QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement Learning

Gabe Barth-Maron
Maximilian Lam
Sharad Chitlangia
Srivatsan Krishnan
Vijay Janapa Reddi
Zishen Wan
Transactions on Machine Learning Research (TMLR) 2022 (2022)

Abstract

Deep reinforcement learning continues to show tremendous potential in achieving task-level autonomy, however, its computational and energy demands remain prohibitively high. In this paper, we tackle this problem by applying quantization to reinforcement learning. To that end, we introduce a novel Reinforcement Learning (RL) training paradigm, \textit{ActorQ}, to speed up actor-learner distributed RL training. \textit{ActorQ} leverages 8-bit quantized actors to speed up data collection without affecting learning convergence. Our quantized distributed RL training system, \textit{ActorQ}, demonstrates end-to-end speedups of 1.5 - 2.5 , and faster convergence over full precision training on a range of tasks (Deepmind Control Suite) and different RL algorithms (D4PG, DQN). Furthermore, we compare the carbon emissions (Kgs of CO2) of \textit{ActorQ} versus standard reinforcement learning on various tasks. Across various settings, we show that \textit{ActorQ} enables more environmentally friendly reinforcement learning by achieving 2.8 less carbon emission and energy compared to training RL-agents in full-precision. Finally, we demonstrate empirically that aggressively quantized RL-policies (up to 4/5 bits) enable significant speedups on quantization-friendly (supports native quantization) resource-constrained edge devices, without degrading accuracy. We believe that this is the first of many future works on enabling computationally energy-efficient and sustainable reinforcement learning. The source code for QuaRL is available here for the public to use: \url{https://bit.ly/quarl-tmlr}.

Research Areas