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Comprehensive comparison of online ADP algorithms for continuous-time optimal control
Zhu, Yuanheng1,2; Zhao, Dongbin1,2
AbstractOnline learning is an important property of adaptive dynamic programming (ADP). Online observations contain plentiful dynamics information, and ADP algorithms can utilize them to learn the optimal control policy. This paper reviews the research of online ADP algorithms for the optimal control of continuous-time systems. With the intensive study, ADP has been developed towards model free and data efficient. After separately introducing the algorithms, we compare their performance on the same problem. This paper is desired to provide a comprehensive understanding of continuous-time online ADP algorithms.
KeywordAdaptive Dynamic Programming Policy Iteration Integral Reinforcement Learning Experience Replay Off-policy
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Funding OrganizationNational Natural Science Foundation of China(61533017 ; Early Career Development Award of SKLMCCS ; 61573353 ; 61603382)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000426912500004
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Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
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GB/T 7714
Zhu, Yuanheng,Zhao, Dongbin. Comprehensive comparison of online ADP algorithms for continuous-time optimal control[J]. ARTIFICIAL INTELLIGENCE REVIEW,2018,49(4):531-547.
APA Zhu, Yuanheng,&Zhao, Dongbin.(2018).Comprehensive comparison of online ADP algorithms for continuous-time optimal control.ARTIFICIAL INTELLIGENCE REVIEW,49(4),531-547.
MLA Zhu, Yuanheng,et al."Comprehensive comparison of online ADP algorithms for continuous-time optimal control".ARTIFICIAL INTELLIGENCE REVIEW 49.4(2018):531-547.
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