Safe multi-agent reinforcement learning for multi-robot control

A challenging problem in robotics is how to control multiple robots cooperatively and safely in real-world applications. Yet, developing multi-robot control methods from the perspective of safe multi-agent reinforcement learning (MARL) has merely been studied. To fill this gap, in this study, we inv...

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Veröffentlicht in:	Artificial intelligence Jg. 319; S. 103905
Hauptverfasser:	Gu, Shangding, Grudzien Kuba, Jakub, Chen, Yuanpei, Du, Yali, Yang, Long, Knoll, Alois, Yang, Yaodong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.06.2023
Schlagworte:	Constrained Markov game Constrained policy optimisation Safe multi-agent benchmarks Safe multi-robot control Safe multi-robot control Safe multi-agent benchmarks Constrained policy optimisation Constrained Markov game
ISSN:	0004-3702, 1872-7921
Online-Zugang:	Volltext
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