Python 从minimax到Alpa-beta修剪minimax的变化

我发现了一个3乘3的tic-tac-toe游戏的minimax代码，我想把它改成Alpha-beta minimax，因为我计划创建一个6乘6的tic-tac-toe

以下是源代码：

def minimax（状态、深度、玩家）：
"""
AI功能，选择最好的动作
：param state：电路板的当前状态
：param depth:树中的节点索引（0
def minimax(state, depth, player):
    """
    AI function that choice the best move
    :param state: current state of the board
    :param depth: node index in the tree (0 <= depth <= 9),
    but never nine in this case (see iaturn() function)
    :param player: an human or a computer
    :return: a list with [the best row, best col, best score]
    """
    if player == COMP:
        best = [-1, -1, -infinity]
    else:
        best = [-1, -1, +infinity]

    if depth == 0 or game_over(state):
        score = evaluate(state)
        return [-1, -1, score]

    for cell in empty_cells(state):
        x, y = cell[0], cell[1]
        state[x][y] = player
        score = minimax(state, depth - 1, -player)
        state[x][y] = 0
        score[0], score[1] = x, y

        if player == COMP:
            if score[2] > best[2]:
                best = score  # max value
        else:
            if score[2] < best[2]:
                best = score  # min value

    return best

def minimax(state, depth,alpha,beta, player):
    """
    AI function that choice the best move
    :param state: current state of the board
    :param depth: node index in the tree (0 <= depth <= 9),
    but never nine in this case (see iaturn() function)
    :param player: an human or a computer
    :return: a list with [the best row, best col, best score]
    """
    

     if depth == 0 or game_over(state):
         score = evaluate(state)
         return [-1, -1, score]
if player == COMP:
        best = [-1, -1, -infinity]
    else:
        best = [-1, -1, +infinity]

    

    for cell in empty_cells(state):
        x, y = cell[0], cell[1]
        state[x][y] = player
        score = minimax(state, depth - 1,alpha,beta, -player)
        state[x][y] = 0
        score[0], score[1] = x, y

        if player == COMP:
            if score[2] > best[2]:
                best = score  # max value
                alpha = max(alpha, score[2]) #added alpha
                if beta <= alpha:
                    break
        else:
            if score[2] < best[2]:
                best = score  # min value
                beta= min(beta, score[2]) #added beta
                if beta <= alpha:
                    break

    return best