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Data-driven adaptive dynamic programming for continuous-time fully cooperative games with partially constrained inputs
Zhang, Qichao1,2; Zhao, Dongbin1,2; Zhu, Yuanheng1,2
Source PublicationNEUROCOMPUTING
2017-05-17
Volume238Issue:*Pages:377-386
SubtypeArticle
AbstractIn this paper, the fully cooperative game with partially constrained inputs in the continuous-time Markov decision process environment is investigated using a novel data-driven adaptive dynamic programming method. First, the model-based policy iteration algorithm with one iteration loop is proposed, where the knowledge of system dynamics is required. Then, it is proved that the iteration sequences of value functions and control policies can converge to the optimal ones. In order to relax the exact knowledge of the system dynamics, a model-free iterative equation is derived based on the model-based algorithm and the integral reinforcement learning. Furthermore, a data-driven adaptive dynamic programming is developed to solve the model-free equation using generated system data. From the theoretical analysis, we prove that this model-free iterative equation is equivalent to the model-based iterative equations, which means that the data-driven algorithm can approach the optimal value function and control policies. For the implementation purpose, three neural networks are constructed to approximate the solution of the model-free iteration equation using the off-policy learning scheme after the available system data is collected in the online measurement phase. Finally, two examples are provided to demonstrate the effectiveness of the proposed scheme. (C) 2017 Published by Elsevier B.V.
KeywordAdaptive Dynamic Programming Optimal Control Neural Network Fully Cooperative Games Data-driven Constrained Input
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.neucom.2017.01.076
WOS KeywordZERO-SUM GAMES ; H-INFINITY CONTROL ; DIFFERENTIAL GRAPHICAL GAMES ; NONLINEAR-SYSTEMS ; LEARNING SOLUTION ; UNKNOWN DYNAMICS ; MULTIAGENT SYSTEMS ; EXPERIENCE REPLAY ; CONTROL DESIGN ; ALGORITHM
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China (NSFC)(61273136 ; National Key Research and Development Plan(2016YFB0101000) ; 61573353 ; 61533017 ; 61603382)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000397372100033
Citation statistics
Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14336
Collection复杂系统管理与控制国家重点实验室_深度强化学习
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, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Qichao,Zhao, Dongbin,Zhu, Yuanheng. Data-driven adaptive dynamic programming for continuous-time fully cooperative games with partially constrained inputs[J]. NEUROCOMPUTING,2017,238(*):377-386.
APA Zhang, Qichao,Zhao, Dongbin,&Zhu, Yuanheng.(2017).Data-driven adaptive dynamic programming for continuous-time fully cooperative games with partially constrained inputs.NEUROCOMPUTING,238(*),377-386.
MLA Zhang, Qichao,et al."Data-driven adaptive dynamic programming for continuous-time fully cooperative games with partially constrained inputs".NEUROCOMPUTING 238.*(2017):377-386.
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