python - 如何获得minimax算法返回实际 Action ？

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想改进这个问题吗？更新问题，使其只关注一个问题editing this post。
我目前正在尝试对tic-tac-toe实现min i max算法，但是我不确定如何在获得所有游戏状态的min/max之后找到移动的方法。我知道你应该看看哪条路有最多的胜利，但我不知道从这里开始。

def minimax(game_state):
    if game_state.available_moves():
        return evaluate(game_state)
    else:
        return max_play(game_state)

def evaluate(game_state):
    if game_state.has_won(game_state.next_player):
        return 1
    elif game_state.has_won(game_state.opponent()):
        return -1
    else:
        return 0

def min_play(game_state):
    if game_state.available_moves() == []:
        return evaluate(game_state)
    else:
        moves = game_state.available_moves()
        best_score = -1
        for move in moves:
            clone = game_state.make_move(move)
            score = max_play(clone)
            if score < best_score:
                best_move = move
                best_score = score
        return best_score

def max_play(game_state):
    if game_state.available_moves() == []:
        return evaluate(game_state)
    else:
        moves = game_state.available_moves()
        best_score = 1
        for move in moves:
            clone = game_state.make_move(move)
            score = min_play(clone)
            if score > best_score:
                best_move = move
                best_score = score
        return best_score

最佳答案

在顶层非常简单-您只需要记住当前搜索深度的最佳移动，如果您完全计算深度，则将所有最佳移动设置为该深度的最佳；然后尝试使用较深的树重新计算。顺便说一句，赢的最多并不重要，赢就是赢。
本案伪代码：

bestest_move = None
try:
    for depth in range(1, max_depth):
        best_score = float('-inf')
        for move in possible_moves:
            score = evaluate(move)
            if score > best_score:
                best_move = move
                best_score = score

    bestest_move = best_move

except Timeout:
    pass

move(bestest_move)

关于python - 如何获得minimax算法返回实际 Action ？ ，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/28798249/