python实现井字棋小游戏（使用蒙特卡洛搜索树进行训练）

灰太狼 2023-09-30 16:18 4阅读 0赞

>  **需要源码请点赞关注收藏后评论区留言或私信博主**

> 蒙特卡洛搜索树是一类算法的统称，它适用于零和且确定环境的游戏，现在用蒙特卡洛搜索树算法对井字棋进行训练。
> 
> 训练要求将模拟次数设定为2000次，即每个状态都模拟2000次到达终点，到到达胜利叶子节点则回溯得一分，失败或平局则不得分。

**代码运行效果如下**

![72032550b8ab458dba9c90ed6da517ad.png][]

![6d550cec06ec475a8119e3f2529286b0.png][]

![30ce41d2f75a47b68d9422afaf59544e.png][]

部分代码如下

# 深度强化学习——原理、算法与PyTorch实战，代码名称：代31-例8.6-基于蒙特卡洛树的井字棋实例.py
    
    import numpy as np
    import sys
    import math
    import random
    
    
    # 初始化环境
    class environment():
        def __init__(self):
            self.start_env = np.array([[0] * 3] * 3)
    
    
    class State(object):
        def __init__(self):
            self.current_env = [[]]
            self.current_value = 0
            self.current_round_index = 0
            self.cumulative_choices = [[]]
            self.available_choice = [[]]
    
        # 定义结束情况
        def is_end(self):
            tiaojian = True
            for i in range(0, 3):
                for j in range(0, 3):
                    if self.current_env[i][j] == 0:
                        tiaojian = False
            for i in range(0, 3):
                if (np.array(self.current_env)[i] == np.array([1, 1, 1])).all() or (
                        np.array(self.current_env)[i] == np.array([2, 2, 2])).all():
                    tiaojian = True
            if (np.array(self.current_env)[:, 0] == np.array([1, 1, 1])).all() or (
                    np.array(self.current_env)[:, 0] == np.array([2, 2, 2])).all() or (
                    np.array(self.current_env)[:, 1] == np.array([1, 1, 1])).all() or (
                    np.array(self.current_env)[:, 1] == np.array([2, 2, 2])).all() or (
                    np.array(self.current_env)[:, 2] == np.array([1, 1, 1])).all() or (
                    np.array(self.current_env)[:, 2] == np.array([2, 2, 2])).all():
                tiaojian = True
            elif np.array(self.current_env)[0, 0] == np.array(self.current_env)[1, 1] == np.array(self.current_env)[
                2, 2] != 0:
                tiaojian = True
            elif np.array(self.current_env)[0, 2] == np.array(self.current_env)[1, 1] == np.array(self.current_env)[
                2, 0] != 0:
                tiaojian = True
            return tiaojian
    
        # 定义自家胜利情况
        def i_win(self):
            tiaojian = False
            for i in range(0, 3):
                if ((np.array(self.current_env)[i] == np.array([1, 1, 1])).all()):
                    tiaojian = True
    
            if (np.array(self.current_env)[:, 0] == np.array([1, 1, 1])).all() or (
                    np.array(self.current_env)[:, 1] == np.array([1, 1, 1])).all() or (
                    np.array(self.current_env)[:, 2] == np.array([1, 1, 1])).all():
                tiaojian = True
            if np.array(self.current_env)[0, 0] == np.array(self.current_env)[1, 1] == np.array(self.current_env)[
                2, 2] == 1:
                tiaojian = True
            if np.array(self.current_env)[0, 2] == np.array(self.current_env)[1, 1] == np.array(self.current_env)[
                2, 0] == 1:
                tiaojian = True
            return tiaojian
    
        # 定义自家失败情况
        def i_lose(self):
            tiaojian = False
            for i in range(0, 3):
                if ((np.array(self.current_env)[i] == np.array([2, 2, 2])).all()):
                    tiaojian = True
            if (np.array(self.current_env)[:, 0] == np.array([2, 2, 2])).all() or (
                    np.array(self.current_env)[:, 1] == np.array([2, 2, 2])).all() or (
                    np.array(self.current_env)[:, 2] == np.array([2, 2, 2])).all():
                tiaojian = True
            if np.array(self.current_env)[0, 0] == np.array(self.current_env)[1, 1] == np.array(self.current_env)[
                2, 2] == 2:
                tiaojian = True
            if np.array(self.current_env)[0, 2] == np.array(self.current_env)[1, 1] == np.array(self.current_env)[
                2, 0] == 2:
                tiaojian = True
            return tiaojian
    
