蒙特卡洛博弈树五子棋代码

以下是一个简单的蒙特卡洛博弈树五子棋代码的示例：

import random

class Node:
    def __init__(self, move=None, parent=None, state=None):
        self.move = move
        self.parent = parent
        self.children = []
        self.wins = 0
        self.visits = 0
        self.untried_moves = state.get_legal_moves()
        self.player = state.current_player

    def select_child(self):
        best_score = -1
        best_child = None
        for child in self.children:
            score = child.wins / child.visits + (2 * math.log(self.visits) / child.visits)**0.5
            if score > best_score:
                best_score = score
                best_child = child
        return best_child

    def expand(self, state):
        move = random.choice(self.untried_moves)
        new_state = state.move(move)
        new_node = Node(move=move, parent=self, state=new_state)
        self.untried_moves.remove(move)
        self.children.append(new_node)
        return new_node

    def update(self, result):
        self.visits += 1
        self.wins += result

class MonteCarlo:
    def __init__(self, state, time_budget=10):
        self.root = Node(state=state)
        self.time_budget = time_budget

    def search(self):
        end_time = time.time() + self.time_budget
        while time.time() < end_time:
            node = self.select_node(self.root)
            result = self.rollout(node.state)
            self.backpropagate(node, result)
        return self.best_move()

    def select_node(self, node):
        while not node.untried_moves and node.children:
            node = node.select_child()
        if node.untried_moves:
            return node.expand(node.state)
        else:
            return node

    def rollout(self, state):
        player = state.current_player
        while not state.is_terminal():
            move = random.choice(state.get_legal_moves())
            state = state.move(move)
        if state.winner == player:
            return 1
        elif state.winner == 0:
            return 0.5
        else:
            return 0

    def backpropagate(self, node, result):
        while node:
            node.update(result)
            node = node.parent

    def best_move(self):
        best_score = -1
        best_move = None
        for child in self.root.children:
            score = child.wins / child.visits
            if score > best_score:
                best_score = score
                best_move = child.move
        return best_move

这个示例代码使用了一个 `Node` 类来表示蒙特卡洛树中的每个节点，其中包含一个棋步、一个父节点、一个子节点列表、胜利次数和访问次数等信息。还有一个 `MonteCarlo` 类来实现蒙特卡洛树搜索的过程，其中包括节点选择、扩展、模拟和回传等操作。最终可以通过调用 `search` 方法来得到最佳棋步。