score: 10\n",
"Taking action: Hit\n",
"(22, 10, False)\n",
"Game end. Reward: -1.0\n",
"\n",
"Player score: 18 | Usable Ace: False | Dealer score: 10\n",
"Taking action: Hit\n",
"(26, 10, False)\n",
"Game end. Reward: -1.0\n",
"\n"
]
}
],
"source": [
"test_environment(env)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Monte Carlo Prediction Algorithm"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def mc_prediction(policy, env, num_episodes, discount_factor=1.0):\n",
" returns_sum = defaultdict(float)\n",
" returns_count = defaultdict(float)\n",
" display_freq = num_episodes // 10\n",
" \n",
" V = defaultdict(float)\n",
" \n",
" for i_episode in range(1, num_episodes + 1):\n",
" if i_episode % display_freq == 0:\n",
" print('Episode (%d/%d)' %(i_episode,num_episodes))\n",
" sys.stdout.flush()\n",
" \n",
" #Generate an episode\n",
" episode = []\n",
" state = env.reset()\n",
" while True: #Forever\n",
" action = policy(state)\n",
" next_state, reward, done, _ = env.step(action)\n",
" episode.append((state,action,reward))\n",
" if done:\n",
" break\n",
" state = next_state\n",
"\n",
" #Find all states we've visited in this episode\n",
" #We convert each state to a tuple so that we can use it as a dict key\n",
" states_in_episode = set([tuple(x[0]) for x in episode])\n",
" for state in states_in_episode:\n",
" #Find the first occurence of the state in the episode\n",
" first_occurence_idx = next(i for i,x in enumerate(episode) if x[0] == state)\n",
" #Sum up all rewards since first occurence\n",
" G = sum([x[2]*(discount_factor**i) for i,x in enumerate(episode[first_occurence_idx:])])\n",
" #Calculate average return for this state over all sampled episodes\n",
" returns_sum[state] += G\n",
" returns_count[state] += 1.0\n",
" V[state] = returns_sum[state] / returns_count[state]\n",
" \n",
" \n",
" return V"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Train"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### With 10.000 episodes"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Episode (1000/10000)\n",
"Episode (2000/10000)\n",
"Episode (3000/10000)\n",
"Episode (4000/10000)\n",
"Episode (5000/10000)\n",
"Episode (6000/10000)\n",
"Episode (7000/10000)\n",
"Episode (8000/10000)\n",
"Episode (9000/10000)\n",
"Episode (10000/10000)\n"
]
}
],
"source": [
"value_function = mc_prediction(sample_policy,env,num_episodes=10000)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plot"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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MRFmtIBExOEBEcWXlwSWnFVBbmmcBNJ8KEKgFYJV54Oq2jUCVPgU6lU694fvO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eDNitjU8gALi7/3sv50OU0myXehcJdiuSa6uS1+5M15JZwxV865bixhslHroa4a2PJZDGcI+t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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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UqFFR+3LkyBHMmDGj2/Z6w3hNjh07hv79+wNI/Lp3Z9y4cWhoaMCmTZviZg8YgQVVVbtt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\nA2U90gFJwqnYAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_value_function(value_function)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### With 2M episodes"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Episode (200000/2000000)\n",
"Episode (400000/2000000)\n",
"Episode (600000/2000000)\n",
"Episode (800000/2000000)\n",
"Episode (1000000/2000000)\n",
"Episode (1200000/2000000)\n",
"Episode (1400000/2000000)\n",
"Episode (1600000/2000000)\n",
"Episode (1800000/2000000)\n",
"Episode (2000000/2000000)\n"
]
}
],
"source": [
"value_function = mc_prediction(sample_policy,env,num_episodes=2000000)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plot"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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z8vFpmhY0EFAQhIApAKkaCJgo6uGD0E7GbrqGZs2E48BhIJF/f0GA3LkLHN98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05dQACLBgHAMBJ3Edf8Ozc0TUM8HkcsFsPo6OgqXwC/zSoVvWG5wYq+wV9uhJToKUaItBV2mVdo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\nQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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MmTO73V4igu/J0aNH0adPHwCx3/fujBkzBnV1ddi0aVPU7IFgYEGW5W63M3z4cHzyyScR\nt3366acQBCH0WnRHEATY7XaoqgpFUXpsj4iIiOLHgoRERBli1qxZ2LdvHw4ePBhx+6233orf//73\nWL58OXbu3IktW7bg/vvvx4YNG3DjjTcCAFpbW/GjH/0Ia9euxb59+7Bp0yasXr06NMgfPHgwBEHA\n7373O+zbtw9vv/12REp/uMcffxzvv/8+tm/fjnvvvRcnTpzAtddeG/WxF198MSorK3HNNddgzZo1\n2L9/P9avX4/HHnsMb7/9dtTn1NbW4osvvsCiRYswcuTIiL/vfve72L59Oz799FPk5ubi5ptvxiOP\nPIJnn30WO3fuxObNm0PL9A0ePBiLFi3CXXfdhZUrV2L37t3YvHkzXnrpJTzxxBMRbX7yySeYPXt2\nj+9BsDDhyy+/jDVr1kQUIgQ6rtA7HA784Q9/wN69e/Hhhx/i/vvv73G7Z511Fp577jl8+eWX2Lp1\nK+64446Ige3QoUNx6aWX4r/+67+watUq7NmzB5s3b8aLL76IpUuX9rj9WKqqqtC3b1/88pe/xI4d\nO/DZZ5/hf/7nfxLeztlnn43x48fjlltuwTvvvIN9+/bh888/x0svvQQA6NWrF9xuN9asWYPjx493\nyYAJuu2227BhwwYsWbIEO3bswPvvv4+f/vSnuOyyyyKmtnQnmMkiSRIcDgckSUp4f4iIyLoEUdTl\nLxtlZ6+JiExo2LBhmDJlSmiJuaCbb74Z9913H55//nlccMEFWLRoEbZs2YJXX301VLxPkiQ0Njbi\nBz/4AWbMmIErr7wSZWVlePzxxwEAo0aNws9+9jMsX74cM2fOxFNPPRVzUPvTn/4Uv/jFL3Duuefi\niy++wLJly2LOYXe5XFi5ciXGjBmDO++8E2eddRZuuukm1NTUoH///lGfs3z5cpSXl2PixIld7hsy\nZAhGjx6NFStWAADuuusu3H333XjmmWcwe/ZsXHnllRFXvn/+85/jpptuwm9/+1vMnDkTV1xxBV55\n5RVUVlaGHrN3717U1NRg0aJFsV76CIsWLUJrayt69erVJSOhV69e+M1vfoMPPvgAM2fOxAMPPBBX\ncOC+++5DVVUVFi9ejGuuuQbTpk3rcgX+kUcewfXXX49HH30UM2bMwKJFi/Dqq69G7Eui7HY7li5d\niqNHj2Lu3Ln48Y9/3GVlgHiIoogVK1Zg+vTpuPvuuzF9+nTcfvvtaGhoANDx+XvggQfw5z//GePH\nj8e8efOibqe6uhrPPPMMPv74Y5xzzjm44447MHfuXDz44INJ72MwSGCz2TSb+kBERGRFgprAxL1D\nhw6lsy9ERJb32Wef4bbbbsPatWu7nbNP8bv33nuhqioeeugho7tCKRIEoccggKqqkGU5ol4CERHF\nr1+/fkZ3Ia3Wz56mSzunv79Wl3a0xMwBIqIMMmnSJNxxxx3Yt2+f0V0xBUVR0Ldv36jLMJI5BQMI\nnHJARESUGGYOEBERUVaIJ3MgGlmWWbyQiChOZs8cqDn3LF3aGfvOR7q0oyWuVkBERESmJkkSJEni\nlAMiIqJuMDhARERkEXl5efD5fPD5fEZ3xRA5OTlQFAVer5dBAiIiixJEFq+NhcEBIiIii2A1/2+X\nQrTZOk6BOOWAiIioA4MDREREFmH14EC0/Q9OOVAUBYFAwIBeERGRngSRNflj4StDRERElhGrDrMo\ninA4HEkVPCQiIjIDZg4QERER/YsoiqFMAtYlICIyH9YciI3BASIiIrIEQRBiZg6EY10CIiKyIgYH\niIiILCSewTFFYl0CIiKyAgYHiIiILMLqc+lT3f9gXYLglAMGWoiIso8oWfu3sDssSEhERNRJbm6u\n0V2gGFId4GsxoA8GCex2O0RWvSYiIpNg5gAREVEnbrcbra2tRneDMhjrEhARZScWJIyNwQEiIiIL\nsXIqfLwFCZPBugRERJTtGBwgIiIi0gjrEhARZTaB08FiYnCAiIjIIliQUL/9F0UxlEkgyzIURdGt\nbSIiomQwOEBERERZQYvBvZ5X8lmXgIgo87DmQGwMDhARERHpgHUJiIgokzE4QEREZCFWngOfzoKE\niQjWJVBVFYFAICP6RERkFcwciI3VGIiIKClc350oNYIgwOFwwG6383giIiLDMXOAiIiSUlxcjPr6\nel71zCKZWJBQVVXIa96GUFAIFJZAKCqBUFgMwWY3umu6YF0CIiJ9MXMgNgYHiIgoKQwKkBbUA3sQ\neOfPkTcKApCT1xEoKCqB8K+ggVhcCpT0glhcCiG/MOFgRyYGRzpjXQIiIjIKgwNERJS0TJnDTdlL\n3rax642qCrQ2Q21thnpwb8RdvuA/JBuEwmKI/woaCEUlEItKIRSVfnuby532/qdLsC5BcClEHmdE\nRNoQOI0rJgYHiIiIyDBK7abknigHoNYfh1x/HDGT8F1uiEWlUPsPhX3UWODsGUn20jiiKIYyCWRZ\nhqIoRneJiIhMisEBIiJKCq9kZqdMet/UlqYumQGa8nogt7ai9b13gL+9gba/rkTOBfPhOv2M9LWZ\nBp3rEgCAz+fr5hlERBSLKGX+FDOjMDhARERJy4Y53PStTHq/bDYblN21HVMI0sgn24B/DaS9td/A\nW/sNbJWDkP+dhXCdMTmjXpN4FRYWorGxkXUJiIiyXE1NDZYtWwZFUTB79mwsWLAg4v5nn30Wmzdv\nBtARFG5sbMSzzz4LALjiiitQWVkJACgrK8Pdd9+dcn8YHCAiIqK0EEURNpst4k+SJKiqikAggPpt\nX6