Kapturowski: Recurrent Experience Replay in Distributed Reinforcement Learning
Metadata
Title: Recurrent Experience Replay in Distributed Reinforcement Learning
Authors: Kapturowski et al
Publication Year: 2019
Journal: ICLR 2019
Abstract
Building on the recent successes of distributed training of RL agents, in this paper we investigate the training of RNN-based RL agents from distributed prioritized experience replay. We study the effects of parameter lag resulting in representational drift and recurrent state staleness and empirically derive an improved training strategy. Using a single network architecture and fixed set of hyper-parameters, the resulting agent, Recurrent Replay Distributed DQN, quadruples the previous state of the art on Atari-57, and matches the state of the art on DMLab-30. It is the first agent to exceed human-level performance in 52 of the 57 Atari games.