Fully Asynchronous Policy Evaluation in Distributed Reinforcement Learning over Networks

TL;DR

This paper introduces a fully asynchronous distributed reinforcement learning algorithm that allows nodes to update independently without waiting, achieving linear convergence and robustness in networked environments.

Contribution

It presents the first fully asynchronous policy evaluation method with proven linear convergence for distributed reinforcement learning over directed networks.

Findings

Achieves linear convergence rate of ^k in distributed policy evaluation.

Speeds up linearly with the number of nodes in the network.

Robust to straggler nodes and delays.

Abstract

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. This is in sharp contrast to the gossip-based scheme where a pair of nodes concurrently update. Though the fully asynchronous setting involves a difficult multi-timescale decision problem, we design a novel stochastic average gradient (SAG) based distributed algorithm and develop a push-pull augmented graph approach to prove its exact convergence at a linear rate of $\mathcal{O}(c^k)$ where $c\in(0,1)$ and $k$ increases by one no matter on which node updates. Finally, numerical experiments validate that our method speeds up linearly with respect to the number of nodes, and is robust to straggler nodes.