PufferLib: Making Reinforcement Learning Libraries and Environments Play Nice

Joseph Suarez

Keywords: reinforcement learning; infrastructure; tooling

TL;DR: PufferLib makes complex reinforcement learning environments as simple as Atari

Abstract: Reinforcement learning (RL) frameworks often falter in complex environments due to inherent simplifying assumptions. This gap necessitates labor-intensive and error-prone intermediate conversion layers, limiting the applicability of RL as a whole. To address this challenge, we introduce PufferLib, a novel middleware solution. PufferLib transforms complex environments into a broadly compatible, vectorized format, eliminating the need for bespoke conversion layers and enabling more rigorous testing. Users interact with PufferLib through concise bindings, significantly reducing the technical overhead. We release PufferLib's complete source code under the MIT license, a pip module, a containerized setup, comprehensive documentation, and example integrations. We also maintain a community Discord channel to facilitate support and discussion.

Submission Number: 55