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Model identification methods can be feasibly combined with adaptive dynamic programming (ADP) to solve optimal control problems in unknown single-agent and multi-agent systems. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

February 10, 2023Mathematics40 citationsOpen Access

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

RLRui LuoChengdu University ZPZhinan PengUniversity of Electronic Science and Technology of China JHJiangping HuUniversity of Electronic Science and Technology of China

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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.

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