OpenGuanDan: A Large-Scale Imperfect Information Game Benchmark

• URL: http://arxiv.org/abs/2602.00676v1
• Date: Sat, 31 Jan 2026 11:46:29 GMT
• Title: OpenGuanDan: A Large-Scale Imperfect Information Game Benchmark
• Authors: Chao Li, Shangdong Yang, Chiheng Zhan, Zhenxing Ge, Yujing Hu, Bingkun Bao, Xingguo Chen, Yang Gao,
• Abstract summary: OpenGuanDan is a novel benchmark that enables both efficient simulation of GuanDan and comprehensive evaluation of both learning-based and rule-based AI agents.<n>OpenGuanDan poses a suite of nontrivial challenges, including imperfect information, large-scale information set and action spaces, a mixed learning objective involving cooperation and competition, long-horizon decision-making, variable action spaces, and dynamic team composition.<n>We conduct two types of evaluations: (1) pairwise competitions among all GuanDan AI agents, and (2) human-AI matchups.
• Score: 31.554414017099102
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The advancement of data-driven artificial intelligence (AI), particularly machine learning, heavily depends on large-scale benchmarks. Despite remarkable progress across domains ranging from pattern recognition to intelligent decision-making in recent decades, exemplified by breakthroughs in board games, card games, and electronic sports games, there remains a pressing need for more challenging benchmarks to drive further research. To this end, this paper proposes OpenGuanDan, a novel benchmark that enables both efficient simulation of GuanDan (a popular four-player, multi-round Chinese card game) and comprehensive evaluation of both learning-based and rule-based GuanDan AI agents. OpenGuanDan poses a suite of nontrivial challenges, including imperfect information, large-scale information set and action spaces, a mixed learning objective involving cooperation and competition, long-horizon decision-making, variable action spaces, and dynamic team composition. These characteristics make it a demanding testbed for existing intelligent decision-making methods. Moreover, the independent API for each player allows human-AI interactions and supports integration with large language models. Empirically, we conduct two types of evaluations: (1) pairwise competitions among all GuanDan AI agents, and (2) human-AI matchups. Experimental results demonstrate that while current learning-based agents substantially outperform rule-based counterparts, they still fall short of achieving superhuman performance, underscoring the need for continued research in multi-agent intelligent decision-making domain. The project is publicly available at https://github.com/GameAI-NJUPT/OpenGuanDan.

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