CASIA OpenIR
Data-Based Reinforcement Learning for Nonzero-Sum Games With Unknown Drift Dynamics
Zhang, Qichao1,2; Zhao, Dongbin1,2
Source PublicationIEEE TRANSACTIONS ON CYBERNETICS
ISSN2168-2267
2019-08-01
Volume49Issue:8Pages:2874-2885
Abstract

This paper is concerned about the nonlinear optimization problem of nonzero-sum (NZS) games with unknown drift dynamics. The data-based integral reinforcement learning (IRL) method is proposed to approximate the Nash equilibrium of NZS games iteratively. Furthermore, we prove that the data-based IRL method is equivalent to the model-based policy iteration algorithm, which guarantees the convergence of the proposed method. For the implementation purpose, a singl-ecritic neural network structure for the NZS games is given. To enhance the application capability of the data-based IRL method, we design the updating laws of critic weights based on the offline and online iterative learning methods, respectively. Note that the experience replay technique is introduced in the online iterative learning, which can improve the convergence rate of critic weights during the learning process. The uniform ultimate boundedness of the critic weights are guaranteed using the Lyapunov method. Finally, the numerical results demonstrate the effectiveness of the data-based M. algorithm for nonlinear NZS games with unknown drift dynamics.

KeywordIntegral reinforcement learning (IRL) neural network (NN) nonzero-sum (NZS) games off-policy single-critic unknown drift dynamics
DOI10.1109/TCYB.2018.2830820
WOS KeywordH-INFINITY CONTROL ; NONLINEAR-SYSTEMS ; ALGORITHM
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61533017] ; National Natural Science Foundation of China[61573353] ; National Key Research and Development Plan[2016YFB0101000]
WOS Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000467561700005
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/24568
Collection中国科学院自动化研究所
Corresponding AuthorZhao, Dongbin
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Qichao,Zhao, Dongbin. Data-Based Reinforcement Learning for Nonzero-Sum Games With Unknown Drift Dynamics[J]. IEEE TRANSACTIONS ON CYBERNETICS,2019,49(8):2874-2885.
APA Zhang, Qichao,&Zhao, Dongbin.(2019).Data-Based Reinforcement Learning for Nonzero-Sum Games With Unknown Drift Dynamics.IEEE TRANSACTIONS ON CYBERNETICS,49(8),2874-2885.
MLA Zhang, Qichao,et al."Data-Based Reinforcement Learning for Nonzero-Sum Games With Unknown Drift Dynamics".IEEE TRANSACTIONS ON CYBERNETICS 49.8(2019):2874-2885.
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