Reinforcement learning solution for HJB equation arising in constrained optimal control problem
文献类型：期刊论文
| 作者 | Luo, Biao1 ; Wu, Huai-Ning2; Huang, Tingwen3; Liu, Derong4
|
| 刊名 | NEURAL NETWORKS
 |
| 出版日期 | 2015-11-01 |
| 卷号 | 71页码:150-158 |
| 关键词 | Constrained optimal control Data-based Off-policy reinforcement learning Hamilton-Jacobi-Bellman equation The method of weighted residuals |
| 英文摘要 | 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. |
| WOS标题词 | Science & Technology ; Technology ; Life Sciences & Biomedicine |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Neurosciences |
| 研究领域[WOS] | Computer Science ; Neurosciences & Neurology |
| 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 |
| WOS记录号 | WOS:000364160900014 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/10729]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_智能化团队 |
| 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 |
| 推荐引用方式 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,2015,71:150-158. |
| 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,71,150-158. |
| MLA | Luo, Biao,et al."Reinforcement learning solution for HJB equation arising in constrained optimal control problem".NEURAL NETWORKS 71(2015):150-158. |
入库方式： OAI收割
来源：自动化研究所
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