  Despite the recent progress in deep reinforcement learning field (RL), and,
arguably because of it, a large body of work remains to be done in reproducing
and carefully comparing different RL algorithms. We present catalyst.RL, an
open source framework for RL research with a focus on reproducibility and
flexibility. Main features of our library include large-scale asynchronous
distributed training, easy-to-use configuration files with the complete list of
hyperparameters for the particular experiments, efficient implementations of
various RL algorithms and auxiliary tricks, such as frame stacking, n-step
returns, value distributions, etc. To vindicate the usefulness of our
framework, we evaluate it on a range of benchmarks in a continuous control, as
well as on the task of developing a controller to enable a
physiologically-based human model with a prosthetic leg to walk and run. The
latter task was introduced at NeurIPS 2018 AI for Prosthetics Challenge, where
our team took the 3rd place, capitalizing on the ability of catalyst.RL to
train high-quality and sample-efficient RL agents.