e1faV8MPxfd61pENi3Bw2PPwJbRX/kXXQJ3JPPhCBKae1LOgTrEgRfz0zKCiEiou4pioJnnnkG\nP/7xj1FaWop7770XEyZMQP/+/UOPue6660L/fuutt7B79+7Q/x0OB37xi19o2icGB4iIKCkciFCQ\nJEldggCiKEKWZQQCAQQCAXi9XgQCgdCceVVR4NuavuCA6s6FZ+eubh8TOHgAJ5/6LZr//AryL1wA\n95nTIdiy79RIEISI4oWsS0BEFFumLGW4Y8cOlJeXo0+fPgCAqVOn4osvvogIDoT7+OOPcfnll6e1\nT9n3C0hElCWCgySv12t0V9ImG1OyKTnBOe+dswAARAQB2tra4rqKre7bCXja0tZf2V0CtfVofI89\nehgnn1mK5tdWIu/CBcg5exYEmz1tfUuHznUJZFmGLMcs1UhERDq45557Qv+eM2cO5syZE/p/fX09\nSktLQ/8vLS3F9u3bo27n+PHjOHbsGKqrq0O3+f1+3HPPPZAkCfPnz8fEiRNT7i+DA0REaSJJEux2\nu6mDA5R9ehq0h08FsNvtoSyA4Pz2QCAAn8+Htra2lAaf8rYkVymIR59KeDduSfhpct1xND77f2h+\n7VXkXTAfuTPnQHA409DB9JMkKWKVA2b6EBF10HMpw4ceeijmfdG+l2NddPn4448xefJkiGF9f/LJ\nJ1FSUoKjR49iyZIlqKysRHl5eUr9ZXCAiChNglfyzIqDjewT/nnsnAUQvOIcHgTweDwRUwG0pGzr\nWgtAC6rdCc+BwyltQ2moR9OKZWh548/IO+8i5Mw+F6LLrVEP9SWKIkRRZF0CIqIMU1paihMnToT+\nf+LECRQXF0d97Lp163DjjTdG3FZSUgIA6NOnD0aNGoU9e/YwOEBElKmscBJu5uCHGQiCELr6H/wr\nKysLDRQ71wPQi9rYAPXIgbRsWy7qC+WgNlkJSuNJNL38PFre/Aty585D7jnnQ8zJ1WTbyRIEIanv\nFtYlICLqkCk1B6qqqnD48GEcO3YMJSUlWLduHb73ve91edyhQ4fQ2tqK4cOHh25raWmB0+mE3W5H\nU1MTtm3bhvnz56fcJwYHiIjSiINn0kOsgoDBLAC/3w+v1wu73Y76+nrDA1dybXqmFKil5fBu2az5\ndpWWZjS/+hJa3vorcuecj7zzLoSYl695O/FINjgAsC4BEVEmkSQJN9xwAx544AEoioKZM2diwIAB\nePnll1FVVYUJEyYAANauXYupU6dGnFMePHgQv//970O/9QsWLIhZyDARgprAL8yhQ4dSbpCIyCps\nNhvy8vJw8uRJo7uSFgUFBfB4PPD7/UZ3RXNlZWWoq6szuhsR4i0IGPyL9vNeWlqaEcEB3/InoWyp\n0XSbqiiiHbkIHEltSkE8BJcLubPnIvf870AqKEx7e+FEUURubi6am5s12ybrEhBRuH79+hndhbTa\ne/MCXdoZ+Pu/6NKOlpg5QERESWNmhPbCCwIGiwJqWRDQ6AGgGghA2fmN5ttVeg1EYGN66hh0pnq9\naHnzNbS++xZyZpyDvHnzIRWX6NJ2KpkDsbAuARERAQwOEBGljdkLElJqohUEFAQhIgvA6/WipaVF\ns/nhmfB5VPZsB9q1XcFDLSiBZ6v2AYce2/X50PrOm2j94J2OwoUz58BW1lv3fmiFdQmIyAr0XK0g\n2zA4QESUJma/+mbm/dNq38KnAgQLA0qSZHhBQCMpGtcbUAH4/CIQMG56i33QEDS/thLNb/wZOVPP\nRv78hbCVpyctNx2ZA+FYl4CIyLoYHCAiw+Tm5kKWZXi92l5FzCSZcKU2ncy8f4kMwnoqCBgIBNDe\n3q5pFkC20noJQ7V8MPxf6zOdIBqpdzn8u3d2/EeW0fbRB2hbuwbuSVORP38h7AMGatqe3secJEmQ\nJIl1CYjINDJltYJMxOAAERnKzINLTiswF0EQIElSxNKA0QoCtrW1cd52DEp9HdTjRzTbnpqTD8/2\nnZptL2EOJ1RFhurzRd6uKvB8uhaezz6G6/QzkL/gMjgGV2nWrBGfLdYlICIyPwYHiMgwHDxnN7MO\nDkRRhCAIyMnJCWUEdJ4KkEpBQCvTOmvAb8+H6kn/6gSx2CsHwlfbTa0DVYX3y8/h/fJzOMeMQ/6C\nS+EcfkpKbaZ7WkE87dvtdqiqyroERJSVWHMgNgYHiMgwqqpCNPEXtBWCH9m8f90VBAxeIQ3WAuAA\nSBtKrXbBAaVPJXwbt2i2vUQ5R5wC79bNcT++/esNaP96AxwjRyN/waVwVZ+Wxt6lH+sSEBGZD4MD\nRGQYKwyeyVjhBQE7TwXoriBgaWkpPB6PabMjjKD6/VB2bdNmWw4nvPsOabKtZEi9y9G+c3tSz/V9\nsxknHtoM+9DhyJ9/KdzjJiT0fKMzB6JhXQIiyio894yJwQEiMgyDA9ktkwYA8RQE5FQAYym7vgH8\n2qwoIBeUQzkQ/1V7TcWqM5Ag/45a1D/yIOwDByN//kK4zpiS9d+HrEtARJTdGBwgIsMwOJD99Hz/\nggUBOy8LCLAgYDZQtmmzhKFa1g/ercZNJ+ixzkCC/Ht3o/63v4StYgDyL7oE7qnTIIhSzMdnYuZA\nZ6xLQESUnRgcICKijCKKYtSpAOEFAf1+P7MAsoxSm3pwQBUleOsaAYMGx/ZhI+DbtjUt2w4c3I+G\np36DplUvI/+ii5Fz1kwItq6naYIgZM1gm3UJiCgTcSnD2BgcIMpQwYrpZj6ZYuZAdkv16mXnZQHD\nCwJ2rgWQLYMhik45fgRq/fGUtyP3qoS8UdsVD+Il9e4N/+5daW9HPnYEJ59Ziua/vIK8eQuQO2MO\nBIcj7e2mG+sSEBFlPgYHiDKU3W6Hw+FAc3Oz0V1JGwYHsl9P7193BQGDQQC/3w+v18sBg4lpsYSh\nUlAC79b0XLXvkd0BVQFUX7tuTcon6tD43NNofm0l8i74DnJnnwfR5cqKaQXdYV0CIjIalzKMjcEB\nogxlhYGzFfbRKlgQkLqT6pQCFYDPCyBsRQk92QcO0rTOQCKUxpNoevE5tLz+ZxRcthjuuReaotJ2\nsC6B0+lEa2srs4OIiDIAgwNEGcoKA2cr7KPZhA/+XS4XRFFETk4OCwJSTKqvHcru5Jb9C5J7D0Jg\nkzYFDRNlHzoCvlqDMhbCSIWFaPvLizj0wVvIv/wa2MaMN7pLmnC73Whv78jIYF0CItIDaw7ExuAA\nUYZSFMX0A2cGBzJTPAUBA4EAPB4PAKC1tdXgHlMmU3ZsBeTkr/grOflo37FDwx7FT+rdB/496a8z\n0BMxPx8SZCg+HwLHj6HhiV/CfsqpyFt0LWzlFUZ3TzOsS0BEZCwGB4gylKqqEE0+J4rBAWN1XhYw\n0YKAbrfb9J9RSl2q9Qb8Yi5U72GNepMAuwOqqupaZyAqSYKzbznkg/sibvZv3YiGJXfDPes85Fy4\nEKLLbVAHtce6BESUTqw5EBurtl8bAAAgAElEQVSDA0QZigNncwi+j0ad3MZTEDAYBEj0Sh0/oxQP\nOYV6A3LvSvg3bdGwN/Ezss5AuPwxY9BeG+M1kGV43n0T7Z99jNxLFsM5+SxTHZPBugSqqkKWZdYl\nICJKMwYHiDIUB16UCFEUuywLyIKAyTM6qGMWyuEDQGNDUs9VHS549x7QuEfxyZQ6A67Ro2MHBsIo\nTSfR/OxSeD58D3mLr4e9crAOvdNGPMdYMMgJsC4BEaWONQdiY3CAiCiNtB5kds4CCJ4wB4MAfr+f\nBQEpYyi1yU8p8OeVQT2g/5V7qVcf+PcaX2fAXjkQyoE9CT0nsGs7Tj7433BNm4XcBZdDzCtIT+cM\nxLoERETpw+AAZSWXywWv12t0N4h6lMyJa7wFAYP1AIzC7BbqibwtuSkFSmlf+LYakNJvd0CFCrXd\n2DoDYmERRJ8HajLHt6rC+9H7aP/yU+R+53K4ps/J2Pm1qQROWZeAiJLFzIHYGBygrJSbm8vgAGWN\nWAPoYEHAaFMB/H5/jwUBiTKZ6mmDum9n4s+TbPAeP5mGHvUsI+oM2O1w9iqFfDi1KRVqWytaXloG\nz9r3kXfFdXAMP0WjDmpHi6wq1iUgItIOgwOUlYKV/HkSQJlOVVXY7XY4HI5uCwJyKgCZjbJ9C5DE\nd3SgpALK5s1p6FH3MqXOgHvkSMi7ajXbnnxgHxofWQLnGVOQu/C7kIpLNNt2qrSccsW6BEQUtwzN\npsoEDA5QVlIUhenMlFHCpwIECwMG014FQYDP5zNdQUBOK6DuyEnUG1AKy9BuwHSCTKkz4Ko+VdPA\nQLj2Lz5B+9frkXv+ArjPuRCCzbyngKxLQESUHPP+MpCpBTMHzDLIisUKFdOzbR+jFQQUBCEiC8Dj\n8YSmAhQWFqKtrQ1+v9/orhPpRlVVKLUJXv0XBORcvAiOugb4tmyCd+NXUFpb0tPBcHY7VMDwOgOO\nwUOg7E18GkZC2tvR+peX4V23BrmXXw3nqaent70epPu7n3UJiCgaXtiIjcEBykpWuWKZbQPnZGTi\nPgbnsGpRENAqn1WicOrBvUBLU0LPESdOh9x/MMR+/eEacyqcl1+BwN59CHyzBb7NmyAf2JeWvtoH\nDjF8OoFUUgqhtRGqok/AWz52BE2P/wKOU8ch7/JrIPUu16XdzvT67mddAiKi+DA4QFlJURSIFpgv\nZIWBpZH72F1BwOCygCwIGJsVPp8Uv+Ccb5vNhvY9tfAl8uT8QvinXwib+u1xJogi7IMHwT54ENzn\nXwClsQmB2m/g37IFvq2boXo9KffZPnS44YEBwemEvSgfytHDurft27gB9Vs3wT3nAuResACC06Vr\n+3oHhlmXgIgAZOwKLpmAwQHKSlYZlFihtkK6TwzDByxGFAS0ymeVrKPzUpt2uz0iqBYIBNC+aUNi\nG517KeB0A3Jz7HYLC+A4YyIcZ0xETiAAee9e+LduhW/z15APH0p4P6Sy3vDv3Z3w87TmGjYM8u7t\nxnUg4Ifn7dfQ/tlHyF14FVxnTNWtaSOzxliXgIioKwYHLKK8vBxHjhwxuhuasVLmgNn3U6vBc+cB\nSzAIED5gMVtBQKJ0Cs+sCU6z6Vxfw+v1oqWlJSKzRm1thrw//uJ+wrDRCJxyOpDA4Eyw2WCrqoKt\nqgruCy+E0tCAwDffwLdlE/zbtkL19ZC3YLdDFQXD6wy4Tx0Dedc2Q/sQpDTUo/npx+D9+APkX/pd\nSP0HGt0lXbAuARHRtxgcsJBMm9edCqtcjbXKfiayj/EUBMykqQBmfg/N8n1idZ2zAKJl1rS2tsY9\ncFJqN8c/0Lc7IJ9/BQBAEpI/XsXiYjimTIFjyhSofj/k3bs6ggWbNyJwtGtgPBPqDDiGDoO8Oz0r\nE6RCEoGWR++D/bQz4Dx/IaRe6atHkEnnJaxLQGQdgmjO8zItMDhgEcEr7Wa5YmqlzAGzDiyDou1j\n56kAwQFLMgUBjWb299DM+2YmwWNKFEXk5+en9ZiSt22Kv1/TL4BaWNrxb2i03r3dDtvwEbANHwHX\nd+ZDqauDUlsL/9bN8GzdDPsg4wMDUu/eEE7WZczAOEgsKIR6eB+gqvDXfA7/11/CMelsOOcugFhQ\npHl7giBk3CCcdQmIyMoYHLAIswUHzD7gCjL7fkqSBFEU4Xa74Xa7uxQE5FQAMkqmDdriFT69JjgV\nIPyYApDWY0pVFCjb41vCUOhTgcCkWd/2XaPgQGdiWRnEsjLYpk6Fy9cObN+BVgDttd+kpb2eCC4X\n7DkuKMcbDWm/O47+/aHsDat/oMjwffIBfF9+DOe0c+CcPQ+CO1ez9jIpcyAa1iUgMikLXGBMFoMD\nFmG2QaYVCvUB5njfBEGAJEkRSwOGpy2Logi/3w+Px2PK+Z5meA9jMdt7lU3iqQcQPKbCr8yWlZWh\nPY3z7NX9uwFPa88PFAQo8xYDovTtTUj/FWTB4YQwehTyR49C3vHj8H78MdrWrdVk5YP4OiDANWQw\n5L3x12TQi61vPyj7dkS/0+dD+z/ehO/T1XDOmgfHWedAsDtSbjNbvhtZl4CIrILBAYswWxq+FQr1\nAR3vWzC9MdPFKggYnrYc7Yplbm4uFEWB3+83sPfpY+bgAJA9J/fJyIR96xwASLUeQLrJ2zbG9Thh\n/DTIFYMjb9NjvXtRApSODAqhVy+4FyyA+4ILEPjqa7Su+Qf8+/amtX33qWMg7zQmY6EntrwcKE11\n3T5GbWuF940/of2jd+GauwD2iWenvCRYJnxu48W6BETmwJoDsWXHqINSZrbgADMHjKN1QcBM3Eet\nmX3/KDXhNTbCgwDhgbXw7JpMptTGERzIK0Bg5ne63q7qMNCKdiw6HLCdMQGFZ0yAcuAgPGs/gueL\nzwB/DyseJMg5fETGrEzQmWPoMCiH4g+MqI0N8PxpGdpXvw3X+QthP+2MpNrN9GkFsbAuARGZFYMD\nFmHGK+1WGHAZNXDuXBAw/Ipl5+Jlqc7DNONnM5yZgx/ZeFJvpJ7qAQQCAbS3t3dZGlBL6XzP1KaT\nUA8f6PmBcxcCrpwuNws6BAcEofuyh2L/CuQuWoSc78yH78sv0fbhB5CjrHaQKFvfflDrDie0XKNu\nJAlCe2tSFR+UY4fR9sfHIVUOgWveZbANG5XQ87M1OBCOdQmIso8gmPe8M1UMDliEVa60m026B5bh\n85aDf3oXBDTz4NkK+N51FTyuwutsRKsH4Pf7dR1IpPu9kms39Tj4FYaOQmDUhK53qCqQpoKEnRqK\n61FCjhvOs6bBMe1MyLt2w7P2Q7TXrAeS+B4UcnNhswlQmtJX6yEVzhEjoYYXIUyCvG8XWpc+DNuI\narjmXQap/6C4nmeG4EBQeF2CYNYPEVG2YXDAIhRFgd1uN7oblCCtBs7RpgIAkfOW29raMmbespkw\n+GFe8dQDaG9vt