A strategic learning algorithm for state-based games

doi:10.1016/j.automatica.2019.108615

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	A strategic learning algorithm for state-based games
	Li, Changxi 1; Xing, Yu 2; He, Fenghua 1; Cheng, Daizhan 2
	2020-03-01
发表期刊	AUTOMATICA
ISSN	0005-1098
卷号	113 页码:9
摘要	Learning 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.
关键词	Strategic learning State-based games Recurrent state equilibria Multi-agent systems
DOI	10.1016/j.automatica.2019.108615
收录类别	SCI
语种	英语
资助项目	National 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研究方向	Automation & Control Systems ; Engineering
WOS类目	Automation & Control Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:000514216600003
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/50929
专题	中国科学院数学与系统科学研究院
通讯作者	He, Fenghua
作者单位	1.Harbin Inst Technol, Control & Simulat Ctr, Harbin 150001, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
推荐引用方式 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.