终极井字棋Alpha-Beta搜索算法优化求助：搜索深度受限性能差

我正在完成学校项目，为终极井字棋（由3x3个普通井字棋组成的棋盘，每一步落子会指定对手下一步的落子棋盘）编写Alpha-Beta搜索算法。当前算法因分支因子过大，为避免性能问题不得不限制搜索深度——搜索深度超过4时基本无法使用，这导致AI效果很差：仅能战胜随机落子的Bot，对阵其他AI Bot时几乎全败。我正在实现启发式函数对落子进行排序，但不确定能否解决问题。

以下是代码核心片段：

# choose a move to play - this is called everytime its the bots move
def play(max_rec_depth=4):

    n = execute_alp_bta(max_rec_depth)
    
    place(curr, n, 1)
    return n
    
    
# Call to execute
def execute_alp_bta(max_rec_depth) -> int:
    global curr
    
    max_pos = -1 
    max_val = float('-inf') 
    
    moves = get_heuristic_ordered_moves(curr)
    # Iterate over possible moves on the current board
    for i in range(1, 10):
        if boards[curr][i] == 0:
            boards[curr][i] = 1  # Simulate place Maximiser (us)
            
            # Recurse starting from opponents view
            score = minimax(max_rec_depth, float('-inf'), float('inf'), is_maximising_player=False, curr_val=i)
            boards[curr][i] = 0  # Undo the simulated move
            
            # Update the best score and position if the current score is better
            if score > max_val:
                max_val = score
                max_pos = i

    # Return the position that gives the maximum value as per minimax
    if max_pos == -1:
        raise ValueError("Error with Minimax recursion")
    
    # print(f"found max val of {max_val} for pos {max_pos}")
    return max_pos


# MAXIMISING PLAYER - should be the US the computer
def minimax(depth, alpha, beta, is_maximising_player, curr_val) -> int:
    eval = evaluate() # returns win loss draw or none
    if depth == 0 or abs(eval) == 1:  # Terminal condition
        return eval
    
    if is_maximising_player:
        max_val = float('-inf')
        for i in range(1, 10):
            if boards[curr_val][i] == 0:
                boards[curr_val][i] = 1 # Maximisers move
                # print(f"placed at board: {curr_val} with index {i}")
                score = minimax(depth-1, alpha, beta, False, curr_val=i)
                # print(f"exited back out of recursion. Undid board: {curr_val} with index {i}")
                boards[curr_val][i] = 0 # Undo move
                max_val = max(max_val, score)
                alpha = max(alpha, score)
                if beta <= alpha:
                    break
            
        return max_val
    else: 
        min_val = float('inf')
        for i in range(1, 10):
            if boards[curr_val][i] == 0:
                boards[curr_val][i] = -1 # minimizers move
                # print(f"placed at board: {curr_val} with index {i}")
                score = minimax(depth-1, alpha, beta, True, curr_val=i)
                # print(f"exited back out of recursion. Undid board: {curr_val} with index {i}")
                boards[curr_val][i] = 0 # undo move
                min_val = min(min_val, score)
                beta = min(beta, score)
                if beta <= alpha:
                    break
        
            
        return min_val

现寻求该算法的优化方案或改进建议，我是AI算法领域新手，恳请帮助。

内容的提问来源于stack exchange，提问作者Alex