KMS Of Academy of mathematics and systems sciences, CAS
| 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 Publication | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
 |
| ISSN | 2162-237X |
| Volume | 33Issue:4Pages:1520-1534 |
| Abstract | In 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. |
| Keyword | Mathematical 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) |
| DOI | 10.1109/TNNLS.2020.3042589 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | JSPS KAKENHI[17H03284] |
| WOS Research Area | Computer Science ; Engineering |
| WOS Subject | Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
| WOS ID | WOS:000778930100016 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/60286 |
| Collection | 中国科学院数学与系统科学研究院 |
| Corresponding Author | Xu, Zhenhui |
| Affiliation | 1.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. |
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