Question

我目前正在尝试为tic tac toe实现minimax算法，但我不确定如何在获得所有game_states的最小值/最大值后找出如何进行移动。我知道你应该看看哪条路的获胜次数最多，但我不知道从哪里开始。

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

Answer 1

顶层真的很简单 - 所有你需要记住的是当前搜索深度的最佳移动，如果你完全评估深度，那么将最好的设置为最佳深度;并尝试用更深的树再次评估。顺便说一句，最大数量的胜利并不重要，胜利就是胜利。

案例的伪代码：

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)

如何让minimax算法返回实际移动？

1 个答案: