Q-learning 玩maze遊戲

 

import pygame
import numpy as np
import random
import sys

# 定義迷宮環境
class Maze:
    def __init__(self):
        self.size = 10
        self.maze = np.zeros((self.size, self.size))
        self.start = (0, 0)
        self.goal = (9, 9)
        self.maze[4, 2:7] = 1  # 添加牆壁
        self.maze[2, 1] = 1
        self.current_position = self.start
    
    def reset(self):
        self.current_position = self.start
        return self.current_position
    
    def manhattan_distance(self):
        return abs(self.current_position[0] - self.goal[0]) + abs(self.current_position[1] - self.goal[1])

    def step(self, action):
        x, y = self.current_position
        if action == 0:  # 上
            x -= 1
        elif action == 1:  # 右
            y += 1
        elif action == 2:  # 下
            x += 1
        elif action == 3:  # 左
            y -= 1
        if 0 <= x < self.size and 0 <= y < self.size and self.maze[x, y] == 0:
            self.current_position = (x, y)
            if self.current_position == self.goal:
                reward = 100
                done = True
            else:
                reward = -1
                done = False
        else:
            reward = -100
            done = True
        
        # done = self.current_position == self.goal
        return self.current_position, reward, done

    def render(self, screen):
        for x in range(self.size):
            for y in range(self.size):
                color = (255, 255, 255) if self.maze[x, y] == 0 else (0, 0, 0)
                if (x, y) == self.current_position:
                    color = (0, 255, 0)
                if (x, y) == self.goal:
                    color = (255, 0, 0)
                pygame.draw.rect(screen, color, (y*40, x*40, 40, 40))
        pygame.display.flip()

# Q-learning
class QLearning:
    def __init__(self, env):
        self.env = env
        self.q_table = np.zeros((env.size, env.size, 4))
        self.gamma = 0.9
        self.epsilon = 0.1
        self.alpha = 0.1

    def select_action(self, state):
        if random.random() < self.epsilon:
            return random.randint(0, 3)
        else:
            x, y = state
            return np.argmax(self.q_table[x, y])

    def update(self, state, action, reward, next_state):
        x, y = state
        nx, ny = next_state
        future_rewards = np.max(self.q_table[nx, ny])
        self.q_table[x, y, action] += self.alpha * (reward + self.gamma * future_rewards - self.q_table[x, y, action])

# 主程序
def main():
    pygame.init()
    screen = pygame.display.set_mode((400, 400))
    clock = pygame.time.Clock()
    maze = Maze()
    agent = QLearning(maze)

    for episode in range(10000):
        state = maze.reset()
        done = False
        while not done:
            action = agent.select_action(state)
            next_state, reward, done = maze.step(action)
            agent.update(state, action, reward, next_state)
            state = next_state

            for event in pygame.event.get():
                if event.type == pygame.QUIT:
                    pygame.quit()
                    sys.exit()

            if episode >= 8000:
                screen.fill((0, 0, 0))
                maze.render(screen)
                clock.tick(10)

if __name__ == '__main__':
    main()

運行效果：