NAPPO: Modular and scalable reinforcement learning in pytorch

Reinforcement learning (RL) has been very successful in recent years but, limited by its sample inefficiency, often requires large computational resources. While new methods are being investigated to increase the efficiency of RL algorithms it is critical to enable training at scale, yet using a code-base flexible enough to allow for method experimentation. Here, we present NAPPO, a pytorch-based library for RL which provides scalable proximal policy optimization (PPO) implementations in a simple, modular package. We validate it by replicating previous results on Mujoco and Atari environments. Furthermore, we provide insights on how a variety of distributed training schemes with synchronous and asynchronous communication patterns perform. Finally we showcase NAPPO by obtaining the highest to-date test performance on the Obstacle Tower Unity3D challenge environment. The full source code is available.