Fully Asynchronous Policy Evaluation in Distributed Reinforcement Learning over Networks

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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METHOD TYPE
Entropy Regularization
Regularization
Convolution
Convolutions
Dense Connections
Feedforward Networks
Softmax
Output Functions
A3C
Policy Gradient Methods