一、开篇引入：AI军棋助手的时代浪潮

 传统规则型AI的简化实现
class TraditionalJungleAI:
    def choose_move(self, board, available_moves):
        best_move = None
        best_score = -float('inf')
        for move in available_moves:
             基于固定规则评估每一步的价值
            score = self.evaluate_by_rules(board, move)
            if score > best_score:
                best_score = score
                best_move = move
        return best_move
    
    def evaluate_by_rules(self, board, move):
         纯规则判断：子力值、位置权重、保护状态等
        score = piece_value[move.piece]  position_weight[move.pos]
        if self.is_protected(move.piece):
            score += 10
        return score

import math
import random
from typing import List, Tuple

class MCTSNode:
    """MCTS树节点"""
    def __init__(self, state, parent=None, action=None):
        self.state = state           当前棋盘状态（含可见信息）
        self.parent = parent
        self.action = action         到达此节点的动作
        self.children = []           子节点列表
        self.visit_count = 0         访问次数
        self.total_value = 0.0       累积价值
        
    def ucb_score(self, exploration_constant=1.414) -> float:
        """UCB公式：平衡探索与利用"""
        if self.visit_count == 0:
            return float('inf')
        exploitation = self.total_value / self.visit_count
        exploration = exploration_constant  math.sqrt(
            math.log(self.parent.visit_count) / self.visit_count
        )
        return exploitation + exploration


class DRLMCTSAgent:
    """结合深度强化学习（DRL）的MCTS军棋智能体"""
    
    def __init__(self, policy_network, value_network):
        self.policy_network = policy_network   策略网络：给定局面，输出各动作概率
        self.value_network = value_network     价值网络：给定局面，输出胜率估值
        
    def search(self, state, num_simulations=800) -> Tuple[int, float]:
        """执行MCTS，返回最优动作及其估值"""
        root = MCTSNode(state)
        
        for _ in range(num_simulations):
            node = self._select(root)       选择：使用UCB公式选节点
            value = self._simulate(node)    模拟：使用价值网络评估或随机对局
            self._backpropagate(node, value)   反向传播
        
         返回访问次数最多的子节点对应的动作
        best_child = max(root.children, key=lambda c: c.visit_count)
        return best_child.action, best_child.total_value / best_child.visit_count
    
    def _select(self, node: MCTSNode) -> MCTSNode:
        """选择阶段：从根节点向下选择UCB值最高的节点"""
        while node.children:
             若存在未扩展的子节点，直接扩展
            if len(node.children) < len(self._get_legal_actions(node.state)):
                return self._expand(node)
            node = max(node.children, key=lambda c: c.ucb_score())
        return node
    
    def _expand(self, node: MCTSNode) -> MCTSNode:
        """扩展阶段：为当前节点添加一个新的子节点"""
        legal_actions = self._get_legal_actions(node.state)
        taken_actions = {c.action for c in node.children}
        for action in legal_actions:
            if action not in taken_actions:
                new_state = self._apply_action(node.state, action)
                child = MCTSNode(new_state, parent=node, action=action)
                node.children.append(child)
                return child
        return node
    
    def _simulate(self, node: MCTSNode) -> float:
        """模拟阶段：使用价值网络评估当前局面胜率"""
         实际应用中，可结合价值网络估值 + 快速随机走子
        return self.value_network.predict(node.state)
    
    def _backpropagate(self, node: MCTSNode, value: float):
        """反向传播：更新路径上所有节点的统计信息"""
        while node:
            node.visit_count += 1
            node.total_value += value
             注意：价值在交替层级需要取反（对手视角）
            value = -value
            node = node.parent
    
    def _get_legal_actions(self, state) -> List[int]:
        """获取当前状态下所有合法动作（实际需实现军棋规则逻辑）"""
        pass
    
    def _apply_action(self, state, action):
        """执行动作，返回新状态"""
        pass


 训练流程示意
def train_ai_agent(episodes=10000):
    """
    训练结合MCTS的强化学习军棋AI
    采用自我对弈（self-play）方式积累训练数据
    """
    policy_net = PolicyNetwork()    策略网络
    value_net = ValueNetwork()      价值网络
    agent = DRLMCTSAgent(policy_net, value_net)
    
    for episode in range(episodes):
        state = init_board()         初始化棋盘
        game_history = []
        
        while not is_game_over(state):
             使用MCTS选择最优动作
            action, value = agent.search(state, num_simulations=400)
            game_history.append((state, action, value))
            state = apply_action(state, action)
        
         对局结束，根据胜负计算最终奖励
        final_reward = 1 if is_winner(state, agent) else -1
        
         使用历史数据训练神经网络（实际需实现经验回放等机制）
        for state, action, predicted_value in game_history:
             训练策略网络：最大化动作概率与MCTS输出的一致
             训练价值网络：缩小predicted_value与final_reward的差距
            pass