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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
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================================================
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 <a href="https://gym.openai.com/envs/Taxi-v1/">Taxi-v1</a> environment by OpenAI Gym library.
## Week 2 - Monte Carlo Prediction algorithm
Monte Carlo Prediction algorithm built for the <a href="https://gym.openai.com/envs/Blackjack-v0/">Blackjack-v0</a> environment by OpenAI Gym library.
## Week 3 - Q-Learning algorithm
Q-Learning algorithm built for the <a href="https://gym.openai.com/envs/MountainCarContinuous-v0/">MountainCarContinuous-v0</a> environment by OpenAI Gym library.
## Week 4 - Policy Gradients algorithm
Policy Gradients algorithm built for the <a href="https://gym.openai.com/envs/Pong-v0/">Pong-v0</a> environment by OpenAI Gym library.
## Week 5 - Actor-Critic model
Actor-Critic model built for the <a href="https://gym.openai.com/envs/Pendulum-v0/">Pendulum-v0</a> environment by OpenAI Gym library.
## Week 6 - Proximal Policy Optimization
Proximal Policy Optimization algorithm built for the <a href="https://gym.openai.com/envs/Pendulum-v0/">Pendulum-v0</a> 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 <a href="https://gym.openai.com/envs/Taxi-v1/">Taxi-v1</a> environment by OpenAI Gym library. By <a href="https://github.com/llSourcell/AI_for_Video_Games_Syllabus">Siraj Raval.</a>
The submission file is: <a href="">RL_Value_Iteration_Taxi.ipynb</a>
## Dependencies for challenge
* numpy
* [OpenAI Gym](https://gym.openai.com/docs/)
## Environment
That's the least relevant thing but, as mentioned before, <a href="https://gym.openai.com/envs/Taxi-v1/">"Taxi"</a> 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**
<img src="src/TheTaxiGame.png">
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 <a href=\"https://gym.openai.com/envs/Taxi-v1/\">here</a>."
]
},
{
"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",
"<img src=\"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",
"<img src=\"src/value_function_1.png\" width=\"500px\">\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",
"<img src=\"src/optimal_value_function.png\" width=\"500px\">\n",
"\n",
"The optimal policy 𝛑* is the policy that corresponds to optimal value function.\n",
"\n",
"<img src=\"src/optimal_policy.png\" width=\"500px\">\n",
"\n",
"#### Q function:\n",
"\n",
"Now we introduce another function which is the **state-action pair Q function**.\n",
"\n",
"<img src=\"src/q_func_simple.png\" width=\"200px\">\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",
"<img src=\"src/q_optimal.png\" width=\"500px\">\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",
"<img src=\"src/value_iteration_pseudo.png\" width=\"500px\">"
]
},
{
"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**<br>\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_)<br>\n",
" 2.2 Store the reward from those NEXT STATES according to the formula: **probability * (reward + PreviousValueFunction[NEXT\\_STATE])**<br>\n",
"\n",
"3. **Sum** all those rewards from NEXT STATES<br>\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 <a href=\"https://gym.openai.com/envs/Taxi-v1/\">official documentation</a>, **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",
"<img src=\"src/playing_with_neural_nets.jpg\" width=\"250px\">\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/ <br>\n",
"[2] An overview of MAXQ hierarchical reinforcement learning - https://link.springer.com/book/10.1007/3-540-44914-0#page=38<br>\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:**<br>\n",
"The first-visit MC method estimates _vπ(s)_ as the average of the returns following first visits to _s_\n",
"\n",
"<img src=\"src/first_visit_algorithm.png\" width=\"700px\">"
]
},
{
"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": [
"<matplotlib.figure.Figure at 0x204c0e99e48>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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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
Condensed preview — 16 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (4,497K chars).
[
{
"path": "README.md",
"chars": 1248,
"preview": "# Reinforcement_Learning_AI_Video_Games\nCode for each week's short video of Siraj Raval Course on Reinforcement Learning"
},
{
"path": "Week 1/README.md",
"chars": 1491,
"preview": "## Coding Challenge\n\nThis week's coding challenge is to implement Value iteration algorithm built for the <a href=\"https"
},
{
"path": "Week 1/RL_Value_Iteration_Taxi.ipynb",
"chars": 22055,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 1. Value Iteration algorithm appl"
},
{
"path": "Week 2/Monte_Carlo_Prediction.ipynb",
"chars": 569366,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Monte Carlo Methods\"\n ]\n },\n "
},
{
"path": "Week 2/README.md",
"chars": 869,
"preview": "## Coding Challenge -- (Not submitted)\n\nThis week's coding challenge is to implement Monte Carlo Prediction algorithm. H"
},
{
"path": "Week 3/QLearning_MountainCar.ipynb",
"chars": 1322587,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Q Learning for MountainCar\"\n ]\n"
},
{
"path": "Week 3/README.md",
"chars": 997,
"preview": "## Coding Challenge -- (Not submitted)\n\nThis week's coding challenge is to implement Q-Learning algorithm. Here it is bu"
},
{
"path": "Week 4/Policy_Gradients_Pong.ipynb",
"chars": 10902,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Policy Gradients algorithm for Po"
},
{
"path": "Week 4/README.md",
"chars": 1468,
"preview": "## Coding Challenge -- (Not submitted)\n\nThis week's coding challenge is to implement Policy Gradients algorithm. Here it"
},
{
"path": "Week 5/.ipynb_checkpoints/Actor_Critic_Model_Pendulum-checkpoint.ipynb",
"chars": 276012,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Actor-critic model for Pendulum\"\n"
},
{
"path": "Week 5/Actor_Critic_Model_Pendulum.ipynb",
"chars": 276636,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Actor-critic model for Pendulum\"\n"
},
{
"path": "Week 5/README.md",
"chars": 830,
"preview": "## Code of the Week\n\nThis week's code is an implementation of the Actor-Critic model. Here it is built for the <a href=\""
},
{
"path": "Week 6/.ipynb_checkpoints/Proximal_Policy_Optimization_Pendulum-checkpoint.ipynb",
"chars": 984416,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Proximal Policy Optimization for "
},
{
"path": "Week 6/Proximal_Policy_Optimization_Pendulum.ipynb",
"chars": 984416,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Proximal Policy Optimization for "
},
{
"path": "Week 6/README.md",
"chars": 1415,
"preview": "## Coding Challenge\n\nThis week's coding challenge is to implement Proximal Policy Optimization algorithm. Here it is bui"
}
]
// ... and 1 more files (download for full content)
About this extraction
This page contains the full source code of the alberduris/Reinforcement_Learning_AI_Video_Games GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 16 files (4.2 MB), approximately 1.1M tokens. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.