SuperSuit: Simple Microwrappers for Reinforcement Learning Environments
This solves a practical problem for reinforcement learning developers by providing a unified tool to reduce bugs and save time, though it is incremental as it builds on existing wrapper concepts.
The authors tackled the lack of a comprehensive library for reinforcement learning environment wrappers, which caused bugs and inefficiencies, by introducing SuperSuit, a Python library that includes all popular wrappers and supports standard specifications like Gym and PettingZoo.
In reinforcement learning, wrappers are universally used to transform the information that passes between a model and an environment. Despite their ubiquity, no library exists with reasonable implementations of all popular preprocessing methods. This leads to unnecessary bugs, code inefficiencies, and wasted developer time. Accordingly we introduce SuperSuit, a Python library that includes all popular wrappers, and wrappers that can easily apply lambda functions to the observations/actions/reward. It's compatible with the standard Gym environment specification, as well as the PettingZoo specification for multi-agent environments. The library is available at https://github.com/PettingZoo-Team/SuperSuit,and can be installed via pip.