8xxpfS0hKHdDvn8K6LeJQo6zd1O8G0QBAG2qiHIrxqC3IsXov3zz+D5aA2Uhvr4\nNiCKcA3oD3n/noS7qgchNxfqsYOabS+wbRNaajfDMXYSHOddDKlXuWbbzhY2mw1utxvNzc2hbAIi\nyjCsORATgwMWYbaaA1aRyMAynoKAWq1jriWzD57NvH9m3regeOsBWH25TVWWoezY0u1jhLPPh1pU\nFvW+dC1j2EUKQRqxIB/uOXPgmjULga3fwLP2I/i2bOx2m+7qasg7M7POAAA4Bw2GsqdW242qKnwb\nPoXv6y/gmjIT9jkXQSwoivpQM2UOBAWzB4Ir9UiSxLoERJQ1GBywCDPO6w7++JrtxCJctKBO52UB\nUy0IaDQrDDAp83WuB+BwOFBSUqJrPYBspuzdAbR7Y94v9O6HwKQ5Me/XKzigqqkP0ARRhH30KNhH\nj4Jyoh7eTz+Bd91aKM1NEY9znnJKRgcGpF69oezfmb4GZBnete+h/fOPkDPzfEjT50KIUmvCjDqf\nl7AuAVFmSXWVFTNjcMAizFhzwMzBgc4FAYuLi7ukLPv9fk0KAhrN7MEBs+9ftom3HoAoimhqajLd\n1b50fVco3S1hKAhQ5i0C/vUdFvUh0Gtagbb7L5aWIGfePLjPPRf+TZvgWfsR/Nu3wV7RP77ijAay\nlxRDOXAy7e2ovna0/v0vENe+h9y5CyBNngk1S5boTUZ35yXhdQm4FCIRZSLzfjtTBDNOKwjuUzb/\nuMZTEFBRFFMOUoI4eM5emfzexVMPoLvgWm5urt5dTrt0vlfdBQeE06dC7l/V7fMFHQKcos0GNeBP\ny7YFux2OcePgGDcO8pGjaH/nbwhsPZ6WtrRgHzQEyoHduraptLagedVySB+8hfwLL4c4fqqu7esl\nnosWwQsAqqqyLgGRAQTWHIiJwQGLMPO0gmyQSkFAt9tt6hOHbHofk2H2/TMS6wFkBrXhBNRjh6Pf\nmZuPwMwFcWwl/UFeUZQgIz3BgXBS375wXrYYypNHodQdS3t7CRMESAjolavRhdxwAiefXwrnl+sg\nTT8X9lPGwp9BdXBSlUhGI+sSEFGmYXDAIsw4rSDTsiGytSCg0cw+eDb7/ukheGyFTwXonGHDegDG\nkbubUnDuJYC753nmgpr+9023da0FAUJOLnJuvA1tT/wKSktTz8/RkXPEyPTWGohToKUJdb/7JWx9\nB6Bo/iIIo09He3tmLveYCEEQkvoeYl0CIh3p9XuQhRgcsBCzzdE3atAVaypAthYENBoHzxQUbz0A\nv99vmu8xM1Bqoy9hKFSNRKB6Ys8bUFXNawFE7Y9eXzOCCKgKUFwM9w23oO2p30L1ZcagV3A6gQbj\npzuIFQOhHNgDAAgc3o+6p34BW0UliuZfCWn0OHi93qw9xlM9z2JdAiIyEoMDFhK80m6WtLV0ZkN0\nLggYa85yrKkAFD+zv3YMfnTVOQCQaD0ASp7Wr6ca8EPZ+U3XO2x2yOdfEdc2REGvlQp0+iyFHe9C\nv/5wXXU9PH/8PZABgzzH0GFQtV66MAmCEuiyPkXg4D7UPfkQbP0HoWj+YtirTw99D2QTrS7CsC4B\nERmBwQELMdsgRYs6Cp2XL4uWruzz+Qyds2y2jI9ozPS5pA7BE9vOQQDWAzBOOo4zZVct4Pd1beus\n86AW945rG7otY6jb4DzydZZGnALXJYvgXfmCTu1HJxaXQNW5CGE00qAqKPt2xbw/cGAP6p74X0gD\nhqBo/iLknDoeHo8na6bjaf17zboERNpjQcLYGBywkEybo5+qRIId0QoChqcrBwcpmZgFYIXggJmZ\nLSgXjdPp7HbFjWysB8DjLT5RVyno1ReBKXPi3oZeyxiqij6Dy2ifHNuESXCePIn29/6mSx+icZT3\ngbLP4FoDogi1Ob4aDPL+XTjx+IOQBlah8DuLUTimI0jg83UNRmWSdP5esy4BEaUbgwMWYrbgQOf9\nMWtBQCsMLs3MLO+fJEkRWQDBAJskSXA6nZxmY1Fd6g0IAtR5iwAp/tMLfTIHVCh6XXGN8fm3z5kL\ntbEBvi8+0acfYWz9K40PDACQBg2Fsmd7Qs+R9+5E/WM/Q+OgYSj4zhUoGjMBXq8XXq83Tb1MjR7B\nfNYlIEqRicZDWmNwwELMEhwIFi1zOp1wOp0oLS0NXan0+/2mKwholsElZb7gYD+RegBlZWVoasqs\nauxa4XHXPaXuKNQTkUv1CWOnQB4wNKHt6LNSgQSo+gQHupu+YF9wGZSmJgS2bdalL0E2p2TY0oUh\ndjvUuqNJP13esx0Nv/0ZmoYMR8FFi1B06unw+XzweDwZFZDUM9OPdQmISGsMDlhINg0y4ykIGJx7\n19DQkFEnBlrLpveNsoMgCF2yADpn2Rhda4PSQ8vvSmVbp6yBnDwEZs1PZkua9Kdbogjo8FlWBRHR\nJxZ0ECQJzsXXQP2/JyAf3Jf2/gCAY/gIKAeNrzVgG1gFede2lLcj76pFw2+WoKlqJPIuvAIF1WMh\nyzI8Ho9lv69Yl4AoMTyvjo3BAQvJxMyBzlMB7HZ73AUBRVGE0+k0dWAAyMz3jbKDKIpdggCdjy8z\nZdlQ97Q+GVJqO9UbOPcSICcv4e3okzkg6DJ5QRDEHn+TBJcLzutuhnfpo1DqT6S3Q3Y7hJaTOpV8\njE3IyYV8cK+m25R3foPG39yPlmGjkTvvUhSMOg0AQsudGsXoQQfrEhBRKhgcsBBFUWC32w1pu6eC\ngMFBSiJFy6wyaGbmAPUkVj2AbCi4SdlJ9bVD2f3tknjC4BEInDopiQ2pMefoZ6U4MxTE/Hy4rv93\neJb+Gmpba9q64xw+IiOWLpT6DoC8O/WsgWjk7ZvR9OvNaB1RjZzzFyJ/5KnIycmB1+tFe3t7WtrM\nBqxLQNQNC4wfksXggIWke5AZPhUgOFAxQ0FAo1khOMAVGXoWTz0Av9/P44t0oez8Bgh+zmw2yBdc\nkdR2RMFsx3z839Vir95wX3sz2v7vcSCg/ZVuIb8A6mF9pi5024/CIsj7018MUd62Cc3bNsEzcgxc\n5y9E3rBTUFRUhPb2dni9Xsv+vrAuARElgsEBC9HqSnuwIKDZli7LVFYJDlAH1gOgbBBRb2DaXKgl\nfZLajj7J/gB0mLqQDHHgIORceR3ann9a8wwKZ+WAhFcGSAextLeu/Qh88zVavvka3tHj4Dx3AXKr\nRqCwsBA+nw9erzft5yWZ+nsWXpcgeL5GZFWCaO7z6lQwOGAhiQQH4ikIyKXL9KGqqumnT1gxc6Cn\negDhWQAMspFWtDrG5OAShmV9IE85J+ntiDrV0Fd1Cg4k8+qKo6rh+s6l8L72imb9kMr7Qtm7Q7Pt\nJUso62NYPwKbNyCwpQbto0+H87yL4a4cgvz8/LQWL8yW3zFRFOFwOFiXgIi6YHDAQjoHB1RVhaqq\ncLlcXYIA8RQEJH0oigKbzfyHqlmzIyRJgiAIyMvLYz0AMpRWx5hy9BBwsqOQnjpvMWBLvpaNqEvm\ngAoomZ2hYJsyDc7Gk2hf/a4m3bDn50JpTnOxwziIublQ6o/1/MB0UVWojfXw/Ob/g/+0ifDOmQ9H\neQVycnIgCILmxQuzJTgQxLoEZFmCuS+6pcL8I44ssHXrVqxatQqqqmLy5MmYM2dOxP0NDQ1YsWIF\nPB4PFEXBRRddhFGjRsW9/UAggLq6Ohw/fhwtLS04cOAAjh49ikAggPPOOw8TJ07M2qrlVrjibJVp\nBdm8jz1l2oiiyHobZBrKto5VCoSxkyFXDktpW3pkdgqCBKg6LGMIQE3h99M2dx6UxpPwb/gipX64\nh4+AnAFLF4r9BkDZb3A/BAHweQFVRaDmMwS+/id8E86Eb/ZFsJf0gsvl0rR4YbaejwR/wwBwKUQi\ni2NwwGCKomDlypW49dZbUVRUhF/96leorq5GeXl56DHvvPMOxo4di2nTpuHIkSP43e9+h/vuuy/q\n9lpbW7Fp0yYcPXoUx44dw8mTJyGKIsrKytC7d2+MHDkSQ4YMQXFxMRwOBwCgqalJl31NB0VRsvbH\nOF7ZPnCOR7bsY7L1AMrKyuD1eg3sOZF2lG0bgZxcBGZfnPK2RKjpn1ggiLoEB1K9EiUIAhwLF0Ft\nbkJgR5KV/UUJiqclpX5oRUBy0yy0JA0aBjW8GKIiI/D5hwis/wT2yTMQmDEPYn4B3G63JsULzXA+\nwroEZAmsORATgwMG27t3L8rKylBWVgYAGDduHDZu3BgRHAAQGlh4PB4UFhbG3J4sy/B6vRg+fDjO\nOussFBYWRkwlKC8vx9GjR7P+xysoOB8/m7IdEpUtA+dUZNo+9lR0k/UAyKpUrwfKvp3AvMVATl7K\n21Pk9A8+BEGfsoeCKKWUOQAAgs0G51XXQ/39Y5APH0z4+c4RI6HuM74IoVQ5BMoBg7MGbDagMcbU\nioAf/rXvwv/5h7BPOwfK2XPR5sqBy+VCYWEh/H5/KFszEWYIDgSxLgGRPmpqarBs2TIoioLZs2dj\nwYIFEfevXr0azz//PEpKSgAA5513HmbPnh26b9WqVQCASy65BDNmzEi5PwwOGKyxsRHFxcWh/xcV\nFWHv3r0RjznvvPPw1FNP4aOPPoLP58Ntt90Wc3sFBQWYPn16zPuDg2mzpIxl2qAyHbiP6dM5AJCu\negBWmP5C2SHVz6C68xuIlUPhP22KFp3RZxUBvb5aNPoOE9xuuK67BZ4nH4XS2BD/83JyoB4/pEkf\nUiIKUDMge8E2aGjPqyT42uH/xxvwf/oBHGefB3XqbHi9XjgcDuTn50NRFHg8nrivoJvxe14UxVAm\nAZdCJLMQMqTmgKIoeOaZZ/DjH/8YpaWluPfeezFhwgT0798/4nFTp07FjTfeGHFbS0sLVq5ciYce\neggAcM8992DChAnIy0stcM/gQAbqPEhav349Jk6ciJkzZ2L37t1Yvnw57r777qQq2AfT8M1Cq+UZ\nMxmDA6mJZ+WN8EyAdGBwgDJBIseYKIqw2WwR02hEUcSJ/bvgu2CxRv3R63jQqx3tvsOEwkK4brgV\nnqWPQvV64nqOc/AQKHtqNetDsqSBw6DsNTh7wemCcjSBzIu2VvjefhX+j9+DfeY8qBOnw+fzwWaz\nwe12QxAEeL1e+Hy+bjdj1u/54G9YaWkpTp48yboERBrZsWMHysvL0adPx3LAU6dOxRdffNElOBBN\nTU0NxowZEwoGjBkzBjU1NZg2bVpKfWJwwGCFhYVoaPj2ysDJkydRUFAQ8ZjPPvsMt9xyCwBg8ODB\nCAQCaG1tRX5+fsLtmW0wbYWBs9nes2i0eB+Dg5lY9QD8fj9X3kgDswY+zLY/sXSeQmO322NmzyiK\nAl9+MeSSXpq0rc9KBfpNK9Ca2KcP3NfehLannwB6+M6SynpBCZ9bbxTJBrX+uNG9gG3A4KQCJWpz\nI3x/fQH+j96BY/ZFUE+fikAgAFEU4Xa7kZOTA4/HE7N4oRm/C6NhXQKi+N1zzz2hf8+ZMyei8Hx9\nfT1KS0tD/y8tLcX27V2Dq5999hm2bt2Kvn374tprr0VZWVmX55aUlKC+vj7l/jI4YLDKykrU1dXh\nxIkTKCwsxIYNG3D11VdHPKaoqAi1tbWYNGkSjhw5Ar/fn3TKiNkGmmbbH6tK5GQqG+sBWCGIRZlN\nkiQ4HA7Y7XYUFRVFVCYPFtPsaQqN4vEgMGayZn0S01+KEACg6hQMTMegUBxcBfcV18Dz4rMd0zBi\nsJeVQNnfqHn7iZIGDYWyO8liihoR8gqgpLhag9pQh/aVy+D78G04z1kAqXo8WltbIQgCXC5XzOKF\ngiBkzO9OOnT+jLMuAWUtHQsSBtP+o4l2zHQ+Xxw/fjzOPPNM2O12vPPOO3jiiSdiFqbX4lyTwQGD\nSZKEhQsX4qmnnoKiKJg0aRL69u2Lv/3tb6isrER1dTUWLFiAl19+GWvWrAEAXHnllUm/+WYbpARr\nKFB2i/a5jFYPAOgICPn9/rgGM5ki0/tH0WXjd2Ws4yZ44q6qKlpaWpK62qc4XIBfu4G2PtfzVahK\n9gYHAEAaMxaupovhfWNV1PvtAwcZv2QgALjcUI7sN7oXkPr0hbJ3hybbUo8dhnfFUogVA+E492LY\nRpwKj8cDj8cDp9OJgoICBAKBUPFCM2cOdLdv4XUJZFk2dYCESEulpaU4ceLbwqknTpyIqEUHICJT\nfM6cOVixYgWAjkyBLVu2hO6rr69PaKn7WBgcyACjRo3q8mZecMEFoX+Xl5fj9ttv16Qts11pVxQl\ndPJL2SdYD8DhcECSJDidzoh6AMEgQDrrAeglGwea8TDrtIJMFz4NILyORjALINpx43A44HQ6kz6W\ntH6PdQkOCKIuRQ9VIK3t2KZNh/NkA9rXfhB5hyBAEhSdcjC6Z6sYCNngrAGxpAzK/l2ab1c5uBfe\nZb+GOGgYnHMvgTR4ONrb29He3g673Y68vLzQ8dFTXYJs1dPKUMHfgvCAJKfwUaYSMmQsVFVVhcOH\nD+PYsWMoKSnBunXr8L3vfS/iMQ0NDaGAwT//+c9QPYKxY8fixRdfREtLRwHYr776CldeeWXKfeKo\nymLMFhwwWyaEWfVUDwDo+Gw2Nzeb8mSCn1NKRnfFNIMBgETqaKT6GVQUjYMDOgzaBVEEZB3aEaS0\nhzpsF3wHQkszvDX/DN3mHD4SygHjaw0I+YWQD2g/KE+UWFgEpSn+FR4SpezZDs/vHoY0vBqOuZdA\nqhgIv98Pv98PSZJQUFCA3NxceDwe0wUJEg0Csy4BUc8kScINN9yABx54AIqiYObMmRgwYABefvll\nVFVVYcKECXjrrbfwz3/+E5IkIS8vL7RqXV5eHhYuXIh7770XAHDppZemvFIBAAhqAkf6oUMZsEQO\npSQ3Nxc2mw2NjcbPTdSCzWZDbm6uafYnlrKyMtTV1RndjR51Vw8gmAUQ/Au/AuF0OuFwONDc3Gxg\n79OnsLAQbW1t8Pv9RndFc8XFxWhsbDRdGqme71l4ECA8E0BV1S7HTSrBs1SPM4/XDy3jA26lNe1X\n9QXJBsg6HHeSXZdjQA340f7MUwjs3gHB6YSjKB9qS1Pa2+2JbchwyD0tG5hmYnkFUHdEvwYFAdLo\n0+E8dwHE3v0AdKT/Bqcc2O12eL1eeL1e/fqURna7HXa7HW1tbUk9n3UJsku/fv2M7kJatf0h+px9\nreXccL8u7WiJmQMWY7alDK1ScyDTUrejzWsOVjhPph6A2a+sm33/zLxvWuqcQWO32yOKaYZPBci0\nYIuqqpoGBqCquqT767ZSgSACOiT3CzY7nFffCOWp38DeqwxqBixdKJT2grwvA7IXbDZ9V6VQVcib\nvkTblg2wjZ0Mx5z5EAoKIMtyl+KFPp8PHo8nY37Dk5HqOQjrEhBlBwYHLMZs0wrMFuyIxYjgQLSU\nZpvNFpoKEGteczLMPnjO5hNCSlx4ECCYCdA5g8br9aKlpSVrTpC1/gTrt7ig+Y49IScHzutvhfra\niozYOzGvAEqDsZlt0oDBUA/vM6ZxRUFg/ToEvvoc4pyLoJx6BsSyPlBVtUvxQlmW4fF4snL6nBbn\nIKxLQBnDRGMhrTE4YHr+so0AACAASURBVDFmCw5YLXMgHbqrBxAcyCQyrzkZZg8OALy6bkaxptEk\nsjyg3pLth6pxvQFR0On1yJDXXWtSaRkC1/wnbJ+uRmD124BszJxuqV//tBQATIggAP52Y/sAAHIA\n7d98Df87f4FUdQrsk2dCGjUWgihGFC/Mzc0FAHg8nqyaahb8btMS6xIQZR4GByzGCoMwM9LifYun\nHoBRKc1m/1yaef/MvG9BwWMnmAWQrctqpvI+KRrvl6jTNW9Vh6kLHe3o/L4LAgRJgnrmbNiGV0N5\n42VjljMUjA/OS4OGQjU6QAFAKO8P/96O6RXyji2Qd2yBUFAM+8SzYJt4NsSC4ojihW63Gzk5OVlT\nvDCd2YuiKMLhcIQyEzP5e5RMwuTnLalgcMBizJY5YBWJDMBi1QMInwqQaQOZTOkHWVv4MRMs3qeq\nakQmgBmW1UyGxokDOk0rULXveKyWdApChAgigI6ruGqvPsC1/wnbl+sQeO91wK/PQFOqHAzlwB5d\n2orJJgGN6VudIBECuk5iUZsa4Hvvr/D9401Ip5zWkU0w9BTIsoyWlhaIogiXy4WcnBx4vV60t7dn\n7O+hHlMbBUGAw+FgXQIiAzE4YDEMDmSnzsGBeJY4y7aBjJmvPlvh6no2ibYyABB57Ph8PrS2tmZV\n2m86aT2tQNCheB+gT5FAFYLu0xc6tyaIItQzpkEaegrUN/4EZXeaCxUKAlSvJ71txME2cBiUvcau\nkgAAYsUgKAf3xH6AIkPevB7y5vUQyvrAPmk67OOnQcnJRVtbW6guQWFhIXw+H7xeb8YNjPWqe8S6\nBKQHgWOhmBgcsKhMqnxPsQXrAQTXNgUQMT9Pj3oAejD74NnMx1qmvnfxFNTs7thxOp0G9DpzaT2t\nQNBjpQJJBOT0tyPZbAjo0E64mN8pxaXAd/8dtprPEXjnL0B7epbRkwYNhbJ3R1q2HTenC8qxzFhi\nW03gdVbrjsL35p/ge+cvsI05A/bJMyANGBJa9tDhcCA/Pz/jihcadd7IugRE+mJwwIKCFf7NMmDJ\ntGX+ktFTPQAA8Pv9aG1tzbirCVrI1AGmlsy+f0YJDwKEZwLoWVAzmyTzPamqquYXxvUIDnQkeqef\nTjMXInT3PgqCAHXcJEhVI4G3VkLetknbxiUb1JMntN1mEmwDBkPJgKUcxcohUPYlUfPA70Pgy48R\n+PJjiBUDYZ80A7axk+AD4PP5YLPZMqp4YfCcxMj2WZeANJMB9VIyFYMDFhScWmCWQWZwf7LhxD/a\nlUwAPVY3z8nJAQDTvGdWY5VVNdJJEISIgoDBIEB4Fo1RBTWzRbIBqrScg+uROSDotGCizoE/VYhz\nGkNBIXDFjbBt2gD57VehtrVq0r40eCiUXds02VayhLz87tP49SKIUJsbU96McnAv2lf9Ee1/+xPs\np0+FffIMBHr3Q1NTU0TxwmBdAiNkSoCbdQmI0ovBAQsyW92BTLvqHE86c6L1ADi4pEyl9fEXvrRm\nMBhgxKoavCr1La2nFOg2P1+391Df3x9BkLoWHeiGWj0O4uBhwN//DHnT+tQad7qgHDmQ2jY0IPWp\nyIxaAwOroOzRsB9eD/zr3od/3fuQhoyAbfJMqKPHoUWWIQgC3G43ioqK0N7eDq/Xa9nvKdYloJSJ\nmTNuyDQMDlhQpg2mU2VUsCN8EBPrSqZW6cxme8+shu9fV6IodskECGYAZcqqGnzPOmj9+kuCXlf6\ndFqpQJdWwsSbORAuNw+45GrYqsdBfvMVqM1NSTUtDRhkeNaAWFwGZf9OQ/sAoGN6xYljadu8vGsb\n5F3b4MsvhG3CNNgnzUCbWoK2tja4XC4UFhbC7/fD4/FY/uo56xIQaYfBAQti5kBiYtUDiLa8Wbp+\noDm4zG5Wfv+Cx094IEAQBMiyHMoEMDoIQN3T+mtNp2R//T5Pen9uw5YxTJQ6vBpC5RCI770Bef0n\niTWbmw9l/+6k2tWSWFQMpdn45QvFyiHpXxUCgNrcCP8Hb8K/5i1II8bAPnkGPMOrI4oXKooCj8eT\n1oFxNnw/sy4BUeoYHLAgBgeiizYVQBCEiKkARg1izPaekXkEjz9JkrpkAgCIGkQj4yTz3aX1tAJR\nl+CACij6pBmrOl+1TfXVE1w5wIWXwzZ6HOTXX4J6sj6u54nl/XQZDHfbhz79oOxPovif1hxOKEcP\n6tumokDeWgN5aw2Ekl6wT5kFTDsnVLzQ7XZDEIS0FC/MtuA26xJQTwQWJIyJwQELMtv89UQGzumo\nB6AHK195NgMzvX+d6wE4HA643W74/f4uNQEosyRfkFDjZQyhx4m6COjQjioI0H1igUbvhzp4GIRb\nfghp9dsIfP5ht9sVSkqh7DU+lV90OHT59PRErBgEZbdx0yvU+uMQ9nwDsaoKSr8qBAIBNDc3QxTF\ntBQvFAQh6wbY4XUJgqvXEFHPGBywIEVRIEmS0d3QTLRgR+d6AHa7PVTUTOt6AHow0+CSMl+0IFrw\nOyN4/Pj9frS1tSEnJwc+n8+wCtqUXtm6jKEgitrPh4jWjiDpn0mmYXuC0wV17gJIo06D8vpLUOui\nz6GXCoohx5lhkC6OQUMROGD8tAbk5EI5tNfYPrjcyM0VgB0b4OtXFbpZURS0trZCEAS4XC7Nihdm\n+3LR2dx3ShMWJIyJwQELUhQFdrvd6G5oIliExuFwdFsPoKWlJeui3uGsEBwI7qMZf8Qz9f0LDwIE\nMwEkSQpl0vj9/h6DaJm6b6SN9CxjqMMxrtdnUhQBHQPMcS9jmKgBgyHc9F+Q1r6HwMfvRwRWxD79\nIBudyi8IkL0eY/vwL2KfCsOnV7hGjoKktgPH90NoqoNaUBZxv6qq8Hg88Hg8cDqdKRcvNOtvMxF1\nxeCABWXj/PVoUwGAjvnMwR8sMxc1s8IAjMGB9BEEoUs9gM4ra7S3t2d9EI20p/kyhtApc0DQq+yh\nvse1KEhQ0rRjgt0BdeYFkE4ZA/WvL0E5cvBft9sN/16WBg6FeiADag3kFxpelFEoLEau3Rf6v21H\nDfynz4n5+Pb2drS3t8NutyMvLy8UOEhk6le2/zZnc98pTVhzICYGByzI6IFKLMnWA7DZbMjLyzP1\nfLJMfc+0ZIV9TLfw6TTBYEBwOk3negAMAlhToifJitYjUVWFAYv/mYcoAnKaj93y/sCN34ftk9VQ\nd2yFYnQqvyQBzSeN7cO/iKW9oOzZYWgf3MOGQsS3WRTSvq3wV08DHK5unxesCxMsXiiKIjweD3w+\nX7fPAzp+WzjAJrIGBgcsyOjMAa3rAXBQaQ5mfh+13jdRFLtkAnSeThOsWJ3uEzozv29mk8zVP60/\nP5KgU1BKp4GM3sMlQZTSHxwAIEg2qNPmQK0eD/GDN6FsXp/2NmOxDRoGZe92w9oPKS6Fss/Y7AWx\nd1+4ETm9QpADsO3ehMCICXFtI1bxQq/XG/M52ViQMBwDG9QFz1tiYnDAgvQKDgTXNw8PAgTXN9ey\nHoDRwQ7ShpkHmcmemIQfQ8FgQPAYCmYCmHk6DRlP68QBvZL9VR2mLnQ0lL0DpriU9oa88DqIU2dB\neP91KLt0rtDvcEI5fljfNmMQ84ugNJwwtA+5gwZAVNu63C7t+gqB4acnlCodrXihz+eDx+Pp8nuS\n7cEBIoofgwMWpPVSht3VAwgGAdI5gDHzoNJKzP4+drdvkiR1yQQAIo+hlpaWiBobmSLT+kPaUjWO\nDoh6XWvXYSCjAlB1HjDJOmQNhAvWnFD6VgLf/Q9Iu74B/vE6lEP7dWnfVjkEyh5ji/8BgNCrHIrB\nRRmlyiFwRQkMAIDY1gTx0E4oFcMS3m7n4oUFBQWQZRkejyeUvcmaA2Q6vKgYE4MDFpTMl2TnegB2\nuz2iqnmsegBE8TJzcCB8veXOKwMAiMgEyMZjyKzvm9Wpqqr5UF7QZZV6AdCjHQMKWumWEQFETbuV\nh4wEBo+AtLUG6j/egFp/PH3N5+Ubv2TgvwgOp7EDTEFAXt8SQImd+m/bUQNfEsGBcOHFC3Nzc0OB\ng2wPDhBR/BgcsKhgKn7nNDGt6wEQxcssJx6CIETNBJAkCXl5eaElAnkMkRESOc7SURVfj6WlBVHU\nJXNAEEXNMyu6b/D/Z+9NgySpzrvf/zkns9burupl6B6mp2dgGJahQYAHGINki+XVq4v9CtnEvbKt\nQNbmCGMbWZY/CMtyWOGwwopwGIcthYKwpZA+YEdIFyO/kl+sqwVjyQakQcyIQQLEwDBoYLbumV5q\nz8xz7ofqrKnqruquJfPJpc4vYmJ6qc6TWZXLef7neZ4/bYDGuGjfVIExOPuuB664FuLHT0M98e9Q\nhRXPxxczOyBfC77XAJuZhXwjWJHC3HslEpsIAwAgFk6ALZ2Fym8beDy3eaEQAul0GolEopHJptHE\nAu1W0BEtDgwhUkqcPHkSr776Kk6dOoXTp08jn8/jN37jNzzvB6Dxjjhb/blEaQW6UyaAm01jWVaL\nkDY1NYWlpXB03PaSOGd8xI1ePyc/Al8O5f+aPuckiQP1ySWdwMcNE45NnDmw2fNGCDg33ArM74dx\n8Htw/vs7QKXc+fW9DJ2fDDyN34UhYH8Nw0B2PA3IrV0FjFcOwfqFd3g2tOM4KBQKGBsbgxAC+Xwe\nlUoF1Wo1UnORKO2rRhM0WhwIGS+88AIeffRRKKVw4MAB3HnnRu/aQ4cO4Zvf/CYYY7j44ovxvve9\nr+22bNvGmTNncPr06ca/hYUFKKUwMzODmZkZbNu2DXv27MHk5CTOnvUvPVAzOG62R1xXm8MaZDLG\n2mYBNGfTVKvVoRbSwvi5DYqeTF6oN/d0mwQrjwzel0N0GokSzgVoVI86qtuwOJGEfev/ALvhFoj/\n/g6cH/4nMODnLMYnIAvBi6l8x67AswaSV14NswthAADE6y/Cmn8bkEx7ug+MsUbzwmQyiVwuh1qt\nhkqlMrTPPU3EoUhjiyhaHAgRUko88sgjuO+++5DP5/Hggw9ifn4eMzMzjdecPXsW3/nOd/CHf/iH\nyGQyWF1d7bi9L3/5y0gmk5iensb27dtx3XXXYdu2bRBCYGJiAsViEdVqleLQfGcYVtXDGjx7RdDH\n11xS44oBzSU1lmU1+gHoydBwEOfrrRs8FweUAsUarBACtm35Pg45IT8fVToL+867wW78JfDvfxPy\n0NN9WUry6YshTxzzYQ97RwU9R0pnkc2g68uGSQfGsSOwr7zJ091w74VKqYbtYTKZxOjo6IbmhRqN\nJtpocSBEHD9+HFNTU5iamgIAXH/99Thy5EiLOPDUU0/hrW99KzKZDABgdHS04/Y+/OEPd/xd3Oz/\n4r6qDgQfPPuN1y4aneCcb8gEcM8dNxOgXC7DsqxYi01eod+j+LC+V8bZxSU4jndBNmc0opok6uhP\nfe4r4kyFfsUhlRuH86u/CXbzbeBP/B/IF37c09/zZNKXrJVe4XN7IF9/JdB9SF15JYTqTaCo2xru\n970b+/rmhQAaz84woZ9RmrbongMd0eJAiFheXsb4+Hjj+3w+j+PHW9PZzpw5AwD4u7/7O0gp8c53\nvhNXXXVVz2NRBWJUxD1wBuJ/jF4fnxBiQyYAY6whArhNAf2y2Bwm4nxexg2lVOPaaBYCALRcG5VK\nBbWat5N8KhtDKWlEYnJxgDBjSWHw41PbZuD83x8CP3EM7LvfgDx+dMu/4bO7Id98faBxPYFxsKL3\nTRZ72oX8BLJGd+UEzfByAeLNl+HMXuHDXm1kffPCTCbT6Eug0WiihxYHQs76SbeUEmfPnsUf/MEf\nYGlpCX//93+Pj3/8441Mgm6RUsZqQh+3TIh2aHGgPe2cAQC0ZAIUCgU4jqNFAM1QsV4ESCbrdmyu\nbWZzqcx6/LhWqMQBCqcCBQCUtoIAHKKMCGDN8cEj5OwlwG9/BOLoT6Ee/wbUqTc6DMrAHCvY5n9r\n8F174ATslJC+7DJwlPr6W3H0sKfiQDf3A7d5IWMM6XQa+Xwe1WoVlUol0Gevfu5rNL2hxYEQkcvl\ncP78+cb3S0tLGBsba3lNPp/Hrl27IITA5OQkLrroIiwsLGBubq6nsaSUDY/1OBD3wBmI/zFudXzr\nLTbd83f9aqe2WqJFT7yCp9tMAMdxUKvVulrRkz44FTCysI9gHEb7/FRgZBkRAMAY9/xtdC7bB+y5\nEuInh4AnHoM819oEWey6DOpECBwKhAG1eCbQXeDTFyONIvpteikW3wQ7fxpqfHrgfem1n5NSCqVS\nCaVSCalUCrlcDpZloVwu6349mvAQ4/n0oGhxIETMzc1hYWEBi4uLyOVyOHToEO69996W11xzzTV4\n9tlncfPNN6NQKODs2bOYnJzseSwpJUzT9GrXA2cYMgfifoyuOLA+yHFFADcLwO0JEOf+ElEjzqJV\nmOhWBPBCIPNBGwAjWGlnXACSwBGBc9I0f8YFrZ8e54AfmQqMw5n/BeDKayEOPw35n/8OFAuAEEAI\n3AkAgO+8FPK1nwW6D9lds+Cqv6wBF+PoIVg3vnPgfRmk2bPbvDCRSATWvFAL2BpNb2hxIEQIIXDP\nPffgoYcegpQSN998M7Zv347HHnsMc3NzmJ+fx5VXXokXX3wRf/VXfwXOOd71rnc1GsH0QtxWoeN2\nPO2IU58IVwRYnwnAOQdjDJZloVaroVQqaRFAM3RQigCd8GdCTRBMUz0HiJ839ZV8OjFCCAHbzzIG\nw4Sz/23ANfshfvg98Ddfg3zlRf/G65ZEEvJMh7IHIsSuPUgNKAwAgDjxM1jX/BKQ6q3sdD1eOEHV\najXUajUYhoFMJgPGWCibF2qGiJjMp/1AiwMhY9++fdi3b1/Lz+66667G14wx/Nqv/drA48RtFVpK\n2ZhAx5UoCiCMsQ39AIQQDXtA27ZRq9VQLBbBOcfIyAiWlsKxeqTpDr0q0z9BiADdfl6+lBVQZA6Q\nFS/Q3osV8b3fsolE2WQaztv+J1Asgj/+vyGPHKQZtwN8xy7IYwFmDTCGkZlxQFYG35R0YBx7DvZV\nBwbaDufcs/u8bdtYXV0F51w3L9RoQkq8oylNR+ImDkQxcO6VMB8j53xDJgDnvCECNAc5nWoOGWOh\nPT7N5ujPbXPCkAkA9PY5eW4lp1Rfnvc9o09FT6AW/ZzsKOxffS/EdbeAf+sRqNMBrN5nsoE7JZiX\n70PCA2HAxXj1OdhX3Ajw/ntkMMY87xUgpUSxWARjDKlUytfmhVrA1rRFz1s6osWBISVOKepA/NwX\n2hEGcYBzviETgHPe4gxQqVSwurra8wM5DMfnJ+7x6YlKfAmLCDAoSinP43jOiM57ouuL3MaQeCzK\n46sPVR/Pmb0Ezvv/GOaPn4R64jGgMnh6fbfw6R3BZg2YJrL5BCC9S7VnlSLEiZfhzF3Z/zZ8fG4p\npVAul1Eul3XzQo0mJGhxYEiJWzAdN7GjHZSfmRvkNAc6jLGWIKdUKsG2bc8mDXEXB+LKMIodcREB\nOuHHR0qV7K+I6vKpxmmMRxgocS4oukM0EJy19j7kHNb1bwW74noY3/8/UIee8l/0Gc1B/jxYp4Tk\nlVfD9FAYcDGOHgqtONBMc/PCkZGRhnAw6H10GJ9Rmi5g8Y4ZBkGLA0NK3AKxuB1PO/wQQIQQGzIB\nALRkAhSLRU9FgE7E/TOMc+ZAXD83zjmSyWRsRYBO+HGOcqpwkyCIVmBkGQr18YgDHMZJj68+Sd/4\nualMFtb//H8g3nIA/Fv/AvXGcd92gU9eBPnay75tf0syI8im/HnP+flTYOdOQk1s7+vv/Sgr2Izm\n5oXpdBqcc5TLZdRqNbJ90GiGGS0ODDFu34E4pG7FrYdCOwYJnts5AwDxD3LCRBxFgbjQLhNACNFo\nmBmn66Ob89DzfgMAOFlivP/jCMOETeiiwlj/9eJ9DkgrDmyBMzMH594/hHnkINR/fAMoFbwdYHwS\n8vVXvN1mj6SvuBIC3vUaWI9x9DCsm/oTBzjngdz7OjUvrFR6e5/0s1fTlpjHDIOgxYEhJk4rtXE6\nlk50c4ztAhwAjSwAtydAHIKcKBLHczRKE69eygFGR0dRq9Vi1UW72/PPF72YwqmAC0D6f2/zQzzZ\nFC5IMiJcQnlFMw7r2pvBLp+H+V//H+Qz3/dMwOCjecjzi55sqx/Y+CQywt/7TN3W8G1AeqTnv6XO\nHFgPZfNCjUajxYGhxl1t1z7y0cAVBxhjbTMBlFIt9oClUkl/tiEizgJW2I4r7j0B/MaPCTejKCug\nOg87pMH7Nx6xbSLpaL2dbyqVRe3OX4e49mbwbz8KNeCKP9s2E3ivgbErLgd3Vn0dgylZdy64+pbe\n/zYk5XDNzQuTySTGxsZg27ZuXqjpj5DNW8KEFgeGmGFIxY8yrgiwvifAxMREiwhQLBb1g1EzlPgp\nAsRZzNkKP1bGGUFwwcjaHsYbyudJ3eGy90/NuWgHnPfeD+OFZ4Hv/m9gdbmv8VkiFWjgy7fPwrBX\nSAIV49gR2FfeBIjepv5hEQeaqVarqFarME1z0+aFYdtvjSYKaHFgiNHiQDjgnG/IBHB7QawPcCYm\nJrC4GFz6o6Z/4hpsUky+dCYAHX7YGNYjQIrMAf+HAIJYWSccjPgeVQ88+/97+6obgEv3wXzq21A/\neAKQ3WfLsZlZyDde639wD8juvBhc0dg1smoJ4sRLcHZd3dvfhVAccLEsC5ZlQQiBdDoNIYRuXqjp\nDu1W0BEtDgwxcQ1WwoorAjQHOG5ZR3M/gEKhoDMBYkhYJ1de4NV9RIsA/rPVeeiLjSEjOveprjHi\na5nUNpG4+SFnDM6g72cyBevt/wv8mpsgvvMo1KsvdfVnDMH2VzB2X4YUkTDQGPPo4b7EgbDjOA4K\nhQI450ilUo3mheVyOehd02gihxYHhpi4ZQ6ExX3BDXCaMwEYYy0BTqlUIrEH1ISLKEyyKNAiQDB0\nc/5F2amAKohWxM0BKZ8TjDNA0o1nmCacqjervHJyGvI998H42XNg3/lXqOVzHV/LZ3dDnnjNk3H7\ngnNkp3OApG14ypfOgC+8ATm1g3RcKqSUKJVKKJfLSKVSyGazWFpaCnq3NGFEz8c6osWBIUZK2ehm\nHweoMyHaBTiMsRZnAC0C9Ib7Gcbx/Yprps5mn5UWAaKHH9cep2reRyAOKEa73kxuY0i8nu74cO3b\nl18L7L4C5g8fh3rqu0CbMVQl2BVl8/J9SBALAy7ilcOxFQdc3B4EQS8WaTRRRIsDQ0zcghUppS/H\n05wFEHSAE+fgGYj/8cUVzjmSyaQWAWKAH3NpsjaBFE0PmaC9P3FOupIfm34KiSSst/5f4PM3wvju\nv0L+7PnGr/jcHsgBXQ4GwkxgNGcC0gpkePHGUVilVSAz2tXro/w8jvK+azRBocWBISZuZQVKqYGO\nZ/0Kp5tV0ZwJEHSAE/fgOW6CVTNxOLZO2TKccyQSCS0CxABfnAoIQk7GBSAJzjvGAUVoEUucqUD9\nbPHjfGvZfn4KtXs+DOPVn4J9+2tQ5xehVoJNM09edTVEQMIA4Noa/hj2/Fu3fm2M5xuaISdG8Y/X\naHFgiImjOLBV8OXaAzb3AxCivhLUbA9YKpXgOIQTwC7xKzsiLMQhgO5ElCZYvZQDMMYwMTGB1VV/\nfbo13rB1Q0I/bAwpnAqI7hvk9ye68RT8D9ZbxlPuqP5jX7oP+NBemD95BnjqOyRjtoNlR5FNyWA7\nIQIwjj0P+6oDW9oaRl0ciPK+azRBocWBISZu4kDz8bgiwPpMANceMOwiQCfc7Igo7XMvxP1BHjbh\nQ/cEGC62Ov98sTEEjTjAGFnxAimUt0RGPB/gnPlSxtIRw4S84a1wrj0A8cYx8Oefgfrps0C1QrYL\nqSuugFB043WC1coQr78I55L5TV8XhibPGo0fqJDNx8KEFgeGmDis0rr2gIZhIJVKgXOOkZGRFhHA\nDW7i8ICLw2e2GXE+viCPTYsAmm7wJRBViqRRIBWKWIKgtDFkjJOuaDPikglgTYBmDM7spXBmLwXu\neDeMo8+DHTkI9eqLvqoxbGIbMiKYJoTtMF45vKU4oDMHNJrhQ4sDQ0yUMgdcEaA5uHEVbcuyYNs2\nqtUqGGOxTm+Oc/DsEvfj85MgRIBhOCeHhaj2GwBAt8ROmXYP4uCGMdpUBWJnBKDN+2kmYF91A3DV\nDWCryzBeeBZ47gdQZ095PnZ2z25wBOuS0AxfPgt+9gTkttmOr4m6OKDRdIRFI/4JAi0ODDFhnNS7\nwU1zTwDGWEtw08keMJlMIpFIBLTnNITxM/OSOB/foA0zm9GZABo/kD50xecsPkG7AqAIM9DibmMY\nBJsFumo0B+um24CbboM49XOInzwDeeQZoFwceFxx8U6kQyQMuBhHD6GmxQGNRtOEFgeGHDdgoU65\n79T1vNkZoJMI0IkoZUL0S5yDZyDex9fPsWkRQOM1mwZHPgQBnCjYVBQOAtQrTcQ2htTFH9RBZ73H\nQXdjOjM74czsBN7+v2C8+iL48wchX34e6LPfT2bnDCDDJw7wk6+AFVegsmPtfx/xngNa2NB0RGcO\ndESLA0OO393vm7MA/A5u4hxYumi3gniiRQANBVtdW37EoYwq5CQIAhjnUITBepxtDJWiD9z66nEg\nDNh754G982ClIowXDwFHfgj15utdb8K49HKkQigMAABTCuZrz6F2dXtbQ8ZYpMUBjUbTO1ocGHLc\n1fZBu983lwG4zgAAWjIB/A5uhiVzwH1v40icxQE3SyeZTGoRQBNK/CgroOg5UG+kR5U5QOkUQ2tj\nSBms19PVyYbzBJXJwrrhrcANbwVfOAXxk2egjhwEVpc7/xEXGNk2CsjwNCJcj3j1CEZv/B+oWPVn\nUDO6rEATV7RbQWe0ODDk9FIH7doDrv+nlAqFPWCcA0uXuB+jl3X5QdIuE4BzvmYVp7QIEAHifq2t\nx68AwOAM0ufHARMcyqZ45tCeD5QxGeeCtKyAMwYnwkGnnJqB/OVfBd52F8yfvwL+/A/h/PQwYLcG\n14krroIZYmEAIve5iwAAIABJREFUAFCroPLTg0jOH0Amk0GlUkG1Wt/nKIsDUd1vjSZotDgw5LRL\nU28WAZozAdygJmgRoBPDMJmP+zFG7fi2KgdozphJJBJIpVKxdtPQRBc/nAoAQDr+C2BCGLDXBWVx\ngNLGEIyTqhFB2Bj6Iu5wDmvXXmDXXuCOX4f58pG628HrrwCJJEZGTUCF/9zkLz+Lwu55MMaQTqeR\nz+dRqVQiLQ5oNJuiew50RIsDIeWFF17Ao48+CqUUDhw4gDvvvLPt6w4fPowvf/nL+NjHPoa5ubme\nxigUCjh16hTefPNNvPHGGzh1qm7d88ADD2wIbHTNWTiIWvDcK2E9vl5EgE6E9dg0w0Wnib5vcajf\naQMAmUitKOv/EW8bwyDCTd/Pk1Qa1jU3AdfcBL68iPQrz0Gce8XfMT2CryyCnz4OOb0LpVIJ5XIZ\nqVQKpmkilUqhXC5HTiSI2v5qNGFBiwMhREqJRx55BPfddx/y+TwefPBBzM/PY2ZmpuV1lUoF3/ve\n97Br165Nt7e6uoqTJ0/i1KlTOHXqFE6fPo1KpYJsNoudO3didnYW1157LW6//XZks1ksLi76eXia\nAYh7gBn0w9wLEUCjCSub3Tt8yRygWvkmum9QNiOktjGkvvNS3+qpV8BlbhLVS98CREQcAADjlcOo\nTdfnk0oplMtlJBIJOI6DsbEx2LaNcrmsF4s08SDGc+lB0eJACDl+/DimpqYwNTUFALj++utx5MiR\nDeLAY489hjvuuAOPP/74ptt77rnnsLCwgOnpadx4442Ynp5GJpMBAIyMjIBzjpWVFX8ORuMpcXcr\nALbuqO4FQYgAcRd2NNHBLR1zz3/TNLF4bhmW7W1HdUF2uvsf9CmATuwAyG0M6cUBehtDx6Edcyk7\niynGwSjPmwHgJ4+BFZagRvKNnzHGUK1WUa1WkUgkMDo6Cill6MpK2xH0YoNG0y2HDx/Gl770JUgp\ncccdd+Dd7353y+//7d/+Dd/97nchhMDY2Bjuu+8+bNu2DQDwnve8p5E5PjU1hY9//OMD748WB0LI\n8vIyxsfHG9/n83kcP3685TUnTpzA0tISrr766i3FgVtvvbXj76SUse5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wMQRoShfqmQM0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QUsLS2hWCyiOkj2k4+xGotRLwCqs1cRXidxvyapnRionAqaKeV29vw3cupiIJXtazz3OS6lRKFQ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nQVokWLZr6yKx7J0D/9v/PZ1z7H+5afE6B7g4wOHMMZIkoVgsAgDq9fpEHxjmeTaatT2Losgj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\nRERERBTj/j/T6xIA2zssrgAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_value_function(value_function)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## References"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Reinforcement Learning: An Introduction - Chapter 5: Monte Carlo Methods"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: Week 2/README.md
================================================
## Coding Challenge -- (Not submitted)
This week's coding challenge is to implement Monte Carlo Prediction algorithm. Here it is built for the Blackjack-v0 environment by OpenAI Gym library. By Siraj Raval.
