On Model Identification Based Optimal Control and It’s Applications to Multi-Agent Learning and Control

Rui Luo, Zhinan Peng and Jiangping Hu ()
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Rui Luo: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Zhinan Peng: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Jiangping Hu: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Mathematics, 2023, vol. 11, issue 4, 1-19

Abstract: This paper reviews recent progress in model identification-based learning and optimal control and its applications to multi-agent systems (MASs). First, a class of learning-based optimal control method, namely adaptive dynamic programming (ADP), is introduced, and the existing results using ADP methods to solve optimal control problems are reviewed. Then, this paper investigates various kinds of model identification methods and analyzes the feasibility of combining the model identification method with the ADP method to solve optimal control of unknown systems. In addition, this paper expounds the current applications of model identification-based ADP methods in the fields of single-agent systems (SASs) and MASs. Finally, some conclusions and some future directions are presented.

Keywords: model identification; optimal control; multi-agent systems; adaptive dynamic programming; reinforcement learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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