用深度强化学习与蒙特卡洛树搜索打造中国象棋智能体
Deep Reinforcement Learning Xiangqi Player with Monte Carlo Tree Search
- 结合神经网络与蒙特卡洛树搜索进行自对弈训练
- 在复杂规则下实现策略优化与自我提升
- 适合研究策略游戏AI或对传统棋类感兴趣的读者
本文提出一种深度强化学习（DRL）系统，用于中国象棋（Xiangqi），将神经网络与蒙特卡洛树搜索（MCTS）结合，实现战略自对弈与自我改进。针对中国象棋特有的棋盘布局、棋子移动限制及胜利条件等复杂性，该方法利用策略-价值网络与MCTS协同模拟走法后果并优化决策。克服了高分支因子和不对称棋子动态等挑战，提升了AI在具有文化意义的战略游戏中表现，并为将DRL-MCTS框架适配特定规则体系提供了新思路。
原文摘要 · Abstract (English)
This paper presents a Deep Reinforcement Learning (DRL) system for Xiangqi (Chinese Chess) that integrates neural networks with Monte Carlo Tree Search (MCTS) to enable strategic self-play and self-improvement. Addressing the underexplored complexity of Xiangqi, including its unique board layout, piece movement constraints, and victory conditions, our approach combines policy-value networks with MCTS to simulate move consequences and refine decision-making. By overcoming challenges such as Xiangqi's high branching factor and asymmetrical piece dynamics, our work advances AI capabilities in culturally significant strategy games while providing insights for adapting DRL-MCTS frameworks to domain-specific rule systems.
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