Off-Policy Actor-Critic with Shared Experience Replay

Abstract

We investigate the combination of actor-critic reinforcement learning algorithms with uniform large-scale experience replay and propose solutions for two challenges: (a) efficient actor-critic learning with experience replay (b) stability of off-policy learning where agents learn from other agents behaviour. We employ those insights to accelerate hyper-parameter sweeps in which all participating agents run concurrently and share their experience via a common replay module. To this end we analyze the bias-variance tradeoffs in V-trace, a form of importance sampling for actor-critic methods. Based on our analysis, we then argue for mixing experience sampled from replay with on-policy experience, and propose a new trust region scheme that scales effectively to data distributions where V-trace becomes unstable. We provide extensive empirical validation of the proposed solution. We further show the benefits of this setup by demonstrating state-of-the-art data efficiency on Atari among agents trained up until 200M environment frames.

References in corpus (13)

• Playing Atari with Deep Reinforcement Learning
• Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
• Trust Region Policy Optimization
• IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
• Distributed Prioritized Experience Replay
• Massively Parallel Methods for Deep Reinforcement Learning
• Model-Based Reinforcement Learning for Atari
• Learning to Navigate in Complex Environments
• Meta-Gradient Reinforcement Learning
• Playing FPS Games with Deep Reinforcement Learning
• Off-Policy Actor-Critic
• Ray Interference: a Source of Plateaus in Deep Reinforcement Learning
• The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning