Fully asynchronous policy evaluation in distributed reinforcement learning over networks

This paper proposes a fully asynchronous scheme for the policy evaluation problem of distributed reinforcement learning (DisRL) over directed peer-to-peer networks. Without waiting for any other node of the network, each node can locally update its value function at any time using (possibly delayed)...

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Vydané v:	Automatica (Oxford) Ročník 136; s. 110092
Hlavní autori:	Sha, Xingyu, Zhang, Jiaqi, You, Keyou, Zhang, Kaiqing, Başar, Tamer
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.02.2022
Predmet:	Distributed reinforcement learning Fully asynchronous updates Multi-agent networks Policy evaluation Policy evaluation Multi-agent networks Distributed reinforcement learning Fully asynchronous updates
ISSN:	0005-1098, 1873-2836
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