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A strategic learning algorithm for state-based games
Li, Changxi1; Xing, Yu2; He, Fenghua1; Cheng, Daizhan2
2020-03-01
Source PublicationAUTOMATICA
ISSN0005-1098
Volume113Pages:9
AbstractLearning algorithm design and applications of state-based games are investigated. First, a heuristic uncoupled learning algorithm, which is a two memory better reply learning rule, is proposed. Under reachability conditions it is proved that for any initial state, if all agents in the state-based game follow the proposed learning algorithm, the action state pair converges almost surely to an action invariant set of recurrent state equilibria. The design of the learning algorithm relies on global and local searches with finite memory, inertia, and randomness. Then, existence of time-efficient universal learning algorithm is studied. Finally, applications of our proposed learning algorithm are discussed, including learning pure Nash equilibrium in finite games and cooperative control with time-varying communication structure. (C) 2019 Elsevier Ltd. All rights reserved.
KeywordStrategic learning State-based games Recurrent state equilibria Multi-agent systems
DOI10.1016/j.automatica.2019.108615
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China (NSFC)[61473099] ; National Natural Science Foundation of China (NSFC)[61773371] ; National Natural Science Foundation of China (NSFC)[61733018] ; National Natural Science Foundation of China (NSFC)[61333001]
WOS Research AreaAutomation & Control Systems ; Engineering
WOS SubjectAutomation & Control Systems ; Engineering, Electrical & Electronic
WOS IDWOS:000514216600003
PublisherPERGAMON-ELSEVIER SCIENCE LTD
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Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/50929
Collection中国科学院数学与系统科学研究院
Corresponding AuthorHe, Fenghua
Affiliation1.Harbin Inst Technol, Control & Simulat Ctr, Harbin 150001, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Li, Changxi,Xing, Yu,He, Fenghua,et al. A strategic learning algorithm for state-based games[J]. AUTOMATICA,2020,113:9.
APA Li, Changxi,Xing, Yu,He, Fenghua,&Cheng, Daizhan.(2020).A strategic learning algorithm for state-based games.AUTOMATICA,113,9.
MLA Li, Changxi,et al."A strategic learning algorithm for state-based games".AUTOMATICA 113(2020):9.
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