JiangJun: Mastering Xiangqi by Tackling Non-Transitivity in Two-Player Zero-Sum Games

Transactions on Machine Learning Research (TMLR), 2023

Recommended citation: Yang Li, Kun Xiong, Yingping Zhang, Jiangcheng Zhu, Stephen Marcus McAleer, Wei Pan, Jun Wang, Zonghong Dai, & Yaodong Yang (2023). JiangJun: Mastering Xiangqi by Tackling Non-Transitivity in Two-Player Zero-Sum Games. Transactions on Machine Learning Research. https://openreview.net/forum?id=MMsyqXIJuk

(ChatGPT-Generated) The paper investigates the non-transitive nature of the Chinese board game Xiangqi and introduces the JiangJun algorithm, blending Monte-Carlo Tree Search (MCTS) with Policy Space Response Oracles (PSRO) to approximate a Nash equilibrium. Tested through a WeChat mini program, the algorithm boasts a remarkable 99.41% win rate against human players, confirming its effectiveness in handling non-transitivity.

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Recommended citation: Yang Li, Kun Xiong, Yingping Zhang, Jiangcheng Zhu, Stephen Marcus McAleer, Wei Pan, Jun Wang, Zonghong Dai, & Yaodong Yang (2023). JiangJun: Mastering Xiangqi by Tackling Non-Transitivity in Two-Player Zero-Sum Games. Transactions on Machine Learning Research.