The submission file is: Monte_Carlo_Prediction.ipynb
## Dependencies for challenge
* numpy
* [OpenAI Gym](https://gym.openai.com/docs/)
### References
[Siraj Raval - Youtube - Monte Carlo Prediction](https://www.youtube.com/watch?v=-YpalutQCKw&ab_channel=SirajRaval)
[Blackjack - OpenAI Gym](https://gym.openai.com/envs/Blackjack-v0/)
Reinforcement Learning: An Introduction - Chapter 5: Monte Carlo Methods
================================================
FILE: Week 3/QLearning_MountainCar.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Q Learning for MountainCar"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"import gym\n",
"from gym import wrappers\n",
"\n",
"\n",
"\n",
"import time\n",
"\n",
"# Imports specifically so we can render outputs in Jupyter.\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"from JSAnimation.IPython_display import display_animation\n",
"from matplotlib import animation\n",
"from IPython.display import display"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Util functions (display)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def display_frames_as_gif(frames):\n",
" \"\"\"\n",
" Displays a list of frames as a gif, with controls\n",
" \"\"\"\n",
" patch = plt.imshow(frames[0])\n",
" plt.axis('off')\n",
"\n",
" def animate(i):\n",
" patch.set_data(frames[i])\n",
"\n",
" anim = animation.FuncAnimation(plt.gcf(), animate, frames = len(frames), interval=50)\n",
" display(display_animation(anim, default_mode='loop'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Hyperparams"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n_states = 60 #In how many slices discretize the continuous space, the bigger, the smoother. \n",
"iter_max = 10000 #Number of epochs\n",
"t_max = 200 #Number of max actions taken per episode. If in 200 steps it's not done, the environment takes it as fail.\n",
"\n",
"#Tweaking params\n",
"initial_lr = 1.0 #Initial Learning Rate\n",
"min_lr = 0.003 #Minimum Learning Rate\n",
"gamma = 1.0 #Discount factor\n",
"eps = 0.02 #Probability of take a random action\n",
"\n",
"gamma_list = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]\n",
"eps_list = [0.00, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create environment"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[2017-12-03 14:50:49,466] Making new env: MountainCar-v0\n"
]
}
],
"source": [
"env_name = 'MountainCar-v0'\n",
"env = gym.make(env_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Observation to state (discretize)\n",
"\n",
"Given an observations it returns a discretized [position,speed] state pair."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def obs_to_state(env, obs):\n",
" \"\"\" Maps an observation to state \"\"\"\n",
" env_low = env.observation_space.low\n",
" env_high = env.observation_space.high\n",
" env_dx = (env_high - env_low) / n_states\n",
" position = int((obs[0] - env_low[0])/env_dx[0])\n",
" speed = int((obs[1] - env_low[1])/env_dx[1])\n",
" return position, speed\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Position index on Q-Table: 20\n",
"Speed index on Q-Table: 30\n"
]
}
],
"source": [
"obs = env.reset()\n",
"pos,speed = obs_to_state(env,obs)\n",
"print('Position index on Q-Table: %d' %(pos))\n",
"print('Speed index on Q-Table: %d' %(speed))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Run a single episode"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def run_episode(env, policy=None, render=False):\n",
" obs = env.reset()\n",
" total_reward = 0\n",
" step_idx = 0\n",
" frames = []\n",
" for _ in range(t_max):\n",
" if render:\n",
" frames.append(env.render(mode = 'rgb_array'))\n",
" if policy is None:\n",
" action = env.action_space.sample()\n",
" else:\n",
" pos,speed = obs_to_state(env, obs)\n",
" action = policy[pos][speed]\n",
" obs, reward, done, _ = env.step(action)\n",
" total_reward += gamma ** step_idx * reward\n",
" step_idx += 1\n",
" if done:\n",
" break\n",
" \n",
" if render:\n",
" env.render(close=True)\n",
" display_frames_as_gif(frames)\n",
" return total_reward\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Random episode for example"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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"*batch_size*: Number of episodes to do param update
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"*gamma*: Discount factor for reward
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"*decay_rate*: Decay factor for optimizer
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]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"H = 200\n",
"batch_size = 10 \n",
"learning_rate = 1e-4\n",
"gamma = 0.99 \n",
"decay_rate = 0.99\n",
"\n",
"resume = False\n",
"render = False"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model initialization"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def init_model():\n",
" '''\n",
" Initialization of the neural network model\n",
" '''\n",
" D = 80 * 80 #Input dimen\n",
" if resume:\n",
" pass\n",
" else:\n",
" model = {}\n",
" model['W1'] = np.random.randn(H,D) / np.sqrt(D) #\"Xavier\" init\n",
" model['W2'] = np.random.randn(H) / np.sqrt(H)\n",
"\n",
" #Update buffers that add up gradients over a batch\n",
" grad_buffer = { k : np.zeros_like(v) for k,v in model.items()} \n",
" #Rmsprop memory\n",
" rmsprop_cache = { k : np.zeros_like(v) for k,v in model.items()}\n",
" \n",
" return model, grad_buffer, rmsprop_cache"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Activation function"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def sigmoid(x):\n",
" return 1.0/(1.0 + np.exp(-x))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Preprocessing"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def prepro(I):\n",
" '''\n",
" Preprocess 210x160x3 uint8 frame into (80x80) 1D float vector\n",
" '''\n",
" I = I[35:195] #Crop\n",
" I = I[::2,::2,0] #Downsample by factor of 2\n",
" I[I==144] = 0 #Erase background type 1\n",
" I[I==109] = 0 #Erase background type 2\n",
" I[I != 0] = 1 #Everything else set to 1\n",
" return I.astype(np.float).ravel()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rewarding"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def discount_reward(r):\n",
" '''\n",
" Take 1D float array of rewards and compute discounted reward\n",
" '''\n",
" discounted_r = np.zeros_like(r)\n",
" running_add = 0\n",
" for t in reversed(range(0,r.size)):\n",
" if r[t] != 0: running_add = 0 #Reset the sum, since this was a game boundary\n",
" running_add = running_add * gamma + r[t]\n",
" discounted_r[t] = running_add\n",
" return discounted_r"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Forward propagation"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def policy_forward(x):\n",
" h = np.dot(model['W1'],x)\n",
" h[h<0] = 0 #ReLU\n",
" logp = np.dot(model['W2'],h)\n",
" p = sigmoid(logp)\n",
" return p,h #Return prob and hidden state"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Backprop"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def policy_backward(eph, epdlogp):\n",
" '''\n",
" Backward pass (eph is array of intermediate hidden states)\n",
" '''\n",
" dW2 = np.dot(eph.T, epdlogp).ravel()\n",
" dh = np.outer(epdlogp, model['W2'])\n",
" dh[eph <= 0] = 0 #Backprop prelu\n",
" dW1 = np.dot(dh.T, epx)\n",
" return {'W1':dW1, 'W2': dW2}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def train():\n",
" env = gym.make('Pong-v0') #No module\n",
" observation = env.reset()\n",
" prev_x = None #Computing the difference frame\n",
" xs,hs,dlogps,drs = [],[],[],[]\n",
" running_reward = None\n",
" reward_sum = 0\n",
" episode_number = 0\n",
" while True:\n",
" if render: env.render()\n",
"\n",
" #Preprocess the observation, set input to network to be difference image\n",
" cur_x = prepro(observation)\n",
" x = cur_x - prev_x if prev_x is not None else np.zeros(D)\n",
" prev_x = cur_x\n",
"\n",
" #Forward the policy network and sample action from the returned prob\n",
" aprob, h = policy_forward(x)\n",
" action = 2 if np.random.uniform() < aprob else 3 #roll the dice!\n",
"\n",
" #Record various intermediates (needed later for backprop)\n",
" xs.append(x) #Observation\n",
" hs.append(h) #Hidden state\n",
" y = 1 if action == 2 else 0 #A 'fake label'\n",
" dlogps.append(y - aprob) #Grad that encourages the action that was taken to be taken\n",
"\n",
" #Step the environment and get new measurements\n",
" observation, reward, done, info = env.step(action)\n",
" reward_sum += reward\n",
"\n",
" drs.append(reward) # Record reward (has to be done after we call step() to get reward for previous action)\n",
"\n",
" if done:\n",
" episode_number += 1\n",
"\n",
" #Stack together all inputs, hs, action gradients, and rewards for this episode\n",
" epx = np.vstack(xs)\n",
" eph = np.vstack(hs)\n",
" epdlogp = np.vstack(dlogps)\n",
" epr = np.vstack(drs)\n",
" xs,hs,dlogps,drs = [],[],[],[]\n",
"\n",
" #Compute the discounted reward backwards through time\n",
" discounted_epr = discount_rewards(epr)\n",
" #Standardize the rewards to be unit normal\n",
" discounted_epr -= np.mean(discounted_epr)\n",
" discounted_epr /= np.std(discounted_epr)\n",
"\n",
" #Policy gradient magic\n",
" epdlogp *= discount_reward # Modulate the gradient with advantage\n",
" grad = policy_backward(eph, epdlogp)\n",
" for k in model: grad_buffer[k] += grad[k] #Accumulate grad over batch\n",
"\n",
" #Perform rmsprop parameter update every batch_size episodes\n",
" if episode_number % batch_size == 0:\n",
" for k,v in model.items():\n",
" g = grad_buffer[k] #Gradient\n",
" rmsprop_cache[k] = decay_rate * rmsprop_cache[k] + (1 - decay_rate) * g**2\n",
" model[k] += learning_rate * g / (np.sqrt(rmsprop_cache[k]) + 1e-5)\n",
" grad_buffer[k] = np.zeros_like(v) #Reset batch gradient buffer\n",
"\n",
" #Boring book-keeping\n",
" running_reward = reward_sum if running_reward is None else running_reward * 0.99 + reward_sum * 0.01\n",
" print('Resetting env. episode reward total was %.3f. running mean: %.3f' % (reward_sum, running_reward))\n",
" if episode_number % 100 == 0: pickle.dump(model, open('save.p','wb'))\n",
" reward_sum = 0\n",
" observation = env.reset()\n",
" prev_x = None\n",
"\n",
" if reward != 0: #Pong has either +1 or -1 reward exactly when game ends\n",
" print('Ep %d: game finished, reward: %.f' % (episode_number,reward)) + ('' if reward == -1 else ' !!!!!!!!')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Warning\n",
"\n",
"Pong environment not available on Windows (12/10/17)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## References\n",
"\n",
"1. [Deep Reinforcement Learning: Pong from Pixels](http://karpathy.github.io/2016/05/31/rl/)\n",
"2. [Simple Reinforcement Learning with Tensorflow: Part 2 - Policy-based Agents](https://medium.com/@awjuliani/super-simple-reinforcement-learning-tutorial-part-2-ded33892c724)\n",
"3. [Reinforcement learning with policy gradient](http://minpy.readthedocs.io/en/latest/tutorial/rl_policy_gradient_tutorial/rl_policy_gradient.html)\n",
"4. [Deep Deterministic Policy Gradients in TensorFlow](http://pemami4911.github.io/blog/2016/08/21/ddpg-rl.html)\n",
"5. [Simple reinforcement learning methods to learn CartPole](http://kvfrans.com/simple-algoritms-for-solving-cartpole/)\n",
"6. [REINFORCEMENT LEARNING (RL) – POLICY GRADIENTS I](https://theneuralperspective.com/2016/11/25/reinforcement-learning-rl-policy-gradients-i/)\n",
"7. [Solving the Basic Game of Pong](https://www.youtube.com/watch?v=pN7ETkOizGM&ab_channel=SirajRaval)\n",
"8. [Pong-v0](https://gym.openai.com/envs/Pong-v0/)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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}
================================================
FILE: Week 4/README.md
================================================
## Coding Challenge -- (Not submitted)
This week's coding challenge is to implement Policy Gradients algorithm. Here it is built for the Pong-v0 environment by OpenAI Gym library. By Siraj Raval.
