  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 \blue{between 1.5 $\times$ and 5.41$\times$},
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 \blue{algorithms} on various tasks. Across various
settings, we show that \textit{ActorQ} enables more environmentally friendly
reinforcement learning by achieving \blue{carbon emission improvements between
1.9$\times$ and 3.76$\times$} compared to training RL-agents in full-precision.
We believe that this is the first of many future works on enabling
computationally energy-efficient and sustainable reinforcement learning. The
source code is available here for the public to use:
\url{https://github.com/harvard-edge/QuaRL}.
