TechRxiv survey maps AI methods for imperfect-information card games with DouDiZhu as benchmark

[TechRxiv] A survey posted to TechRxiv on March 6, 2026 positions DouDiZhu as a representative testbed for imperfect-information card games (IICGs) and provides a review and categorization of existing AI methods. The authors, Qian Luo, Daochen Zha, Tianqiao Zhang and Xuewen Tan, note that imperfect-information games pose decision-making challenges due to hidden information and multi-agent interaction, and that IICGs form a strategically rich subclass that most existing reviews of imperfect-information games have given little attention. According to the survey, DouDiZhu has become an emerging AI research topic because of its asymmetric three-player roles, large state-action space and complex strategic interactions; recent work spans search-based methods such as MCTS, deep reinforcement learning methods such as DQN, DMC and PPO, and hybrid approaches integrating learning, search and opponent modeling. The authors said the literature remains fragmented and lacks a systematic synthesis, and the survey analyzes strengths and limitations, highlights trends and compares performance against available baselines. It also identifies open challenges and future directions, including generalization beyond standard rules, unified bidding and playing, continual learning and standardized evaluation. The paper is a preprint and has not been peer reviewed.