Fully Asynchronous Policy Evaluation in Distributed Reinforcement Learning over Networks

1 Mar 2020 • Xingyu Sha • Jia-Qi Zhang • Keyou You • Kaiqing Zhang • Tamer Başar

This paper proposes a \emph{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 by using (possibly delayed) information from its neighbors... (read more)

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