Reinforcement learning solution for HJB equation arising in constrained optimal control problem
Luo, Biao1; Wu, Huai-Ning2; Huang, Tingwen3; Liu, Derong4
2015-11-01
发表期刊NEURAL NETWORKS
卷号71期号:0页码:150-158
文章类型Article
摘要The 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.
关键词Constrained Optimal Control Data-based Off-policy Reinforcement Learning Hamilton-jacobi-bellman Equation The Method Of Weighted Residuals
WOS标题词Science & Technology ; Technology ; Life Sciences & Biomedicine
DOI10.1016/j.neunet.2015.08.007
关键词[WOS]TIME NONLINEAR-SYSTEMS ; ADAPTIVE OPTIMAL-CONTROL ; DYNAMIC-PROGRAMMING ALGORITHM ; POLICY ITERATION ; INPUT CONSTRAINTS ; LINEAR-SYSTEMS ; CONTROL DESIGN ; STABILIZATION
收录类别SCI
语种英语
项目资助者National 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研究方向Computer Science ; Neurosciences & Neurology
WOS类目Computer Science, Artificial Intelligence ; Neurosciences
WOS记录号WOS:000364160900014
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/10729
专题复杂系统管理与控制国家重点实验室_平行控制
通讯作者Liu, Derong
作者单位1.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
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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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