Event-Triggered Multigradient Recursive Reinforcement Learning Tracking Control for Multiagent Systems

Weiwei Bai

doi:10.1109/TNNLS.2021.3094901

Event-Triggered Multigradient Recursive Reinforcement Learning Tracking Control for Multiagent Systems

2023-01-01

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陈晨 (C. L. Philip Chen)

DOI：10.1109/TNNLS.2021.3094901

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In this article, the tracking control problem of event-triggered multigradient recursive reinforcement learning is investigated for nonlinear multiagent systems (MASs). Attention is focused on the distributed reinforcement learning approach for MASs. The critic neural network (NN) is applied to estimate the long-term strategic utility function, and the actor NN is designed to approximate the uncertain dynamics in MASs. The multigradient recursive (MGR) strategy is tailored to learn the weight vector in NN, which eliminates the local optimal problem inherent in gradient descent method and decreases the dependence of initial value. Furthermore, reinforcement learning and event-triggered mechanism can improve the energy conservation of MASs by decreasing the amplitude of the controller signal and the controller update frequency, respectively. It is proved that all signals in MASs are semiglobal uniformly ultimately bounded (SGUUB) according to the Lyapunov theory. Simulation results are given to demonstrate the effectiveness of the proposed strategy.

Keywords:

Reinforcement learning

Artificial neural networks

Multi-agent systems

Graph theory

Stability criteria

Regulation

Performance analysis

Event-triggered control

multiagent systems (MASs)

multigradient recursive (MGR)

reinforcement learning

Event-Triggered Multigradient Recursive Reinforcement Learning Tracking Control for Multiagent Systems

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