Deep Reinforcement Learning Xiangqi Player with Monte Carlo Tree Search
This work advances AI capabilities in culturally significant strategy games and provides insights for adapting DRL-MCTS frameworks to domain-specific rule systems, but it is incremental as it applies existing methods to a new domain.
The paper tackled the problem of developing an AI for Xiangqi (Chinese Chess) by integrating deep reinforcement learning with Monte Carlo Tree Search to enable strategic self-play and self-improvement, addressing its unique complexity such as high branching factor and asymmetrical piece dynamics.
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.