The submission file is: Policy_Gradients_Pong.ipynb
## Dependencies for challenge
* numpy
* [OpenAI Gym](https://gym.openai.com/docs/)
### References
1. [Deep Reinforcement Learning: Pong from Pixels](http://karpathy.github.io/2016/05/31/rl/)
2. [Simple Reinforcement Learning with Tensorflow: Part 2 - Policy-based Agents](https://medium.com/@awjuliani/super-simple-reinforcement-learning-tutorial-part-2-ded33892c724)
3. [Reinforcement learning with policy gradient](http://minpy.readthedocs.io/en/latest/tutorial/rl_policy_gradient_tutorial/rl_policy_gradient.html)
4. [Deep Deterministic Policy Gradients in TensorFlow](http://pemami4911.github.io/blog/2016/08/21/ddpg-rl.html)
5. [Simple reinforcement learning methods to learn CartPole](http://kvfrans.com/simple-algoritms-for-solving-cartpole/)
6. [REINFORCEMENT LEARNING (RL) – POLICY GRADIENTS I](https://theneuralperspective.com/2016/11/25/reinforcement-learning-rl-policy-gradients-i/)
7. [Solving the Basic Game of Pong](https://www.youtube.com/watch?v=pN7ETkOizGM&ab_channel=SirajRaval)
8. [Pong-v0](https://gym.openai.com/envs/Pong-v0/)
================================================
FILE: Week 5/.ipynb_checkpoints/Actor_Critic_Model_Pendulum-checkpoint.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Actor-critic model for Pendulum"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"import gym\n",
"import numpy as np\n",
"from keras.models import Sequential, Model\n",
"from keras.layers import Dense, Dropout, Input\n",
"from keras.layers.merge import Add, Multiply\n",
"from keras.optimizers import Adam\n",
"import keras.backend as K\n",
"\n",
"import tensorflow as tf\n",
"\n",
"import random\n",
"\n",
"from collections import deque\n",
"\n",
"# Imports specifically so we can render outputs in Jupyter.\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"from JSAnimation.IPython_display import display_animation\n",
"from matplotlib import animation\n",
"from IPython.display import display"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Utils functions (display)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def display_frames_as_gif(frames):\n",
" \"\"\"\n",
" Displays a list of frames as a gif, with controls\n",
" \"\"\"\n",
" patch = plt.imshow(frames[0])\n",
" plt.axis('off')\n",
"\n",
" def animate(i):\n",
" patch.set_data(frames[i])\n",
"\n",
" anim = animation.FuncAnimation(plt.gcf(), animate, frames = len(frames), interval=50)\n",
" display(display_animation(anim, default_mode='loop'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Class ActorCritic\n",
"\n",
"Determines how to assign values to each state, i.e. takes the state and action (two-input model) and determines the corresponding value"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"class ActorCritic:\n",
" def __init__(self, env, sess):\n",
" self.env = env\n",
" self.sess = sess\n",
" \n",
" self.learning_rate = 0.001\n",
" self.epsilon = 1.0\n",
" self.epsilon_decay = .995\n",
" self.gamma = .95\n",
" self.tau = .125\n",
" \n",
" #Actor model - Chain rule\n",
" #Calculate de/dA as = de/dC * dC/dA, where e is error, C critic, A actor\n",
" self.memory = deque(maxlen=2000)\n",
" self.actor_state_input, self.actor_model = self.create_actor_model()\n",
" _, self.target_actor_model = self.create_actor_model()\n",
" \n",
" self.actor_critic_grad = tf.placeholder(tf.float32, [None, self.env.action_space.shape[0]]) #Feed de/dC from Critic\n",
" actor_model_weights = self.actor_model.trainable_weights\n",
" self.actor_grads = tf.gradients(self.actor_model.output, actor_model_weights, -self.actor_critic_grad) #dC/dA from Act\n",
" grads = zip(self.actor_grads, actor_model_weights)\n",
" self.optimize = tf.train.AdamOptimizer(self.learning_rate).apply_gradients(grads)\n",
" \n",
" #Critic model\n",
" \n",
" self.critic_state_input, self.critic_action_input, self.critic_model = self.create_critic_model()\n",
" _, _, self.target_critic_model = self.create_critic_model()\n",
" \n",
" self.critic_grads = tf.gradients(self.critic_model.output, self.critic_action_input) #Where we calculate de/dC\n",
" \n",
" #Initialize for later gradient calculations\n",
" self.sess.run(tf.global_variables_initializer())\n",
" \n",
" \n",
" #MODEL DEFINITIONS\n",
" def create_actor_model(self):\n",
" state_input = Input(shape=self.env.observation_space.shape)\n",
" h1 = Dense(24, activation='relu')(state_input)\n",
" h2 = Dense(48, activation='relu')(h1)\n",
" h3 = Dense(24, activation='relu')(h2)\n",
" output = Dense(self.env.action_space.shape[0], activation='relu')(h3)\n",
" \n",
" model = Model(inputs=state_input, outputs=output)\n",
" adam = Adam(lr=0.001)\n",
" model.compile(loss='mse',optimizer=adam)\n",
" return state_input, model\n",
" \n",
" def create_critic_model(self):\n",
" state_input = Input(shape=self.env.observation_space.shape)\n",
" state_h1 = Dense(24, activation='relu')(state_input)\n",
" state_h2 = Dense(48)(state_h1)\n",
" \n",
" action_input = Input(shape=self.env.action_space.shape)\n",
" action_h1 = Dense(48)(action_input)\n",
" \n",
" merged = Add()([state_h2, action_h1])\n",
" merged_h1 = Dense(24, activation='relu')(merged)\n",
" output = Dense(1, activation='relu')(merged_h1)\n",
" model = Model(inputs=[state_input, action_input], outputs=output)\n",
" \n",
" adam = Adam(lr=0.001)\n",
" model.compile(loss='mse', optimizer=adam)\n",
" return state_input, action_input, model\n",
" \n",
" #MODEL TRAINING\n",
" def remember(self, cur_state, action, reward, new_state, done):\n",
" self.memory.append([cur_state, action, reward, new_state, done])\n",
" \n",
" \n",
" def _train_actor(self, samples):\n",
" for sample in samples:\n",
" cur_state, action, reward, new_state, _ = sample\n",
" predicted_action = self.actor_model.predict(cur_state)\n",
" feed = {self.critic_state_input: cur_state, self.critic_action_input: predicted_action}\n",
" grads = self.sess.run(self.critic_grads, feed_dict=feed)[0]\n",
" \n",
" self.sess.run(self.optimize, feed_dict = {self.actor_state_input: cur_state, self.actor_critic_grad: grads})\n",
" \n",
" \n",
" def _train_critic(self, samples):\n",
" for sample in samples:\n",
" cur_state, action, reward, new_state, done = sample\n",
" \n",
" if not done:\n",
" target_action = self.target_actor_model.predict(new_state)\n",
" future_reward = self.target_critic_model.predict([new_state, target_action])[0][0]\n",
" reward += self.gamma * future_reward\n",
" \n",
" self.critic_model.fit([cur_state, action], reward, verbose=0)\n",
" \n",
" \n",
" def train(self):\n",
" batch_size = 32\n",
" if(len(self.memory) < batch_size):\n",
" return\n",
" \n",
" rewards = []\n",
" samples = random.sample(self.memory, batch_size)\n",
" self._train_critic(samples)\n",
" self._train_actor(samples)\n",
" \n",
" \n",
" #TARGET MODEL UPDATING\n",
" def _update_actor_target(self):\n",
" actor_model_weights = self.actor_model.get_weights()\n",
" actor_target_weights = self.target_critic_model.get_weights()\n",
" \n",
" for i in range(len(actor_target_weights)):\n",
" actor_target_weights[i] = actor_model_weights[i]\n",
" self.target_critic_model.set_weights(actor_target_weights)\n",
" \n",
" \n",
" def _update_critic_target(self):\n",
" critic_model_weights = self.critic_model.get_weights()\n",
" critic_target_weights = self.critic_target_model.get_weights()\n",
" \n",
" for i in range(len(critic_target_weights)):\n",
" critic_target_weights[i] = critic_model_weights[i]\n",
" self.critic_target_model.set_weights(critic_target_weights)\n",
" \n",
" def update_target(self):\n",
" self._update_actor_target()\n",
" self._update_critic_target()\n",
" \n",
" \n",
" #MODEL PREDICTIONS\n",
" \n",
" def act(self, cur_state):\n",
" self.epsilon *= self.epsilon_decay\n",
" if np.random.random() < self.epsilon:\n",
" return self.env.action_space.sample()\n",
" return self.actor_model.predict(cur_state) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train function"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def train(env,num_trials=100,render=False):\n",
" sess = tf.Session()\n",
" K.set_session(sess)\n",
" actor_critic = ActorCritic(env, sess)\n",
" \n",
" #Initialize environment\n",
" cur_state = env.reset()\n",
" action = env.action_space.sample()\n",
" \n",
" frames = []\n",
" \n",
" display_freq = num_trials//10\n",
" i = 0\n",
" while True:\n",
" if(i % display_freq == 0):\n",
" print('At trial %d' % i)\n",
" if render:\n",
" frames.append(env.render(mode = 'rgb_array'))\n",
" \n",
" cur_state = cur_state.reshape((1,env.observation_space.shape[0]))\n",
" action = actor_critic.act(cur_state)\n",
" action = action.reshape((1, env.action_space.shape[0]))\n",
" \n",
" new_state, reward, done, _ = env.step(action)\n",
" new_state = new_state.reshape((1, env.observation_space.shape[0]))\n",
" \n",
" actor_critic.remember(cur_state, action, reward, new_state, done)\n",
" actor_critic.train()\n",
" \n",
" cur_state = new_state\n",
" i+=1\n",
" \n",
" if(i == num_trials):\n",
" break\n",
" \n",
" if render:\n",
" env.render(close=True)\n",
" display_frames_as_gif(frames)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Hyperparams "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"num_trials = 100\n",
"trial_len = 500"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Environment"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[2017-12-16 15:08:01,733] Making new env: Pendulum-v0\n"
]
}
],
"source": [
"env = gym.make(\"Pendulum-v0\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"At trial 0\n",
"At trial 10\n",
"At trial 20\n",
"At trial 30\n",
"At trial 40\n",
"At trial 50\n",
"At trial 60\n",
"At trial 70\n",
"At trial 80\n",
"At trial 90\n"
]
},
{
"data": {
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"[1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Implementation of PPO"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.1 Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/vnd.plotly.v1+html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import tensorflow as tf\n",
"import numpy as np\n",
"import gym\n",
"from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
"import plotly.graph_objs as go\n",
"init_notebook_mode(connected=True)\n",
"\n",
"# Imports specifically so we can render outputs in Jupyter.\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"from JSAnimation.IPython_display import display_animation\n",
"from matplotlib import animation\n",
"from IPython.display import display"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.2 Util functions (display)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def display_frames_as_gif(frames):\n",
" \"\"\"\n",
" Displays a list of frames as a gif, with controls\n",
" \"\"\"\n",
" patch = plt.imshow(frames[0])\n",
" plt.axis('off')\n",
"\n",
" def animate(i):\n",
" patch.set_data(frames[i])\n",
"\n",
" anim = animation.FuncAnimation(plt.gcf(), animate, frames = len(frames), interval=50)\n",
" display(display_animation(anim, default_mode='loop'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.3 Hyperparameters\n",
"\n",
"*EP_MAX* = Number of epochs for training
\n",
"*EP_LEN* = Max length of each episode
\n",
"*GAMMA* = Discount factor
\n",
"*A_LR* = Actor Learning Rate
\n",
"*C_LR* = Critic Learning Rate
\n",
"*BATCH* = Batch size
\n",
"*A_UPDATE_STEPS* = Actor number of update steps
\n",
"*C_UPDATE_STEPS* = Critic number of update steps
\n",
"*S_DIM, A_DIM* = Size of layers
\n",
"*METHOD* = Select the approach. Clipped Surrogate Objective or Adaptive KL Penalty Coefficient
"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"EP_MAX = 500\n",
"EP_LEN = 200\n",
"GAMMA = 0.9\n",
"A_LR = 0.0001\n",
"C_LR = 0.0002\n",
"BATCH = 32\n",
"A_UPDATE_STEPS = 10\n",
"C_UPDATE_STEPS = 10\n",
"S_DIM, A_DIM = 3,1\n",
"METHOD = [\n",
" dict(name='kl_pen', kl_target=0.01, lam=0.5),\n",
" dict(name='clip',epsilon=0.2),\n",
"][1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.4 PPO algorithm implementation"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class PPO(object):\n",
" \n",
" def __init__(self):\n",
" self.sess = tf.Session()\n",
" self.tfs = tf.placeholder(tf.float32,[None, S_DIM], 'state')\n",
" \n",
" #Critic\n",
" with tf.variable_scope('critic'):\n",
" l1 = tf.layers.dense(self.tfs, 100, tf.nn.relu)\n",
" self.v = tf.layers.dense(l1,1)\n",
" self.tfdc_r = tf.placeholder(tf.float32, [None, 1], 'discounted_r')\n",
" self.advantage = self.tfdc_r - self.v\n",
" self.closs = tf.reduce_mean(tf.square(self.advantage))\n",
" self.ctrain_op = tf.train.AdamOptimizer(C_LR).minimize(self.closs)\n",
" \n",
" #Actor\n",
" pi, pi_params = self._build_anet('pi', trainable=True)\n",
" oldpi, oldpi_params = self._build_anet('oldpi', trainable=False)\n",
" with tf.variable_scope('sample_action'):\n",
" self.sample_op = tf.squeeze(pi.sample(1),axis=0) #Choose action\n",
" with tf.variable_scope('update_oldpi'):\n",
" self.update_oldpi_op = [oldp.assign(p) for p, oldp in zip(pi_params, oldpi_params)]\n",
" \n",
" self.tfa = tf.placeholder(tf.float32,[None, A_DIM],'action')\n",
" self.tfadv = tf.placeholder(tf.float32, [None, 1], 'advantage')\n",
" \n",
" with tf.variable_scope('loss'):\n",
" with tf.variable_scope('surrogate'):\n",
" ratio = pi.prob(self.tfa) / oldpi.prob(self.tfa)\n",
" surr = ratio * self.tfadv\n",
" \n",
" if METHOD['name'] == 'kl_pen':\n",
" self.tflam = tf.placeholder(tf.float32, None, 'lambda')\n",
" kl = tf.contrib.distributions.kl_divergence(oldpi, pi)\n",
" self.kl_mean = tf.reduce_mean(kl)\n",
" self.aloss = -(tf.reduce_mean(surr - self.tflam * kl))\n",
" else:\n",
" self.aloss = -tf.reduce_mean(tf.minimum(surr,\n",
" tf.clip_by_value(ratio, 1.-METHOD['epsilon'], 1.+METHOD['epsilon'])*self.tfadv\n",
" )\n",
" )\n",
" with tf.variable_scope('atrain'):\n",
" self.atrain_op = tf.train.AdamOptimizer(A_LR).minimize(self.aloss)\n",
"\n",
" tf.summary.FileWriter(\"log/\", self.sess.graph)\n",
" \n",
" self.sess.run(tf.global_variables_initializer())\n",
" \n",
" \n",
" def update(self, s, a, r):\n",
" self.sess.run(self.update_oldpi_op)\n",
" adv = self.sess.run(self.advantage, {self.tfs: s, self.tfdc_r: r})\n",
" \n",
" #Update actor\n",
" if METHOD['name'] == 'kl_pen':\n",
" for _ in range(A_UPDATE_STEPS):\n",
" _, kl = self.sess.run([self.atrain_op, self.kl_mean],\n",
" {self.tfs: s, self.tfa: a, self.tfadv: adv, self.tflam: METHOD['lam']})\n",
" if kl > 4*METHOD['kl_target']:\n",
" break\n",
" if kl < METHOD['kl_target'] / 1.5: \n",
" METHOD['lam'] /= 2\n",
" elif kl > METHOD['kl_target'] * 1.5:\n",
" METHOD['lam'] *= 2\n",
" METHOD['lam'] = np.clip(METHOD['lam'], 1e-4, 10)\n",
" else:\n",
" [self.sess.run(self.atrain_op, {self.tfs: s, self.tfa: a, self.tfadv: adv}) for _ in range(A_UPDATE_STEPS)]\n",
" \n",
" #Update critic\n",
