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嘿，我正在尝试使用 alpha-beta 和 4 名玩家的静默搜索来实现 negamax，但它有点不同。例如，假设0 = Red、1 = Blue、2 = Yellow、 和3 = Green以及 Red 和 Green 在同一个团队中，这意味着 2 个回合将是最大化者，2 个回合将是最小化者。这是我基于 wiki 的实现。

def search(self, position, alpha, beta, depth, root=True):
    self.nodes += 1
    best_move = []
    if position in self.history and not root: return 0 # prevent a three-fold repetition moves when the engine is winning.
    bestscore = -999999
    if depth == 0 or position.is_final: return self.quiesce(position, alpha, beta)
    for move in position.moves():
        if position.turn in (0, 2): score = -self.search(position.move(move), -beta, -alpha, depth - 1, False)
        else: score = self.search(position.move(move), alpha, beta, depth - 1, False)
        if score >= beta: return score # fail-soft beta-cutoff
        if score > bestscore:
            bestscore = score
            if root: best_move = move
            if score > alpha: alpha = score
    if not root: return bestscore
    else: return best_move

def quiesce(self, position, alpha, beta):
    if position.is_final: return position.score + position.value() # this position is final so just return the evaluation.
    stand_pat = position.score + position.value()
    if stand_pat >= beta: return beta
    if alpha < stand_pat: alpha = stand_pat
    for move in position.moves():
        if move[2] != 1: continue # continue through the moves until we encounter another capture.
        if position.turn in (0, 2): score = -self.quiesce(position.move(move), -beta, -alpha)
        else: score = self.quiesce(position.move(move), alpha, beta)
        if score >= beta: return beta
        if score > alpha: alpha = score
    return alpha

这似乎无法正常工作，我认为如果下一个要移动的玩家在我的团队中，我们不应该切换 alpha 和 beta 并且不要反转值。关于我做错的任何事情的任何建议。

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