强化学习俄罗斯方块用Python编写代码

下面是一个用Python实现的简单强化学习俄罗斯方块的代码，使用的是Q-learning算法：

import numpy as np
import random
import time
import pygame

pygame.init()

# 游戏参数
block_size = 30  # 方块大小
screen_width = 10 * block_size
screen_height = 20 * block_size
board_width = 10
board_height = 20
fps = 60

# 颜色定义
black = (0, 0, 0)
white = (255, 255, 255)
red = (255, 0, 0)
green = (0, 255, 0)
blue = (0, 0, 255)
yellow = (255, 255, 0)
purple = (255, 0, 255)
cyan = (0, 255, 255)

# 方块定义
shapes = [
    np.array([[1, 1], [1, 1]]),
    np.array([[0, 2, 0], [2, 2, 2]]),
    np.array([[0, 3, 3], [3, 3, 0]]),
    np.array([[4, 4, 0], [0, 4, 4]]),
    np.array([[5, 5, 5, 5]]),
    np.array([[0, 0, 6], [6, 6, 6]]),
    np.array([[7, 7, 0], [0, 7, 7]])
]
colors = [white, cyan, yellow, purple, green, red, blue]

# Q-learning参数
num_episodes = 5000
max_steps_per_episode = 200
learning_rate = 0.8
discount_rate = 0.95
exploration_rate = 1.0
max_exploration_rate = 1.0
min_exploration_rate = 0.01
exploration_decay_rate = 0.001

# Q-table初始化
q_table = np.zeros((board_width, board_height, 4))

# 游戏窗口
screen = pygame.display.set_mode((screen_width, screen_height))
pygame.display.set_caption("Tetris")

# 方块类
class Block:
    def __init__(self, shape):
        self.shape = shape
        self.color = colors[shape[0, 0]]
        self.x = board_width // 2 - shape.shape[1] // 2
        self.y = 0
    
    def move(self, dx, dy):
        self.x += dx
        self.y += dy
    
    def rotate(self):
        self.shape = np.rot90(self.shape, k=-1)
    
    def get_pos(self):
        return [(i + self.x, j + self.y) for i, j in np.argwhere(self.shape)]
    
    def draw(self):
        for i, j in self.get_pos():
            pygame.draw.rect(screen, self.color, (i * block_size, j * block_size, block_size, block_size))

# 游戏类
class Tetris:
    def __init__(self):
        self.board = np.zeros((board_width, board_height), dtype=int)
        self.block = Block(random.choice(shapes))
        self.score = 0
        self.lines_cleared = 0
    
    def get_reward(self, lines_cleared):
        if lines_cleared == 0:
            return -10
        elif lines_cleared == 1:
            return 100
        elif lines_cleared == 2:
            return 300
        elif lines_cleared == 3:
            return 500
        elif lines_cleared == 4:
            return 800
    
    def update(self, action):
        dx, dy, da = action
        self.block.move(dx, dy)
        if da == 1:
            self.block.rotate()
        pos = self.block.get_pos()
        if any(i < 0 or i >= board_width or j >= board_height or self.board[i, j] != 0 for i, j in pos):
            return False
        while all(j < board_height and self.board[:, j].sum() != board_width for i, j in pos):
            self.block.move(0, 1)
        for i, j in pos:
            self.board[i, j] = self.block.shape[0, 0]
        lines_cleared = 0
        for j in range(board_height):
            if self.board[:, j].sum() == board_width:
                lines_cleared += 1
                self.board = np.delete(self.board, j, axis=1)
                self.board = np.insert(self.board, 0, 0, axis=1)
        reward = self.get_reward(lines_cleared)
        self.score += reward
        self.lines_cleared += lines_cleared
        self.block = Block(random.choice(shapes))
        return True
    
    def draw(self):
        screen.fill(black)
        for i in range(board_width):
            for j in range(board_height):
                if self.board[i, j] != 0:
                    pygame.draw.rect(screen, colors[self.board[i, j]], (i * block_size, j * block_size, block_size, block_size))
        self.block.draw()
        font = pygame.font.SysFont(None, 30)
        text = font.render("Score: " + str(self.score), True, white)
        screen.blit(text, (screen_width - 150, 50))
        text = font.render("Lines: " + str(self.lines_cleared), True, white)
        screen.blit(text, (screen_width - 150, 100))
        pygame.display.flip()
    
    def get_state(self):
        state = np.array(self.board)
        for i, j in self.block.get_pos():
            state[i, j] = self.block.shape[0, 0]
        return state
    
    def reset(self):
        self.board = np.zeros((board_width, board_height), dtype=int)
        self.block = Block(random.choice(shapes))
        self.score = 0
        self.lines_cleared = 0

# Q-learning
for episode in range(num_episodes):
    tetris = Tetris()
    state = tetris.get_state()
    done = False
    t = 0
    while not done and t < max_steps_per_episode:
        # 探索率
        exploration_rate = min_exploration_rate + (max_exploration_rate - min_exploration_rate) * np.exp(-exploration_decay_rate * episode)
        # 动作选择
        if np.random.rand() < exploration_rate:
            action = np.random.randint(0, 4)
        else:
            action = np.argmax(q_table[tuple(state.flatten())])
        # 执行动作
        dx, dy, da = [(0, -1), (0, 1), (1, 0), (0, 0)][action]
        new_state = tetris.get_state()
        reward = tetris.get_reward(tetris.lines_cleared)
        done = not tetris.update((dx, dy, da))
        # 更新Q表
        q_table[tuple(state.flatten() + (action,))] = (1 - learning_rate) * q_table[tuple(state.flatten() + (action,))] + learning_rate * (reward + discount_rate * np.max(q_table[tuple(new_state.flatten())]))
        state = new_state
        t += 1
    print("Episode {}: score = {}".format(episode, tetris.score))

# 游戏循环
tetris = Tetris()
done = False
while not done:
    for event in pygame.event.get():
        if event.type == pygame.QUIT:
            done = True
    state = tetris.get_state()
    action = np.argmax(q_table[tuple(state.flatten())])
    dx, dy, da = [(0, -1), (0, 1), (1, 0), (0, 0)][action]
    done = not tetris.update((dx, dy, da))
    tetris.draw()
    time.sleep(1 / fps)
    
pygame.quit()

这个代码使用了Pygame库来绘制游戏界面，并使用了Q-learning算法来学习控制方块移动。在游戏结束后，代码会输出训练过程中每个episode的得分，然后进入测试模式，自动运行学习到的策略来玩游戏。