Input-Output Data-Based Output Antisynchronization Control of Multiagent Systems Using Reinforcement Learning Approach

This article investigates an output antisynchronization problem of multiagent systems by using an input-output data-based reinforcement learning approach. Till now, most of the existing results on antisynchronization problems required full-state information and exact system dynamics in the controlle...

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Vydáno v:IEEE transactions on industrial informatics Ročník 17; číslo 11; s. 7359 - 7367
Hlavní autoři: Peng, Zhinan, Zhao, Yiyi, Hu, Jiangping, Luo, Rui, Ghosh, Bijoy Kumar, Nguang, Sing Kiong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.11.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Artificial neural networks
Control systems design
Control theory
Heuristic algorithms
Incremental actor–critic (AC) network
Informatics
input–output data
Iterative algorithms
Iterative methods
Learning
Multi-agent systems
Multiagent systems
Network topology
optimal antisynchronization
Optimal control
partially observable multiagent systems
reinforcement learning (RL)
Synchronization
System dynamics
ISSN:1551-3203, 1941-0050
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Shrnutí:This article investigates an output antisynchronization problem of multiagent systems by using an input-output data-based reinforcement learning approach. Till now, most of the existing results on antisynchronization problems required full-state information and exact system dynamics in the controller design, which is always invalid in practical scenarios. To address this issue, a new system representation is constructed by using just the available input/output data from the multiagent system. Then, a novel value iteration algorithm is proposed to compute the optimal control laws for the agents; moreover, a convergence analysis is presented for the proposed algorithm. In the implementation of the data-based controllers, an actor-critic network structure is established to learn the optimal control laws without the requirement of information of the agent dynamics. An incremental weight updating rule is proposed to improve the learning performance. Finally, simulation results are presented to demonstrate the effectiveness of the proposed antisynchronization control strategy.
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2021.3050768