{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T11:44:03Z","timestamp":1758282243391,"version":"3.44.0"},"reference-count":20,"publisher":"World Scientific Pub Co Pte Ltd","issue":"13","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62403403"],"award-info":[{"award-number":["62403403"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key Project of Scientific Research from Hunan Pro-vincial Department of Education","award":["24A0600"],"award-info":[{"award-number":["24A0600"]}]},{"name":"General Funding Project of Hunan Provincial Social Science Achievements Evaluation Committee","award":["XSP25YBZ065"],"award-info":[{"award-number":["XSP25YBZ065"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:p> The card game Dou Di Zhu (competitive two-against-one game) presents a challenging multiplayer imperfect-information game problem due to its large action space. We developed a deep Monte Carlo (DMC) reinforcement learning (RL) framework called ASP-DouZero, which employs dynamic programming (DP) to prune the action space effectively, using statistical analysis results. The pruned action space was then applied to self-play data generation and neural network decision processes. We evaluated ASP-DouZero against the state-of-the-art DouZero framework under identical training conditions. Results showed the proposed approach achieved a 5% higher win rate in standardized matches after convergence while requiring 50% less training time on equivalent hardware. These findings demonstrate that action space pruning significantly improves decision-making performance and training efficiency in DMC-based approaches for Dou Di Zhu. <\/jats:p>","DOI":"10.1142\/s0218001425520147","type":"journal-article","created":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T00:11:24Z","timestamp":1749773484000},"source":"Crossref","is-referenced-by-count":0,"title":["Action Space Pruning for Deep Reinforcement Learning in Dou Di Zhu"],"prefix":"10.1142","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0252-5292","authenticated-orcid":false,"given":"Shanglin","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Xiangnan University, Chenzhou 423000, P. R. China"},{"name":"Hunan Engineering Research Centre of Advanced, Embedded Computing and Intelligent, Medical Systems, Chenzhou 423000, P. R. 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