Fully Asynchronous Policy Evaluation in Distributed Reinforcement Learning over Networks
This addresses the challenge of stragglers and delays in peer-to-peer networks for reinforcement learning, offering a robust solution for distributed systems.
The paper tackles the policy evaluation problem in distributed reinforcement learning by proposing a fully asynchronous scheme where nodes update independently using delayed neighbor information, achieving exact convergence at a linear rate of O(c^k) and showing linear speedup with node count in experiments.
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.