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Adaptive Q-Learning for Data-Based Optimal Output Regulation With Experience Replay
Luo, Biao1; Yang, Yin2; Liu, Derong3
Source PublicationIEEE TRANSACTIONS ON CYBERNETICS
ISSN2168-2267
2018-12-01
Volume48Issue:12Pages:3337-3348
Corresponding AuthorLuo, Biao(biao.luo@hotmail.com)
AbstractIn this paper, the data-based optimal output regulation problem of discrete-time systems is investigated. An off-policy adaptive Q-learning (QL) method is developed by using real system data without requiring the knowledge of system dynamics and the mathematical model of utility function. By introducing the Q-function, an off-policy adaptive QI, algorithm is developed to learn the optimal Q-function. An adaptive parameter alpha(i) in the policy evaluation is used to achieve tradeoff between the current and future Q-functions. The convergence of adaptive QI, algorithm is proved and the influence of the adaptive parameter is analyzed. To realize the adaptive QL algorithm with real system data, the actor-critic neural network (NN) structure is developed. The least-squares scheme and the batch gradient descent method are developed to update the critic and actor NN weights, respectively. The experience replay technique is employed in the learning process, which leads to simple and convenient implementation of the adaptive QL method. Finally, the effectiveness of the developed adaptive QL method is verified through numerical simulations.
KeywordData-based experience replay neural networks (NNs) off-policy optimal control Q-learning (QL)
DOI10.1109/TCYB.2018.2821369
WOS KeywordDISCRETE-TIME-SYSTEMS ; H-INFINITY CONTROL ; SPATIALLY DISTRIBUTED PROCESSES ; UNCERTAIN NONLINEAR-SYSTEMS ; BARRIER LYAPUNOV FUNCTIONS ; POLICY ITERATION ; CONTROL DESIGN ; CONTROLLER-DESIGN ; UNKNOWN DYNAMICS ; TRACKING CONTROL
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61503377] ; National Natural Science Foundation of China[61533017] ; National Natural Science Foundation of China[U1501251] ; Qatar National Research Fund under National Priority Research Project[NPRP9-466-1-103]
Funding OrganizationNational Natural Science Foundation of China ; Qatar National Research Fund under National Priority Research Project
WOS Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000450613100007
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:11[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/22603
Collection复杂系统管理与控制国家重点实验室_平行控制
Corresponding AuthorLuo, Biao
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Hamad Bin Khalifa Univ, Coll Sci & Engn, Doha, Qatar
3.Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Guangdong, 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
Luo, Biao,Yang, Yin,Liu, Derong. Adaptive Q-Learning for Data-Based Optimal Output Regulation With Experience Replay[J]. IEEE TRANSACTIONS ON CYBERNETICS,2018,48(12):3337-3348.
APA Luo, Biao,Yang, Yin,&Liu, Derong.(2018).Adaptive Q-Learning for Data-Based Optimal Output Regulation With Experience Replay.IEEE TRANSACTIONS ON CYBERNETICS,48(12),3337-3348.
MLA Luo, Biao,et al."Adaptive Q-Learning for Data-Based Optimal Output Regulation With Experience Replay".IEEE TRANSACTIONS ON CYBERNETICS 48.12(2018):3337-3348.
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