强化学习训练井字棋的代码

以下是一个简单的强化学习训练井字棋的Python代码示例：

import numpy as np

# 初始化游戏棋盘
board = np.zeros((3, 3))

# 定义玩家和AI的棋子类型
player_piece = 1
ai_piece = -1

# 定义游戏结束的条件
def game_over(board):
    # 判断是否有一方连成三个棋子
    for i in range(3):
        if sum(board[i, :]) == 3 or sum(board[:, i]) == 3:
            return True
    if board[0, 0] + board[1, 1] + board[2, 2] == 3 or board[0, 2] + board[1, 1] + board[2, 0] == 3:
        return True
    # 判断是否已经下满了所有的格子
    if np.all((board == 0) == False):
        return True
    return False

# 定义玩家的落子函数
def player_move(board):
    while True:
        row = int(input("请输入要下的行数（1~3）：")) - 1
        col = int(input("请输入要下的列数（1~3）：")) - 1
        if row >= 0 and row < 3 and col >= 0 and col < 3 and board[row, col] == 0:
            board[row, col] = player_piece
            break

# 定义AI的落子函数
def ai_move(board):
    best_score = -np.inf
    best_move = None
    for i in range(3):
        for j in range(3):
            if board[i, j] == 0:
                board[i, j] = ai_piece
                score = minimax(board, 0, False)
                board[i, j] = 0
                if score > best_score:
                    best_score = score
                    best_move = (i, j)
    board[best_move[0], best_move[1]] = ai_piece

# 定义minimax算法
def minimax(board, depth, is_maximizing):
    if game_over(board):
        if is_maximizing:
            return -1
        else:
            return 1
    if is_maximizing:
        best_score = -np.inf
        for i in range(3):
            for j in range(3):
                if board[i, j] == 0:
                    board[i, j] = ai_piece
                    score = minimax(board, depth + 1, False)
                    board[i, j] = 0
                    best_score = max(best_score, score)
        return best_score
    else:
        best_score = np.inf
        for i in range(3):
            for j in range(3):
                if board[i, j] == 0:
                    board[i, j] = player_piece
                    score = minimax(board, depth + 1, True)
                    board[i, j] = 0
                    best_score = min(best_score, score)
        return best_score

# 开始游戏
while not game_over(board):
    player_move(board)
    if game_over(board):
        break
    ai_move(board)
    print(board)

# 判断胜负
if sum(board[0, :]) == 3 or sum(board[1, :]) == 3 or sum(board[2, :]) == 3:
    print("你赢了！")
elif sum(board[:, 0]) == 3 or sum(board[:, 1]) == 3 or sum(board[:, 2]) == 3:
    print("你赢了！")
elif board[0, 0] + board[1, 1] + board[2, 2] == 3 or board[0, 2] + board[1, 1] + board[2, 0] == 3:
    print("你赢了！")
elif sum(board[0, :]) == -3 or sum(board[1, :]) == -3 or sum(board[2, :]) == -3:
    print("你输了！")
elif sum(board[:, 0]) == -3 or sum(board[:, 1]) == -3 or sum(board[:, 2]) == -3:
    print("你输了！")
elif board[0, 0] + board[1, 1] + board[2, 2] == -3 or board[0, 2] + board[1, 1] + board[2, 0] == -3:
    print("你输了！")
else:
    print("平局！")

这个代码使用了minimax算法来实现AI的决策。minimax算法是一种递归算法，会考虑所有可能的落子情况，并计算每种情况下的得分，然后选择得分最优的落子。由于井字棋的状态空间相对较小，因此使用minimax算法是可行的。在实际的应用中，可能需要使用更复杂的算法来处理更大的状态空间。