六子棋建立博弈树α-β剪枝算法代码

以下是一个六子棋博弈树的α-β剪枝算法的Python代码：

import numpy as np

# 博弈树节点
class Node:
    def __init__(self, state, depth, alpha=-np.inf, beta=np.inf):
        self.state = state
        self.depth = depth
        self.alpha = alpha
        self.beta = beta
        self.children = []

    def is_terminal(self):
        return self.depth == 0 or self.state.is_game_over()

    def evaluate(self):
        # 计算当前节点的估价值
        return self.state.evaluate()

    def generate_children(self):
        # 生成当前节点的所有子节点
        for move in self.state.get_legal_moves():
            new_state = self.state.apply_move(move)
            child = Node(new_state, self.depth - 1)
            self.children.append(child)

    def get_best_child(self):
        # 获取当前节点的最佳子节点
        best_child = None
        if self.children:
            best_child = self.children[0]
            for child in self.children:
                if child.alpha >= best_child.alpha:
                    best_child = child
        return best_child

# MiniMax算法
def minimax(node, maximizing_player):
    if node.is_terminal():
        return node.evaluate()

    if maximizing_player:
        value = -np.inf
        for child in node.children:
            value = max(value, minimax(child, False))
            node.alpha = max(node.alpha, value)
            if node.beta <= node.alpha:
                break
        return value
    else:
        value = np.inf
        for child in node.children:
            value = min(value, minimax(child, True))
            node.beta = min(node.beta, value)
            if node.beta <= node.alpha:
                break
        return value

# α-β剪枝算法
def alphabeta(node, depth, alpha, beta, maximizing_player):
    if depth == 0 or node.is_terminal():
        return node.evaluate()

    if maximizing_player:
        value = -np.inf
        for child in node.children:
            value = max(value, alphabeta(child, depth - 1, alpha, beta, False))
            alpha = max(alpha, value)
            if beta <= alpha:
                break
        return value
    else:
        value = np.inf
        for child in node.children:
            value = min(value, alphabeta(child, depth - 1, alpha, beta, True))
            beta = min(beta, value)
            if beta <= alpha:
                break
        return value

# 六子棋游戏状态
class GameState:
    def __init__(self):
        self.board = np.zeros((6, 6), dtype=int)
        self.current_player = 1

    def is_game_over(self):
        # 判断游戏是否结束
        if self.get_winner() is not None:
            return True
        return False

    def get_winner(self):
        # 判断游戏胜负
        for i in range(6):
            if np.sum(self.board[i]) == 6:
                return 1
            elif np.sum(self.board[i]) == -6:
                return -1

        for i in range(6):
            if np.sum(self.board[:, i]) == 6:
                return 1
            elif np.sum(self.board[:, i]) == -6:
                return -1

        if self.board.trace() == 6 or np.fliplr(self.board).trace() == 6:
            return 1
        elif self.board.trace() == -6 or np.fliplr(self.board).trace() == -6:
            return -1

        return None

    def evaluate(self):
        # 计算当前游戏状态的估价值
        winner = self.get_winner()
        if winner is not None:
            return winner * np.inf
        else:
            return np.sum(self.board)

    def get_legal_moves(self):
        # 获取当前玩家的合法落子位置
        moves = []
        for i in range(6):
            for j in range(6):
                if self.board[i][j] == 0:
                    moves.append((i, j))
        return moves

    def apply_move(self, move):
        # 更新游戏状态
        new_state = GameState()
        new_state.board = np.copy(self.board)
        new_state.current_player = -self.current_player

        i, j = move
        new_state.board[i][j] = self.current_player

        return new_state

# 初始化博弈树
def initialize_tree(state, depth):
    root = Node(state, depth)
    root.generate_children()
    return root

# 选择最佳落子位置
def select_move(state, depth):
    root = initialize_tree(state, depth)
    for child in root.children:
        minimax(child, False)
        #alphabeta(child, depth - 1, -np.inf, np.inf, False)  # 使用α-β剪枝算法
        root.alpha = max(root.alpha, child.alpha)

    best_child = root.get_best_child()
    return best_child.state.get_last_move()

# 测试
state = GameState()
while not state.is_game_over():
    if state.current_player == 1:
        move = select_move(state, 3)
        state = state.apply_move(move)
    else:
        move = tuple(map(int, input("请输入黑方落子位置：").split()))
        state = state.apply_move(move)
    print(state.board)
print("游戏结束，胜利者为", state.get_winner())

在代码中，`Node`类表示一个博弈树节点，`GameState`类表示一个六子棋游戏状态。`minimax`函数是标准的MiniMax算法，`alphabeta`函数是α-β剪枝算法。`initialize_tree`函数用于初始化博弈树，`select_move`函数用于选择最佳落子位置。在测试部分，程序会先使用AI自动落子，然后等待人类玩家输入落子位置，直到游戏结束。