# SuperSuit: Simple Microwrappers for Reinforcement Learning Environments

arXiv: https://arxiv.org/abs/2008.08932 · HTML: https://mlsanity.com/paper/2008.08932/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2020.

## Built on by (most cited first, 10 of 10)

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- [Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environments](https://mlsanity.com/paper/2111.01100/) (2021)
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- [Multi-Robot Collaboration through Reinforcement Learning and Abstract Simulation](https://mlsanity.com/paper/2503.05092/) (2025)
- [Nuclear Microreactor Control with Deep Reinforcement Learning](https://mlsanity.com/paper/2504.00156/) (2025)
- [Heterogeneous RBCs via Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2510.12272/) (2025)
- [Auction-Based RIS Allocation With DRL: Controlling the Cost-Performance Trade-Off](https://mlsanity.com/paper/2603.04433/) (2026)
- [CoRL: Environment Creation and Management Focused on System Integration](https://mlsanity.com/paper/2303.02182/) (2023)
- [Cooperative Deep Reinforcement Learning for Fair RIS Allocation](https://mlsanity.com/paper/2603.25572/) (2026)

## Abstract (arXiv)

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.