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Discrete-Time Deterministic Q-Learning: A Novel Convergence Analysis
Wei, Qinglai1; Lewis, Frank L.2,3; Sun, Qiuye4; Yan, Pengfei1; Song, Ruizhuo5
Source PublicationIEEE TRANSACTIONS ON CYBERNETICS
2017-05-01
Volume47Issue:5Pages:1224-1237
SubtypeArticle
AbstractIn this paper, a novel discrete-time deterministic Q-learning algorithm is developed. In each iteration of the developed Q-learning algorithm, the iterative Q function is updated for all the state and control spaces, instead of updating for a single state and a single control in traditional Q-learning algorithm. A new convergence criterion is established to guarantee that the iterative Q function converges to the optimum, where the convergence criterion of the learning rates for traditional Q-learning algorithms is simplified. During the convergence analysis, the upper and lower bounds of the iterative Q function are analyzed to obtain the convergence criterion, instead of analyzing the iterative Q function itself. For convenience of analysis, the convergence properties for undiscounted case of the deterministic Q-learning algorithm are first developed. Then, considering the discounted factor, the convergence criterion for the discounted case is established. Neural networks are used to approximate the iterative Q function and compute the iterative control law, respectively, for facilitating the implementation of the deterministic Q-learning algorithm. Finally, simulation results and comparisons are given to illustrate the performance of the developed algorithm.
KeywordAdaptive Critic Designs Adaptive Dynamic Programming (Adp) Approximate Dynamic Programming Neural Networks (Nns) Neuro-dynamic Programming Optimal Control Q-learning
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TCYB.2016.2542923
WOS KeywordOPTIMAL TRACKING CONTROL ; ZERO-SUM GAMES ; H-INFINITY CONTROL ; INPUT-OUTPUT DATA ; DEAD-ZONE INPUT ; NONLINEAR-SYSTEMS ; ALGORITHM ; DESIGN ; REPRESENTATION ; APPROXIMATION
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation (NNSF) of China(61374105 ; Fundamental Research Funds for the Central Universities(FRF-TP-15-056A3) ; Open Research Project from SKLMCCS(20150104) ; National Science Foundation(ECCS-1405173 ; Office of Naval Research, Arlington, VA, USA(N00014-13-1-0562 ; U.S. Army Research Office(W911NF-11-D-0001) ; China NNSF(61120106011) ; China Education Ministry Project 111(B08015) ; 61304079 ; IIS-1208623) ; N000141410718) ; 61273140)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000399797000009
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13630
Collection复杂系统管理与控制国家重点实验室_平行控制
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Texas Arlington, UTA Res Inst, Arlington, TX 76118 USA
3.Northeastern Univ, Shenyang 110036, Peoples R China
4.Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110036, Peoples R China
5.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
Recommended Citation
GB/T 7714
Wei, Qinglai,Lewis, Frank L.,Sun, Qiuye,et al. Discrete-Time Deterministic Q-Learning: A Novel Convergence Analysis[J]. IEEE TRANSACTIONS ON CYBERNETICS,2017,47(5):1224-1237.
APA Wei, Qinglai,Lewis, Frank L.,Sun, Qiuye,Yan, Pengfei,&Song, Ruizhuo.(2017).Discrete-Time Deterministic Q-Learning: A Novel Convergence Analysis.IEEE TRANSACTIONS ON CYBERNETICS,47(5),1224-1237.
MLA Wei, Qinglai,et al."Discrete-Time Deterministic Q-Learning: A Novel Convergence Analysis".IEEE TRANSACTIONS ON CYBERNETICS 47.5(2017):1224-1237.
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