Neural-network-based synchronous iteration learning method for multi-player zero-sum games

doi:10.1016/j.neucom.2017.02.051

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 平行控制

	Neural-network-based synchronous iteration learning method for multi-player zero-sum games
	Song, Ruizhuo 1; Wei, Qinglai2 ; Song, Biao 1
刊名	NEUROCOMPUTING
	2017-06-14
卷号	242 页码:73-82
关键词	Adaptive Dynamic Programming Approximate Dynamic Programming Adaptive Critic Designs Multi-player Iteration Learning Neural Network
DOI	10.1016/j.neucom.2017.02.051
文献子类	Article
英文摘要	In 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.
WOS关键词	DISCRETE-TIME-SYSTEMS ; ADAPTIVE TRACKING CONTROL ; NONLINEAR-SYSTEMS ; POLICY ITERATION ; DEAD-ZONE ; DESIGN ; INPUT ; ALGORITHM
WOS研究方向	Computer Science
语种	英语
WOS记录号	WOS:000399859500007
资助机构	National 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)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/15265]
专题	复杂系统管理与控制国家重点实验室_平行控制
作者单位	1.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
推荐引用方式 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.