        # 设置/获取可用动作
        def set_available_choice(self, choice):
            self.available_choice = choice
    
        def get_available_choice(self):
            return self.available_choice
    
        # 设置/获取当前环境
        def get_current_env(self):
            return self.current_env
    
        def set_current_env(self, env):
            self.current_env = env
    
        # 设置/获取累计奖赏
        def get_current_value(self):
            return self.current_value
    
        def set_current_value(self, value):
            self.current_value = value
    
        def get_current_round_index(self):
            return self.current_round_index
    
        def set_current_round_index(self, turn):
            self.current_round_index = turn
    
        # 设置/获取累积动作
        def get_cumulative_choices(self):
            return self.cumulative_choices
    
        def set_cumulative_choices(self, choices):
            self.cumulative_choices = choices
    
        # 判断是否结束
        def is_terminal(self):
            # The round index starts from 1 to max round number
            return self.is_end()
    
        # 计算累计奖赏
        def compute_reward(self):
            return self.current_value
    
        # 随机策略得到下一状态
        def get_next_state_with_random_choice(self):
            a = np.array([[0] * 3] * 3)
            b = [0] * len(self.available_choice)
            random_choice = random.choice([choice for choice in self.available_choice])
            next_state = State()
            next_state.set_current_round_index(self.current_round_index + 1)
            next_state.set_cumulative_choices(self.cumulative_choices + [random_choice])
            for i in range(0, len(self.available_choice)):
                b[i] = self.available_choice[i]
            next_state.available_choice = b
            next_state.available_choice.remove(random_choice)
            if next_state.current_round_index != 0 and next_state.current_round_index % 2 == 0:
                for i in range(0, 3):
                    for j in range(0, 3):
                        a[i][j] = self.current_env[i][j]
                a[random_choice[0]][random_choice[1]] = 1
                next_state.set_current_env(a)
            if next_state.current_round_index != 0 and next_state.current_round_index % 2 == 1:
                for i in range(0, 3):
                    for j in range(0, 3):
                        a[i][j] = self.current_env[i][j]
                a[random_choice[0]][random_choice[1]] = 2
                next_state.set_current_env(a)
            if next_state.i_win():
                next_state.set_current_value(1)
            if next_state.i_lose():
                next_state.set_current_value(-0.5)
            if next_state.i_lose() != True and next_state.i_win() != True:
                next_state.set_current_value(0)
            return next_state
    
        def __repr__(self):
            return "State: {}, value: {},  choices: {}".format(hash(self), self.current_value,
                                                               self.available_choice)
    
    
    # 建立节点
    class Node(object):
        def __init__(self):
            self.env = [[]]
            self.parent = None
            self.children = []
            self.visit_times = 0
            self.quality_value = 0.0
            self.state = None
    
        def avanum(self):
            num = 0
            a = self.get_state().current_env
            for i in range(0, 3):
                for j in range(0, 3):
                    if a[i][j] == 0:
                        num += 1
            return num
    
        def set_state(self, state):
            self.state = state
    
        def get_state(self):
            return self.state
    
        def get_parent(self):
            return self.parent
    
        def set_parent(self, parent):
            self.parent = parent
    
        def get_children(self):
            return self.children
    
        def get_visit_times(self):
            return self.visit_times
    
        def set_visit_times(self, times):
            self.visit_times = times
    
        def visit_times_add_one(self):
            self.visit_times += 1
    
        def get_quality_value(self):
            return self.quality_value
    
        def set_quality_value(self, value):
            self.quality_value = value
    
        def quality_value_add_n(self, n):
            self.quality_value += n
    
        def is_all_expand(self):
            return len(self.children) == self.avanum()
    
        def add_child(self, sub_node):
            sub_node.set_parent(self)
            self.children.append(sub_node)
    
        def __repr__(self):
            return "Node: {}, Q/N: {}/{}, state: {}".format(hash(self), self.quality_value, self.visit_times, self.state)
    
    
    # *************************************
    # 搜索树策略
    def tree_policy(node):
        # Check if the current node is the leaf node
        while node.get_state().is_terminal() == False:
            if node.is_all_expand():
                node_best = best_child(node, True)
            else:
                # Return the new sub node
                sub_node = expand(node)
                return sub_node
            # Return the leaf node
            return node_best
    
