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Neural-network-based synchronous iteration learning method for multi-player zero-sum games
Song, Ruizhuo1; Wei, Qinglai2; Song, Biao1
Source PublicationNEUROCOMPUTING
2017-06-14
Volume242Pages:73-82
SubtypeArticle
AbstractIn this paper, a synchronous solution method for multi-player zero-sum games without system dynamics is established based on neural network. The policy iteration (PI) algorithm is presented to solve the Hamilton-Jacobi-Bellman (HJB) equation. It is proven that the obtained iterative cost function is convergent to the optimal game value. For avoiding system dynamics, off-policy learning method is given to obtain the iterative cost function, controls and disturbances based on Pl. Critic neural network (CNN), action neural networks (ANNs) and disturbance neural networks (DNNs) are used to approximate the cost function, controls and disturbances. The weights of neural networks compose the synchronous weight matrix, and the uniformly ultimately bounded (UUB) of the synchronous weight matrix is proven. Two examples are given to show that the effectiveness of the proposed synchronous solution method for multi-player ZS games. (C) 2017 Elsevier B.V. All rights reserved.
KeywordAdaptive Dynamic Programming Approximate Dynamic Programming Adaptive Critic Designs Multi-player Iteration Learning Neural Network
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.neucom.2017.02.051
WOS KeywordDISCRETE-TIME-SYSTEMS ; ADAPTIVE TRACKING CONTROL ; NONLINEAR-SYSTEMS ; POLICY ITERATION ; DEAD-ZONE ; DESIGN ; INPUT ; ALGORITHM
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61304079 ; Fundamental Research Funds for the Central Universities(FRF-TP-15-056A3) ; Open Research Project from SKLMCCS(20150104) ; 61673054 ; 61374105)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000399859500007
Citation statistics
Cited Times:10[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15265
Collection复杂系统管理与控制国家重点实验室_平行控制
Affiliation1.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Song, Ruizhuo,Wei, Qinglai,Song, Biao. Neural-network-based synchronous iteration learning method for multi-player zero-sum games[J]. NEUROCOMPUTING,2017,242:73-82.
APA Song, Ruizhuo,Wei, Qinglai,&Song, Biao.(2017).Neural-network-based synchronous iteration learning method for multi-player zero-sum games.NEUROCOMPUTING,242,73-82.
MLA Song, Ruizhuo,et al."Neural-network-based synchronous iteration learning method for multi-player zero-sum games".NEUROCOMPUTING 242(2017):73-82.
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