Python:Connect 4的Alpha-Beta-Minimax

我正在尝试将Python中的minimax用于Connect 4作为个人项目来实现AI。目前我有这个

def alphaBeta(myBoard, column, depth, alpha, beta, player):
    parent = board()
    for r in range(ROWS):
        for c in range(COLUMNS):
            parent.board[r][c] = myBoard.board[r][c]
    parent.move(column, player)
    if parent.isFull() or parent.isWon()[0] or depth <= 0:
        if parent.isFull(): return 0
        if parent.isWon()[1] == player: return float('inf')
        elif parent.isWon()[0]: return -1*float('inf')
        else: return statScore(parent, player)
    if player == "O":
        for child in range(COLUMNS):
            alpha = max(alpha, alphaBeta(parent, child, depth-1, alpha, beta, "X"))
            if beta <= alpha:
                break
        return alpha
    else:
        for child in range(COLUMNS):
            beta = min(beta, alphaBeta(parent, child, depth-1, alpha, beta, "O"))
            if beta <= alpha:
                break
        return beta

def ai(myBoard, depth):
    output = []
    bestScore = float('inf')
    for column in range(COLUMNS):
        if myBoard.isValid(column):
            score = alphaBeta(myBoard, column, depth, -1*float('inf'), float('inf'), "O")
            if score < bestScore:
                output = [column]
                bestScore = score
            elif score == bestScore:
                output.append(column)
    move = random.choice(output)
    return move

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关于这个问题，你需要更具体一些。尝试将棋盘设置为即将获胜的棋盘，向我们显示配置和您期望发生的情况，并告诉我们实际发生的情况。添加了一个问题示例。myBoard.isWon（）返回一个形式的元组（布尔值，如果有人赢了或没有赢，哪个玩家赢了）。

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| | | | | | | |
| | | | | | | |
| | | | | | | |
| | | | |O| | |
| | |X|O|O| | |
|X|X|O|X|O|X|X|
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