gistlib - reinforcement learning for tic tac toe in python

To implement reinforcement learning for Tic Tac Toe in Python, you can follow these steps:

Define the game environment:
- Create a 3x3 grid to represent the Tic Tac Toe board.
- Implement functions to initialize the board, make moves, check for game over conditions (win/loss/draw) and get a list of available moves.
Design the AI agent:
- Choose a reinforcement learning algorithm such as Q-learning.
- Create a Q-table to store the action-value estimates for each state-action pair.
- Define the exploration-exploitation trade-off, such as using an epsilon-greedy policy.
Implement the training loop:
- Initialize the Q-table with small random values.
- For each episode:
  - Reset the game environment.
  - Choose an action based on the current state using the epsilon-greedy policy.
  - Make the chosen move and observe the next state and reward.
  - Update the Q-table using the Q-learning update rule.
  - Repeat until the game is over.
- Decay the exploration rate over time to decrease exploration and increase exploitation.
Evaluate the trained agent:
- Play against the trained agent or simulate multiple games to evaluate its performance.
- You can track the win rate, lose rate, and draw rate to assess the agent's progress.

Here's a high-level code snippet to get you started:


main.py
import numpy as np

# Step 1: Define the game environment

def initialize_board():
    return np.zeros((3, 3), dtype=int)

def make_move(board, row, col, player):
    if board[row][col] == 0:
        board[row][col] = player
        return True
    return False

# Step 2: Design the AI agent

class QLearningAgent:
    def __init__(self, alpha, gamma, epsilon):
        self.alpha = alpha
        self.gamma = gamma
        self.epsilon = epsilon
        self.q_table = np.zeros((3, 3, 9))  # Q-table for each possible state-action pair
    
    def choose_action(self, state):
        if np.random.uniform() < self.epsilon:
            return np.random.choice(9)  # Explore (choose random action)
        else:
            q_values = self.q_table[state[0], state[1]]
            return np.argmax(q_values)  # Exploit (choose action with highest Q-value)
    
    def update_q_table(self, state, action, reward, next_state):
        q_value = self.q_table[state[0], state[1], action]
        max_q_value = np.max(self.q_table[next_state[0], next_state[1]])
        new_q_value = (1 - self.alpha) * q_value + self.alpha * (reward + self.gamma * max_q_value)
        self.q_table[state[0], state[1], action] = new_q_value

# Step 3: Implement the training loop

def play_game(agent):
    board = initialize_board()
    current_player = 1
    game_over = False
    
    while not game_over:
        action = agent.choose_action(board)
        row = action // 3
        col = action % 3
        if make_move(board, row, col, current_player):
            if check_game_over(board):
                game_over = True
                reward = get_reward(board)
            else:
                current_player = 3 - current_player
                reward = 0
            next_state = board.copy()
            agent.update_q_table(board, action, reward, next_state)
            board = next_state

# Step 4: Evaluate the trained agent

def evaluate_agent(agent, num_games):
    wins = 0
    losses = 0
    draws = 0
    
    for _ in range(num_games):
        # Play against random opponent or another agent
        # Keep track of wins, losses, and draws
    
    win_rate = wins / num_games
    loss_rate = losses / num_games
    draw_rate = draws / num_games
    
    return win_rate, loss_rate, draw_rate

# Main program
if __name__ == "__main__":
    agent = QLearningAgent(alpha=0.1, gamma=0.9, epsilon=0.8)
    
    for _ in range(num_episodes):
        play_game(agent)
    
    win_rate, loss_rate, draw_rate = evaluate_agent(agent, num_games)
    print(f"Win rate: {win_rate:.2f}, Loss rate: {loss_rate:.2f}, Draw rate: {draw_rate:.2f}")
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Note that this is just a basic implementation to get you started. You may need to modify and expand the code depending on your specific requirements and desired improvements.

reinforcement learning for tic tac toe in python

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