    
    # 默认策略
    def default_policy(node):
        # Get the state of the game
        current_state = node.get_state()
    
        # Run until the game over
    
        while current_state.is_terminal() == False:
            # Pick one random action to play and get next state
            current_state = current_state.get_next_state_with_random_choice()
    
        final_state_reward = current_state.compute_reward()
        return final_state_reward
    
    
    # 扩展
    def expand(node):
        tried_sub_node_states = [sub_node.get_state().current_env for sub_node in node.get_children()]
        # Check until get the new state which has the different action from others
        noin = False
        while noin == False:
            noin = True
            new_state = node.get_state().get_next_state_with_random_choice()
            for i in range(0, len(tried_sub_node_states)):
                if (new_state.current_env == tried_sub_node_states[i]).all():
                    noin = False
    
        sub_node = Node()
        sub_node.set_state(new_state)
        node.add_child(sub_node)
        return sub_node
    
    
    def best_child(node, is_exploration):
        # TODO: Use the min float value
        best_score = -sys.maxsize
        best_sub_node = None
    
        # Travel all sub nodes to find the best one
        for sub_node in node.get_children():
    
            # Ignore exploration for inference
            if is_exploration:
                C = 1 / math.sqrt(2.0)
            else:
                C = 0.0
    
            # UCB = quality / times + C * sqrt(2 * ln(total_times) / times)
            left = sub_node.get_quality_value() / sub_node.get_visit_times()
            right = 2.0 * math.log(node.get_visit_times()) / sub_node.get_visit_times()
            score = left + C * math.sqrt(right)
    
            if score > best_score:
                best_sub_node = sub_node
                best_score = score
    
        return best_sub_node
    
    
    # 回传
    def backup(node, reward):
        # Update util the root node
        while node != None:
            # Update the visit times
            node.visit_times_add_one()
            # Update the quality value
    
            node.quality_value_add_n(reward)
    
            # Change the node to the parent node
            node = node.parent
    
    
    # 蒙特卡洛搜索树算法
    def monte_carlo_tree_search(node):
        computation_budget = 4000
        # Run as much as possible under the computation budget
        for i in range(computation_budget):
            # 1. Find the best node to expand
            expand_node = tree_policy(node)
            # 2. Random run to add node and get reward
            reward = default_policy(expand_node)
            # 3. Update all passing nodes with reward
    
            backup(expand_node, reward)
    
        # N. Get the best next node
    
        best_next_node = best_child(node, False)
        a = [[sub_node.quality_value, sub_node.get_state().current_env] for sub_node in node.get_children()]
        print(a)
        return best_next_node
    
    
    # *************************************
    def main():
        # Create the initialized state and initialized node
        init_state = State()
        init_state.set_current_env(np.array([[0] * 3] * 3))
        init_state.set_current_round_index(1)
        init_state.set_available_choice([[0, 0], [0, 1], [0, 2], [1, 0], [1, 1], [1, 2], [2, 0], [2, 1], [2, 2]])
        init_node = Node()
        init_node.state = init_state
        init_env = environment()
        current_node = init_node
    
        # Set the rounds to play
        d = 0
        while (current_node.get_state().is_terminal() != True):
            if d % 2 == 0:
                print("Play round: {}".format(d + 1))
                print("你好，这是我下的步骤，来与我一战")
                current_node = monte_carlo_tree_search(current_node)
                print(current_node.get_state().current_env)
            else:
                new = Node()
                bb = State()
                new.set_state(bb)
                print("你的回合，请君下棋")
                n = 3
                a = [[0] * n] * n
                for i in range(n):
                    a[i] = input().split(" ")
                for i in range(0, 3):
                    for j in range(0, 3):
                        a[i][j] = int(a[i][j])
            = current_node.get_state().current_round_index + 1
                current_node = new
            d += 1
        if current_node.get_state().i_win():
            print("我赢了！你真菜")
        if current_node.get_state().i_lose():
            print("我输了，快给我调力度")
        if current_node.get_state().i_win() != True and current_node.get_state().i_lose() != True:
            print("平局，你还不错")
    
    
    if __name__ == "__main__":
        main()

[72032550b8ab458dba9c90ed6da517ad.png]: https://img-blog.csdnimg.cn/72032550b8ab458dba9c90ed6da517ad.png
[6d550cec06ec475a8119e3f2529286b0.png]: https://img-blog.csdnimg.cn/6d550cec06ec475a8119e3f2529286b0.png
[30ce41d2f75a47b68d9422afaf59544e.png]: https://img-blog.csdnimg.cn/30ce41d2f75a47b68d9422afaf59544e.png