CSpace
Model-Free Reinforcement Learning by Embedding an Auxiliary System for Optimal Control of Nonlinear Systems
Xu, Zhenhui1; Shen, Tielong1; Cheng, Daizhan2
2022-04-01
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN2162-237X
Volume33Issue:4Pages:1520-1534
AbstractIn this article, a novel integral reinforcement learning (IRL) algorithm is proposed to solve the optimal control problem for continuous-time nonlinear systems with unknown dynamics. The main challenging issue in learning is how to reject the oscillation caused by the externally added probing noise. This article challenges the issue by embedding an auxiliary trajectory that is designed as an exciting signal to learn the optimal solution. First, the auxiliary trajectory is used to decompose the state trajectory of the controlled system. Then, by using the decoupled trajectories, a model-free policy iteration (PI) algorithm is developed, where the policy evaluation step and the policy improvement step are alternated until convergence to the optimal solution. It is noted that an appropriate external input is introduced at the policy improvement step to eliminate the requirement of the input-to-state dynamics. Finally, the algorithm is implemented on the actor-critic structure. The output weights of the critic neural network (NN) and the actor NN are updated sequentially by the least-squares methods. The convergence of the algorithm and the stability of the closed-loop system are guaranteed. Two examples are given to show the effectiveness of the proposed algorithm.
KeywordMathematical model Trajectory Heuristic algorithms Optimal control System dynamics Artificial neural networks Convergence Approximate optimal control design auxiliary trajectory completely model-free integral reinforcement learning (IRL)
DOI10.1109/TNNLS.2020.3042589
Indexed BySCI
Language英语
Funding ProjectJSPS KAKENHI[17H03284]
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000778930100016
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/60286
Collection中国科学院数学与系统科学研究院
Corresponding AuthorXu, Zhenhui
Affiliation1.Sophia Univ, Dept Engn & Appl Sci, Tokyo 1028554, Japan
2.Chinese Acad Sci, Acad Math & Syst Sci, Key Lab Syst & Control, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Xu, Zhenhui,Shen, Tielong,Cheng, Daizhan. Model-Free Reinforcement Learning by Embedding an Auxiliary System for Optimal Control of Nonlinear Systems[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2022,33(4):1520-1534.
APA Xu, Zhenhui,Shen, Tielong,&Cheng, Daizhan.(2022).Model-Free Reinforcement Learning by Embedding an Auxiliary System for Optimal Control of Nonlinear Systems.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,33(4),1520-1534.
MLA Xu, Zhenhui,et al."Model-Free Reinforcement Learning by Embedding an Auxiliary System for Optimal Control of Nonlinear Systems".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 33.4(2022):1520-1534.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Xu, Zhenhui]'s Articles
[Shen, Tielong]'s Articles
[Cheng, Daizhan]'s Articles
Baidu academic
Similar articles in Baidu academic
[Xu, Zhenhui]'s Articles
[Shen, Tielong]'s Articles
[Cheng, Daizhan]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Xu, Zhenhui]'s Articles
[Shen, Tielong]'s Articles
[Cheng, Daizhan]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.