SuperSuit: Simple Microwrappers for Reinforcement Learning Environments
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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.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Aug 2020.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 1; 2024: 2; 2025: 3; 2026: 2.
10 papers.
- 2021GridLearn: Multiagent Reinforcement Learning for Grid-Aware Building Energy Managementcited by 12
- 2021Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environmentscited by 5
- 2024MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learningcited by 5
- 2024Enhancing Two-Player Performance Through Single-Player Knowledge Transfer: An Empirical Study on Atari 2600 Gamescited by 1
- 2025Nuclear Microreactor Control with Deep Reinforcement Learningcited by 1
- 2025Heterogeneous RBCs via Deep Multi-Agent Reinforcement Learningcited by 1
- 2026Auction-Based RIS Allocation With DRL: Controlling the Cost-Performance Trade-Offcited by 1
- 2023CoRL: Environment Creation and Management Focused on System Integrationcited by 0
- 2025Multi-Robot Collaboration through Reinforcement Learning and Abstract Simulationcited by 0
- 2026Cooperative Deep Reinforcement Learning for Fair RIS Allocationcited by 0
- 2026Cooperative Deep Reinforcement Learning for Fair RIS Allocation26 Mar 2026
- 2026Auction-Based RIS Allocation With DRL: Controlling the Cost-Performance Trade-Off18 Feb 2026
- 2025Heterogeneous RBCs via Deep Multi-Agent Reinforcement Learning14 Oct 2025
- 2025Nuclear Microreactor Control with Deep Reinforcement Learning31 Mar 2025
- 2025Multi-Robot Collaboration through Reinforcement Learning and Abstract Simulation7 Mar 2025
- 2024Enhancing Two-Player Performance Through Single-Player Knowledge Transfer: An Empirical Study on Atari 2600 Games22 Oct 2024
- 2024MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning23 Jul 2024
- 2023CoRL: Environment Creation and Management Focused on System Integration3 Mar 2023
- 2021Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environments1 Nov 2021
- 2021GridLearn: Multiagent Reinforcement Learning for Grid-Aware Building Energy Management12 Oct 2021