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Off-Policy Integral Reinforcement Learning Method to Solve Nonlinear Continuous-Time Multiplayer Nonzero-Sum Games
Song, Ruizhuo1; Lewis, Frank L.2,3; Wei, Qinglai4
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
2017-03-01
Volume28Issue:3Pages:704-713
SubtypeArticle
AbstractThis paper establishes an off-policy integral reinforcement learning (IRL) method to solve nonlinear continuous-time (CT) nonzero-sum (NZS) games with unknown system dynamics. The IRL algorithm is presented to obtain the iterative control and off-policy learning is used to allow the dynamics to be completely unknown. Off-policy IRL is designed to do policy evaluation and policy improvement in the policy iteration algorithm. Critic and action networks are used to obtain the performance index and control for each player. The gradient descent algorithm makes the update of critic and action weights simultaneously. The convergence analysis of the weights is given. The asymptotic stability of the closed-loop system and the existence of Nash equilibrium are proved. The simulation study demonstrates the effectiveness of the developed method for nonlinear CT NZS games with unknown system dynamics.
KeywordAdaptive Critic Designs Adaptive Dynamic Programming (Adp) Approximate Dynamic Programming Integral Reinforcement Learning (Irl) Nonlinear Systems Nonzero Sum (Nzs) Off-policy
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TNNLS.2016.2582849
WOS KeywordOPTIMAL TRACKING CONTROL ; ADAPTIVE OPTIMAL-CONTROL ; H-INFINITY CONTROL ; DIFFERENTIAL-GAMES ; UNKNOWN DYNAMICS ; FEEDBACK-CONTROL ; LINEAR-SYSTEMS ; CONTROL DESIGN ; OUTPUT DATA ; ALGORITHM
Indexed BySCI
Language英语
Funding OrganizationDirectorate for Biological Sciences through the National Science Foundation(ECCS-1128050) ; National Natural Science Foundation of China(61304079 ; Fundamental Research Funds for the Central Universities(FRF-TP-15-056A3) ; State Key Laboratory of Management and Control for Complex Systems(20150104) ; Office of Naval Research(N00014-13-1-0562) ; Air Force Office of Scientific Research European Office of Aerospace Research and Development(13-3055) ; U.S. Army Research Office(W911NF-11-D-0001) ; China National Natural Science Foundation(61120106011) ; China Education Ministry Project 111(B08015) ; 61433004 ; 61374105)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000395980500019
Citation statistics
Cited Times:29[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14397
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.Univ Texas Arlington, UTA Res Inst, Arlington, TX 76019 USA
3.Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110819, Peoples R China
4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Song, Ruizhuo,Lewis, Frank L.,Wei, Qinglai. Off-Policy Integral Reinforcement Learning Method to Solve Nonlinear Continuous-Time Multiplayer Nonzero-Sum Games[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2017,28(3):704-713.
APA Song, Ruizhuo,Lewis, Frank L.,&Wei, Qinglai.(2017).Off-Policy Integral Reinforcement Learning Method to Solve Nonlinear Continuous-Time Multiplayer Nonzero-Sum Games.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,28(3),704-713.
MLA Song, Ruizhuo,et al."Off-Policy Integral Reinforcement Learning Method to Solve Nonlinear Continuous-Time Multiplayer Nonzero-Sum Games".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 28.3(2017):704-713.
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