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A Linear Programming-Based Reinforcement Learning Mechanism for Incomplete-Information Games

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摘要 Dear Editor,Recently, with the development of artificial intelligence, game intelligence decision-making has attracted more and more attention.In particular, incomplete-information games(IIG) have gradually become a new research focus, where players make decisions without sufficient information, such as the opponent's strategies or preferences.
出处 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第11期2340-2342,共3页 自动化学报(英文版)
基金 partially supported by the National Natural Science Foundation of China (62073079, 62173308) the Natural Science Foundation of Zhejiang Province of China (LZ24F030009, LR20F030001)。
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