" [self.sess.run(self.ctrain_op, {self.tfs: s, self.tfdc_r: r}) for _ in range(C_UPDATE_STEPS)]\n",
" \n",
" def _build_anet(self, name, trainable):\n",
" with tf.variable_scope(name):\n",
" l1 = tf.layers.dense(self.tfs, 100, tf.nn.relu, trainable=trainable)\n",
" mu = 2 * tf.layers.dense(l1, A_DIM, tf.nn.tanh, trainable=trainable)\n",
" sigma = tf.layers.dense(l1, A_DIM, tf.nn.softplus, trainable=trainable)\n",
" norm_dist = tf.contrib.distributions.Normal(loc=mu, scale=sigma)\n",
" params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=name)\n",
" return norm_dist, params\n",
" \n",
" def choose_action(self, s):\n",
" s = s[np.newaxis, :]\n",
" a = self.sess.run(self.sample_op, {self.tfs: s})[0]\n",
" return np.clip(a, -2, 2)\n",
" \n",
" def get_v(self, s):\n",
" if s.ndim < 2: s = s[np.newaxis, :]\n",
" return self.sess.run(self.v, {self.tfs: s})[0,0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.5 Train function implementation\n",
"\n",
"Just the function to iterate and perform the training phase.\n",
"\n",
"All episode rewards are stored on *all_ep_r* and returned in order to inspect them (plot). \n",
"\n",
"Also the graph is saved in the *log* folder at the same path this IPYNB is located."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def train(env, ppo, epochs,render=False):\n",
" all_ep_r = []\n",
" frames = []\n",
" display_freq = EP_MAX//10\n",
" \n",
" for ep in range(epochs):\n",
" s = env.reset()\n",
" \n",
" buffer_s, buffer_a, buffer_r = [],[],[]\n",
" ep_r = 0\n",
" for t in range(EP_LEN):\n",
" if (render and (ep == epochs-1)):\n",
" frames.append(env.render(mode = 'rgb_array'))\n",
" a = ppo.choose_action(s)\n",
" s_, r, done, _ = env.step(a)\n",
" buffer_s.append(s)\n",
" buffer_a.append(a)\n",
" buffer_r.append((r+8)/8)\n",
" s = s_\n",
" ep_r += r\n",
" \n",
" #Udpate ppo\n",
" if (t+1) % BATCH == 0 or t == EP_LEN - 1:\n",
" v_s_ = ppo.get_v(s_)\n",
" discounted_r = []\n",
" for r in buffer_r[::-1]:\n",
" v_s_ = r + GAMMA * v_s_\n",
" discounted_r.append(v_s_)\n",
" discounted_r.reverse()\n",
" \n",
" bs, ba, br = np.vstack(buffer_s), np.vstack(buffer_a), np.array(discounted_r)[:, np.newaxis]\n",
" buffer_s, buffer_a, buffer_r = [],[],[]\n",
" ppo.update(bs, ba, br)\n",
" \n",
" if ep == 0: all_ep_r.append(ep_r)\n",
" else: all_ep_r.append(all_ep_r[-1]*0.9 + ep_r*0.1)\n",
" \n",
" if(ep % display_freq == 0):\n",
" print('At epoch %d - Ep. Reward %i' %(ep, ep_r))\n",
" \n",
" if render:\n",
" env.render(close=True)\n",
" display_frames_as_gif(frames)\n",
" \n",
" return all_ep_r"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Train\n",
"\n",
"First of all, create an instance on the **Environment** and the **PPO** class."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[2017-12-29 14:16:06,959] Making new env: Pendulum-v0\n"
]
}
],
"source": [
"env = gym.make('Pendulum-v0').unwrapped\n",
"ppo = PPO()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.1 Render a test episode"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"At epoch 0 - Ep. Reward -1825\n"
]
},
{
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},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.2 perform the training!"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"At epoch 0 - Ep. Reward -1531\n",
"At epoch 50 - Ep. Reward -1252\n",
"At epoch 100 - Ep. Reward -1361\n",
"At epoch 150 - Ep. Reward -624\n",
"At epoch 200 - Ep. Reward -643\n",
"At epoch 250 - Ep. Reward -707\n",
"At epoch 300 - Ep. Reward -983\n",
"At epoch 350 - Ep. Reward -129\n",
"At epoch 400 - Ep. Reward -421\n",
"At epoch 450 - Ep. Reward -264\n"
]
},
{
"data": {
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"
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### YAY! We've made it!\n",
"\n",
"Our little IA now knows how to stabilize a Pendulum!!! :D"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Plot\n",
"\n",
"Let's see how the rewards has increased across epochs"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
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""
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"# Create a trace\n",
"trace = go.Scatter(\n",
" x = np.arange(0,len(all_ep_r)),\n",
" y = all_ep_r,\n",
")\n",
"data = [trace]\n",
"# Plot and embed in ipython notebook!\n",
"iplot(data, filename='basic-line')"
]
},
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"metadata": {},
"source": [
"![](\"src/rewards_epochs.png\")
"
]
},
{
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},
"source": [
"## References\n",
"\n",
"1. [Proximal Policy Optimization Algorithms](https://arxiv.org/pdf/1707.06347.pdf)\n",
"2. [6.4 PPO/DPPO Proximal Policy Optimization (强化学习 Reinforcement Learning with tensorflow 教学) - Morvan](https://www.youtube.com/watch?v=_B2oMdOVVJc&t=348s&ab_channel=%E5%91%A8%E8%8E%AB%E7%83%A6)\n",
"3. [War Robots - Siraj Raval](https://www.youtube.com/watch?v=tm5kQmjfZN8&ab_channel=SirajRaval)"
]
}
],
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================================================
FILE: Week 6/Proximal_Policy_Optimization_Pendulum.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Proximal Policy Optimization for Pendulum"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Proximal Policy Optimization"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Proximal Policy Optimization** is a **new family of policy gradient methods for reinforcement learning**, which alternate between **sampling data through interaction with the environment, and optimizing a \"surrogate\" objective function using stochastic gradient ascent.** \n",
"\n",
"Whereas **standard** policy gradient methods perform **one gradient update per data sample**, here a novel objective function that enables **multiple epochs of minibatch updates** is used. \n",
"\n",
"The new methods are called Proximal Policy Optimization (PPO), and have some of the benefits of trust region policy optimization (TRPO), but are much simpler to implement, more general, and have better sample complexity (empirically).\n",
"\n",
"[1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.1 PPO Algorithm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"[1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Implementation of PPO"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.1 Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/vnd.plotly.v1+html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import tensorflow as tf\n",
"import numpy as np\n",
"import gym\n",
"from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
"import plotly.graph_objs as go\n",
"init_notebook_mode(connected=True)\n",
"\n",
"# Imports specifically so we can render outputs in Jupyter.\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"from JSAnimation.IPython_display import display_animation\n",
"from matplotlib import animation\n",
"from IPython.display import display"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.2 Util functions (display)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def display_frames_as_gif(frames):\n",
" \"\"\"\n",
" Displays a list of frames as a gif, with controls\n",
" \"\"\"\n",
" patch = plt.imshow(frames[0])\n",
" plt.axis('off')\n",
"\n",
" def animate(i):\n",
" patch.set_data(frames[i])\n",
"\n",
" anim = animation.FuncAnimation(plt.gcf(), animate, frames = len(frames), interval=50)\n",
" display(display_animation(anim, default_mode='loop'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.3 Hyperparameters\n",
"\n",
"*EP_MAX* = Number of epochs for training
\n",
"*EP_LEN* = Max length of each episode
\n",
"*GAMMA* = Discount factor
\n",
"*A_LR* = Actor Learning Rate
\n",
"*C_LR* = Critic Learning Rate
\n",
"*BATCH* = Batch size
\n",
"*A_UPDATE_STEPS* = Actor number of update steps
\n",
"*C_UPDATE_STEPS* = Critic number of update steps
\n",
"*S_DIM, A_DIM* = Size of layers
\n",
"*METHOD* = Select the approach. Clipped Surrogate Objective or Adaptive KL Penalty Coefficient
"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"EP_MAX = 500\n",
"EP_LEN = 200\n",
"GAMMA = 0.9\n",
"A_LR = 0.0001\n",
"C_LR = 0.0002\n",
"BATCH = 32\n",
"A_UPDATE_STEPS = 10\n",
"C_UPDATE_STEPS = 10\n",
"S_DIM, A_DIM = 3,1\n",
"METHOD = [\n",
" dict(name='kl_pen', kl_target=0.01, lam=0.5),\n",
" dict(name='clip',epsilon=0.2),\n",
"][1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.4 PPO algorithm implementation"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class PPO(object):\n",
" \n",
" def __init__(self):\n",
" self.sess = tf.Session()\n",
" self.tfs = tf.placeholder(tf.float32,[None, S_DIM], 'state')\n",
" \n",
" #Critic\n",
" with tf.variable_scope('critic'):\n",
" l1 = tf.layers.dense(self.tfs, 100, tf.nn.relu)\n",
" self.v = tf.layers.dense(l1,1)\n",
" self.tfdc_r = tf.placeholder(tf.float32, [None, 1], 'discounted_r')\n",
" self.advantage = self.tfdc_r - self.v\n",
" self.closs = tf.reduce_mean(tf.square(self.advantage))\n",
" self.ctrain_op = tf.train.AdamOptimizer(C_LR).minimize(self.closs)\n",
" \n",
" #Actor\n",
" pi, pi_params = self._build_anet('pi', trainable=True)\n",
" oldpi, oldpi_params = self._build_anet('oldpi', trainable=False)\n",
" with tf.variable_scope('sample_action'):\n",
" self.sample_op = tf.squeeze(pi.sample(1),axis=0) #Choose action\n",
" with tf.variable_scope('update_oldpi'):\n",
" self.update_oldpi_op = [oldp.assign(p) for p, oldp in zip(pi_params, oldpi_params)]\n",
" \n",
" self.tfa = tf.placeholder(tf.float32,[None, A_DIM],'action')\n",
" self.tfadv = tf.placeholder(tf.float32, [None, 1], 'advantage')\n",
" \n",
" with tf.variable_scope('loss'):\n",
" with tf.variable_scope('surrogate'):\n",
" ratio = pi.prob(self.tfa) / oldpi.prob(self.tfa)\n",
" surr = ratio * self.tfadv\n",
" \n",
" if METHOD['name'] == 'kl_pen':\n",
" self.tflam = tf.placeholder(tf.float32, None, 'lambda')\n",
" kl = tf.contrib.distributions.kl_divergence(oldpi, pi)\n",
" self.kl_mean = tf.reduce_mean(kl)\n",
" self.aloss = -(tf.reduce_mean(surr - self.tflam * kl))\n",
" else:\n",
" self.aloss = -tf.reduce_mean(tf.minimum(surr,\n",
" tf.clip_by_value(ratio, 1.-METHOD['epsilon'], 1.+METHOD['epsilon'])*self.tfadv\n",
" )\n",
" )\n",
" with tf.variable_scope('atrain'):\n",
" self.atrain_op = tf.train.AdamOptimizer(A_LR).minimize(self.aloss)\n",
"\n",
" tf.summary.FileWriter(\"log/\", self.sess.graph)\n",
" \n",
" self.sess.run(tf.global_variables_initializer())\n",
" \n",
" \n",
" def update(self, s, a, r):\n",
" self.sess.run(self.update_oldpi_op)\n",
" adv = self.sess.run(self.advantage, {self.tfs: s, self.tfdc_r: r})\n",
" \n",
" #Update actor\n",
" if METHOD['name'] == 'kl_pen':\n",
" for _ in range(A_UPDATE_STEPS):\n",
" _, kl = self.sess.run([self.atrain_op, self.kl_mean],\n",
" {self.tfs: s, self.tfa: a, self.tfadv: adv, self.tflam: METHOD['lam']})\n",
" if kl > 4*METHOD['kl_target']:\n",
" break\n",
" if kl < METHOD['kl_target'] / 1.5: \n",
" METHOD['lam'] /= 2\n",
" elif kl > METHOD['kl_target'] * 1.5:\n",
" METHOD['lam'] *= 2\n",
" METHOD['lam'] = np.clip(METHOD['lam'], 1e-4, 10)\n",
" else:\n",
" [self.sess.run(self.atrain_op, {self.tfs: s, self.tfa: a, self.tfadv: adv}) for _ in range(A_UPDATE_STEPS)]\n",
" \n",
" #Update critic\n",
" [self.sess.run(self.ctrain_op, {self.tfs: s, self.tfdc_r: r}) for _ in range(C_UPDATE_STEPS)]\n",
" \n",
" def _build_anet(self, name, trainable):\n",
" with tf.variable_scope(name):\n",
" l1 = tf.layers.dense(self.tfs, 100, tf.nn.relu, trainable=trainable)\n",
" mu = 2 * tf.layers.dense(l1, A_DIM, tf.nn.tanh, trainable=trainable)\n",
" sigma = tf.layers.dense(l1, A_DIM, tf.nn.softplus, trainable=trainable)\n",
" norm_dist = tf.contrib.distributions.Normal(loc=mu, scale=sigma)\n",
" params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=name)\n",
" return norm_dist, params\n",
" \n",
" def choose_action(self, s):\n",
" s = s[np.newaxis, :]\n",
" a = self.sess.run(self.sample_op, {self.tfs: s})[0]\n",
" return np.clip(a, -2, 2)\n",
" \n",
" def get_v(self, s):\n",
" if s.ndim < 2: s = s[np.newaxis, :]\n",
" return self.sess.run(self.v, {self.tfs: s})[0,0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.5 Train function implementation\n",
"\n",
"Just the function to iterate and perform the training phase.\n",
"\n",
"All episode rewards are stored on *all_ep_r* and returned in order to inspect them (plot). \n",
"\n",
"Also the graph is saved in the *log* folder at the same path this IPYNB is located."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def train(env, ppo, epochs,render=False):\n",
" all_ep_r = []\n",
" frames = []\n",
" display_freq = EP_MAX//10\n",
" \n",
" for ep in range(epochs):\n",
" s = env.reset()\n",
" \n",
" buffer_s, buffer_a, buffer_r = [],[],[]\n",
" ep_r = 0\n",
" for t in range(EP_LEN):\n",
" if (render and (ep == epochs-1)):\n",
" frames.append(env.render(mode = 'rgb_array'))\n",
" a = ppo.choose_action(s)\n",
" s_, r, done, _ = env.step(a)\n",
" buffer_s.append(s)\n",
" buffer_a.append(a)\n",
" buffer_r.append((r+8)/8)\n",
" s = s_\n",
" ep_r += r\n",
" \n",
" #Udpate ppo\n",
" if (t+1) % BATCH == 0 or t == EP_LEN - 1:\n",
" v_s_ = ppo.get_v(s_)\n",
" discounted_r = []\n",
" for r in buffer_r[::-1]:\n",
" v_s_ = r + GAMMA * v_s_\n",
" discounted_r.append(v_s_)\n",
" discounted_r.reverse()\n",
" \n",
" bs, ba, br = np.vstack(buffer_s), np.vstack(buffer_a), np.array(discounted_r)[:, np.newaxis]\n",
" buffer_s, buffer_a, buffer_r = [],[],[]\n",
" ppo.update(bs, ba, br)\n",
" \n",
" if ep == 0: all_ep_r.append(ep_r)\n",
" else: all_ep_r.append(all_ep_r[-1]*0.9 + ep_r*0.1)\n",
" \n",
" if(ep % display_freq == 0):\n",
" print('At epoch %d - Ep. Reward %i' %(ep, ep_r))\n",
" \n",
" if render:\n",
" env.render(close=True)\n",
" display_frames_as_gif(frames)\n",
" \n",
" return all_ep_r"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Train\n",
"\n",
"First of all, create an instance on the **Environment** and the **PPO** class."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[2017-12-29 14:16:06,959] Making new env: Pendulum-v0\n"
]
}
],
"source": [
"env = gym.make('Pendulum-v0').unwrapped\n",
"ppo = PPO()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.1 Render a test episode"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"At epoch 0 - Ep. Reward -1825\n"
]
},
{
"data": {
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2. After training our agent with PPO it masters the pendulum balance!
And this is how the reward changes across epochs
### References
1. [Proximal Policy Optimization Algorithms](https://arxiv.org/pdf/1707.06347.pdf)
2. [6.4 PPO/DPPO Proximal Policy Optimization (强化学习 Reinforcement Learning with tensorflow 教学) - Morvan](https://www.youtube.com/watch?v=_B2oMdOVVJc&t=348s&ab_channel=%E5%91%A8%E8%8E%AB%E7%83%A6)
3. [War Robots - Siraj Raval](https://www.youtube.com/watch?v=tm5kQmjfZN8&ab_channel=SirajRaval)