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Reinforcement learning solution for HJB equation arising in constrained optimal control problem
Luo, Biao1; Wu, Huai-Ning2; Huang, Tingwen3; Liu, Derong4
Source PublicationNEURAL NETWORKS
2015-11-01
Volume71Issue:0Pages:150-158
SubtypeArticle
AbstractThe constrained optimal control problem depends on the solution of the complicated Hamilton-Jacobi-Bellman equation (HJBE). In this paper, a data-based off-policy reinforcement learning (RL) method is proposed, which learns the solution of the HJBE and the optimal control policy from real system data. One important feature of the off-policy RL is that its policy evaluation can be realized with data generated by other behavior policies, not necessarily the target policy, which solves the insufficient exploration problem. The convergence of the off-policy RL is proved by demonstrating its equivalence to the successive approximation approach. Its implementation procedure is based on the actor-critic neural networks structure, where the function approximation is conducted with linearly independent basis functions. Subsequently, the convergence of the implementation procedure with function approximation is also proved. Finally, its effectiveness is verified through computer simulations. (C) 2015 Elsevier Ltd. All rights reserved.
KeywordConstrained Optimal Control Data-based Off-policy Reinforcement Learning Hamilton-jacobi-bellman Equation The Method Of Weighted Residuals
WOS HeadingsScience & Technology ; Technology ; Life Sciences & Biomedicine
DOI10.1016/j.neunet.2015.08.007
WOS KeywordTIME NONLINEAR-SYSTEMS ; ADAPTIVE OPTIMAL-CONTROL ; DYNAMIC-PROGRAMMING ALGORITHM ; POLICY ITERATION ; INPUT CONSTRAINTS ; LINEAR-SYSTEMS ; CONTROL DESIGN ; STABILIZATION
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61233001 ; Beijing Natural Science Foundation(4132078) ; Early Career Development Award of SKLMCCS ; NPRP grant from the Qatar National Research Fund(NPRP 4-1162-1-181) ; 61273140 ; 61304086 ; 61374105)
WOS Research AreaComputer Science ; Neurosciences & Neurology
WOS SubjectComputer Science, Artificial Intelligence ; Neurosciences
WOS IDWOS:000364160900014
Citation statistics
Cited Times:33[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/10729
Collection复杂系统管理与控制国家重点实验室_平行控制
Corresponding AuthorLiu, Derong
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Beijing Univ Aeronaut & Astronaut, Beihang Univ, Sci & Technol Aircraft Control Lab, Beijing 100191, Peoples R China
3.Texas A&M Univ Qatar, Doha, Qatar
4.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
Recommended Citation
GB/T 7714
Luo, Biao,Wu, Huai-Ning,Huang, Tingwen,et al. Reinforcement learning solution for HJB equation arising in constrained optimal control problem[J]. NEURAL NETWORKS,2015,71(0):150-158.
APA Luo, Biao,Wu, Huai-Ning,Huang, Tingwen,&Liu, Derong.(2015).Reinforcement learning solution for HJB equation arising in constrained optimal control problem.NEURAL NETWORKS,71(0),150-158.
MLA Luo, Biao,et al."Reinforcement learning solution for HJB equation arising in constrained optimal control problem".NEURAL NETWORKS 71.0(2015):150-158.
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