使用 『蒙特卡洛树搜索算法』 来实现 miniAlphaGo for Reversi，黑白棋中创建AIPlayer部分的python代码怎么实现

以下是一个简单的示例代码：

import random
import copy

class AIPlayer:
    def __init__(self, color, max_depth=3, num_simulations=100):
        self.color = color
        self.max_depth = max_depth
        self.num_simulations = num_simulations

    def get_move(self, board):
        best_move = None
        best_score = -float('inf')
        for move in board.get_legal_moves(self.color):
            score = self.evaluate_move(board, move)
            if score > best_score:
                best_move = move
                best_score = score
        return best_move

    def evaluate_move(self, board, move):
        # Make a copy of the board and apply the move
        new_board = copy.deepcopy(board)
        new_board.make_move(move, self.color)

        # Run simulations using Monte Carlo tree search
        total_score = 0
        for i in range(self.num_simulations):
            score = self.simulate(new_board)
            total_score += score

        return total_score / self.num_simulations

    def simulate(self, board):
        # Randomly play out the game until the end
        current_color = self.color
        while not board.is_game_over():
            move = random.choice(board.get_legal_moves(current_color))
            board.make_move(move, current_color)
            current_color = board.get_opponent(current_color)

        # Evaluate the final score
        score = board.get_score(self.color)
        if score > board.get_score(board.get_opponent(self.color)):
            return 1
        elif score < board.get_score(board.get_opponent(self.color)):
            return -1
        else:
            return 0

在这个实现中，`evaluate_move` 方法使用 Monte Carlo tree search 算法来评估每个可能的落子，并返回一个分数，表示这个落子的好坏程度。`simulate` 方法会随机地模拟一场游戏，然后评估最终的得分。在 `get_move` 方法中，AIPlayer 会遍历所有合法的落子，并选择分数最高的落子作为它的决策。