五子棋python极大极小算法实现代码

好的，下面是一个简单的五子棋Python极大极小算法实现的代码示例：

import numpy as np

# 定义棋盘大小
ROW, COL = 15, 15

# 定义棋子类型
EMPTY = 0
BLACK = 1
WHITE = 2

# 定义评估函数中的权重
SCORE_TABLE = [
    [50, 500, 5000, 50000, 50000],
    [10, 50, 500, 5000, 50000],
    [5, 10, 50, 500, 5000],
    [1, 5, 10, 50, 500],
    [0, 1, 5, 10, 50]
]

# 创建棋盘
board = np.zeros((ROW, COL), dtype=int)

def is_valid_move(row, col):
    # 判断落子是否在合法范围内
    return row >= 0 and row < ROW and col >= 0 and col < COL and board[row][col] == EMPTY

def is_game_over(row, col):
    # 判断游戏是否结束
    return is_win(row, col, BLACK) or is_win(row, col, WHITE) or np.sum(board == EMPTY) == 0

def is_win(row, col, chess):
    # 判断是否获胜
    for i in range(5):
        if (col + i < COL and board[row][col + i] == chess and 
            col + i - 4 >= 0 and np.all(board[row][col + i - 4:col + i + 1] == chess)):
            return True
        if (row + i < ROW and board[row + i][col] == chess and 
            row + i - 4 >= 0 and np.all(board[row + i - 4:row + i + 1, col] == chess)):
            return True
        if (row + i < ROW and col + i < COL and board[row + i][col + i] == chess and 
            row + i - 4 >= 0 and col + i - 4 >= 0 and np.all(np.diag(board[row + i - 4:row + i + 1, col + i - 4:col + i + 1]) == chess)):
            return True
        if (row - i >= 0 and col + i < COL and board[row - i][col + i] == chess and 
            row - i + 4 < ROW and col + i - 4 >= 0 and np.all(np.diag(board[row - i:row - i + 5, col + i - 4:col + i + 1]) == chess)):
            return True
    return False

def evaluate():
    # 评估当前局势得分
    score = 0
    for row in range(ROW):
        for col in range(COL):
            if board[row][col] == BLACK:
                score += SCORE_TABLE[row][col]
            elif board[row][col] == WHITE:
                score -= SCORE_TABLE[row][col]
    return score

def minimax(depth, alpha, beta, is_maximizing_player):
    if depth == 0 or is_game_over(last_move[0], last_move[1]):
        return evaluate()

    if is_maximizing_player:
        max_eval = float('-inf')
        for row in range(ROW):
            for col in range(COL):
                if is_valid_move(row, col):
                    board[row][col] = BLACK
                    eval = minimax(depth - 1, alpha, beta, False)
                    board[row][col] = EMPTY
                    max_eval = max(max_eval, eval)
                    alpha = max(alpha, eval)
                    if beta <= alpha:
                        break
        return max_eval
    else:
        min_eval = float('inf')
        for row in range(ROW):
            for col in range(COL):
                if is_valid_move(row, col):
                    board[row][col] = WHITE
                    eval = minimax(depth - 1, alpha, beta, True)
                    board[row][col] = EMPTY
                    min_eval = min(min_eval, eval)
                    beta = min(beta, eval)
                    if beta <= alpha:
                        break
        return min_eval

def get_best_move():
    best_eval = float('-inf')
    best_move = None
    for row in range(ROW):
        for col in range(COL):
            if is_valid_move(row, col):
                board[row][col] = BLACK
                eval = minimax(3, float('-inf'), float('inf'), False)
                board[row][col] = EMPTY
                if eval > best_eval:
                    best_eval = eval
                    best_move = (row, col)
    return best_move

# 测试代码
last_move = (7, 7)
board[6][7] = WHITE
board[6][6] = BLACK
board[8][7] = WHITE
board[8][6] = BLACK
board[7][6] = WHITE
board[7][5] = BLACK

best_move = get_best_move()
print(f"Best move: {best_move}")

这段代码实现了一个简单的五子棋AI，使用了极大极小算法进行决策。你可以根据需要调整评估函数、搜索深度以及其他相关参数来优化算法的性能和效果。希望能帮到你！