Deep Reinforcement Learning Xiangqi Player with Monte Carlo Tree Search
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909652980072448 |
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| author | Yilmaz, Berk Hu, Junyu Liu, Jinsong |
| author_facet | Yilmaz, Berk Hu, Junyu Liu, Jinsong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_15880 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Reinforcement Learning Xiangqi Player with Monte Carlo Tree Search Yilmaz, Berk Hu, Junyu Liu, Jinsong Artificial Intelligence Machine Learning 68T05, 68T20 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. |
| title | Deep Reinforcement Learning Xiangqi Player with Monte Carlo Tree Search |
| topic | Artificial Intelligence Machine Learning 68T05, 68T20 |
| url | https://arxiv.org/abs/2506.15880 |