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

Published: 24 Jul 2023, Last Modified: 24 Jul 2023Accepted by TMLREveryoneRevisionsBibTeX
Abstract: This paper presents an empirical exploration of non-transitivity in perfect-information games, specifically focusing on Xiangqi, a traditional Chinese board game comparable in game-tree complexity to chess and shogi. By analyzing over 10,000 records of human Xiangqi play, we highlight the existence of both transitive and non-transitive elements within the game’s strategic structure. To address non-transitivity, we introduce the JiangJun algorithm, an innovative combination of Monte-Carlo Tree Search (MCTS) and Policy Space Response Oracles (PSRO) designed to approximate a Nash equilibrium. We evaluate the algorithm empirically using a WeChat mini program and achieve a Master level with a 99.41% win rate against human players. The algorithm’s effectiveness in overcoming non-transitivity is confirmed by a plethora of metrics, such as relative population performance and visualization results. Our project site is available at https://sites.google.com/view/jiangjun-site/.
Submission Length: Regular submission (no more than 12 pages of main content)
Code: https://github.com/liyang619/JiangJun
Assigned Action Editor: ~Michal_Valko1
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Submission Number: 938