蒙特卡罗数搜索算法代码

以下是一个简单的蒙特卡罗树搜索算法的示例代码，用于解决一个二人博弈问题。

class Node:
    def __init__(self, state, player):
        self.state = state # 当前节点的状态
        self.player = player # 当前玩家
        self.visit_count = 0 # 访问次数
        self.win_score = 0 # 获胜次数
        self.children = [] # 子节点列表

    def select(self):
        # 选择子节点
        node = self
        while node.children:
            node = max(node.children, key=lambda n: n.win_score / n.visit_count + sqrt(log(node.visit_count) / n.visit_count))
        return node

    def expand(self):
        # 扩展子节点
        for action in self.state.get_actions():
            state = self.state.apply_action(action)
            player = self.state.get_next_player(self.player)
            self.children.append(Node(state, player))

    def simulate(self):
        # 模拟
        state = self.state
        player = self.player
        while not state.is_terminal():
            action = random.choice(state.get_actions())
            state = state.apply_action(action)
            player = state.get_next_player(player)
        return state.get_winner() == self.player

    def backpropagate(self, result):
        # 回溯更新
        self.visit_count += 1
        self.win_score += result
        if self.parent:
            self.parent.backpropagate(result)

    def get_best_child(self):
        # 获取最佳子节点
        return max(self.children, key=lambda n: n.win_score / n.visit_count)

class MonteCarloTreeSearch:
    def __init__(self, state, player):
        self.root = Node(state, player)

    def search(self, iterations):
        # 迭代搜索
        for i in range(iterations):
            node = self.root.select()
            if not node.state.is_terminal():
                if not node.children:
                    node.expand()
                child = random.choice(node.children)
                result = child.simulate()
                child.backpropagate(result)
            else:
                result = node.state.get_winner() == node.player
                node.backpropagate(result)

        return self.root.get_best_child().state.get_last_action()

其中，`Node`类表示一个节点，包含当前状态、当前玩家、访问次数、获胜次数和子节点列表等信息。`MonteCarloTreeSearch`类实现了蒙特卡罗树搜索算法，包含迭代搜索、选择、扩展、模拟和回溯等步骤。在迭代搜索中，算法通过选择当前最优的子节点，扩展子节点，模拟并回溯更新节点信息，最终得到最佳的决策。