6*6 Tictaoe上的极小极大Alpha-beta修剪

Question

我试图在我的班上建立一个Alpha-beta剪枝算法，但我仍在努力。 我希望函数的返回是x的位置、y的位置和分数的列表。我从this github得到的原始代码是minimax，没有Alpha-beta修剪

以下是没有Alpha-beta修剪代码的原始minimax：

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

以下是我修改并设法接近我想要的内容：

from math import inf as infinity
from random import choice
import platform
    
def minimax(state, depth, alpha, beta, player):
    """ 
    state: current state of the board
    player: integer, 1 is COMp while -1 is HUMAN
    return a list of [x position, y position, score]
    """
    

    if depth == 0 or game_over(state):
        score = evaluate(state)  # evaluate is a function to see the state of the current board; 
                                   computer win : +1, human win : -1, draw : 0
        return [-1, -1, score]
    
    if player==COMP: 
       
        best = [-1, -1, -infinity]
  
        # empty_cells(state) find the current available positions on the board
        for cell in empty_cells(state): 
            x, y = cell[0], cell[1]
            state[x][y] = player  
            val = minimax(state, depth - 1, alpha, beta, -player)              
            best = max(best[2], val[2])
            alpha = max(alpha, val[2])
  
            # Alpha Beta Pruning 
            if beta <= alpha: 
                break 
           
        return best 
       
    else:
        best = [-1, -1, +infinity]
  
        # empty_cells(state) find the current available positions on the board
        for cell in empty_cells(state): 
            x, y = cell[0], cell[1]
            state[x][y] = player
           
            val = minimax(state, depth - 1, alpha, beta, player)
                             
            best = min(best[2], val[2]) 
            beta = min(beta, val[2]) 
  
            # Alpha Beta Pruning 
            if beta <= alpha: 
                break  
        return best

我尝试将Alpha-beta剪枝添加到原始代码中，但是它的模式与我在互联网上发现的许多minimax不同，因此我尝试使用我熟悉的模式编写新代码。建议对这两个代码中的任何一个进行Alpha-beta剪枝都非常有用