Optimal Tracking Control of a Nonlinear Multiagent System Using Q-Learning via Event-Triggered Reinforcement Learning

This article offers an optimal control tracking method using an event-triggered technique and the internal reinforcement Q-learning (IrQL) algorithm to address the tracking control issue of unknown nonlinear systems with multiple agents (MASs). Relying on the internal reinforcement reward (IRR) form...

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Vydané v:	Entropy (Basel, Switzerland) Ročník 25; číslo 2; s. 299
Hlavní autori:	Wang, Ziwei, Wang, Xin, Tang, Yijie, Liu, Ying, Hu, Jun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 05.02.2023 MDPI
Predmet:	Algorithms Analysis Communication Control Controllers Design Distance learning event-triggered mechanism Game theory Iterative methods Machine learning Mathematical optimization Methods Multi-agent systems Multiagent systems Neural networks neural networks (NNs) Nonlinear control Nonlinear systems Optimal control optimal tracking control Parameter modification Process controls Reinforcement learning (Machine learning) reinforcement learning (RL) System dynamics systems with multiple agents Technology application Tracking control China systems with multiple agents event-triggered mechanism optimal tracking control neural networks (NNs) reinforcement learning (RL)
ISSN:	1099-4300, 1099-4300
On-line prístup:	Získať plný text
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