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Since the recent advent of deep reinforcement learning for game play andsimulated robotic control, a multitude of new algorithms have flourished. Mostare model-free algorithms which can be categorized into three families: deepQ-learning, policy gradients, and Q-value policy gradients. These havedeveloped along separate lines of research, such that few, if any, code basesincorporate all three kinds. Yet these algorithms share a great depth of commondeep reinforcement learning machinery. We are pleased to share rlpyt, whichimplements all three algorithm families on top of a shared, optimizedinfrastructure, in a single repository. It contains modular implementations ofmany common deep RL algorithms in Python using PyTorch, a leading deep learninglibrary. rlpyt is designed as a high-throughput code base for small- tomedium-scale research in deep RL. This white paper summarizes its features,algorithms implemented, and relation to prior work, and concludes with detailedimplementation and usage notes. rlpyt is available athttps://github.com/astooke/rlpyt.

rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch