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我正在尝试为我的 negamax AI（不是国际象棋 - 10-10-5 mnk 或名为 Take 5 的五子棋风格的游戏）实现一个简单的静止搜索。它已经有一个转置表。主要的 negamax 算法如下所示：

def negamax(board, depth, alpha, beta, player, ttable):
    if board in ttable:
        value, vtype = ttable[board]
        if vtype == 0:
            return value
        elif vtype == 1:
            beta = min(alpha, value)
        else:
            alpha = max(beta, value)
    score = board.getValue()
    if abs(score) == 1000000000000:
        return player * score
    if depth == 0:
        return player * score
    oldAlpha = alpha
    possibleMoves = board.getBestNextMoves()
    value = -1000000000000
    for move in possibleMoves:
        #child = board#Board(board)
        board.place(move, player)
        value = max(value, -negamax(board, depth-1, -beta, -alpha,
                                    -player, ttable))
        board.place(move, 0)
        alpha = max(alpha, value)
        if alpha >= beta:
            break # alpha beta prune
    if value <= oldAlpha:
        vtype = 1
    elif value >= beta:
        vtype = -1
    else:
        vtype = 0
    ttable[board] = (value, vtype)
    return value

我在网上寻找了一些静止搜索算法，但它们似乎无法正常工作，但这就是我想出的：

def quiesce(board, alpha, beta, player, ttable):
if board in ttable:
    value, vtype = ttable[board]
    if vtype == 0:
        score = value
    else:
        score = player * board.getValue()
        ttable[board] = (score, 0)
else:
    score = board.getValue()
    ttable[board] = (score, 0)
if score >= beta:
    return score
if alpha < score:
    alpha = score
for move in board.getForcedMoves(player):
    board.place(move, player)
    score = -quiesce(board, -beta, -alpha, -player, ttable)
    board.place(move, 0)
    if score >= beta:
        return score
    if score > alpha:
        alpha = score
return alpha

你能检查我是否正确地实现了这个算法的静止搜索？

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