SuperSuit: Simple Microwrappers for Reinforcement Learning Environments

• URL: http://arxiv.org/abs/2008.08932v1
• Date: Mon, 17 Aug 2020 00:30:06 GMT
• Title: SuperSuit: Simple Microwrappers for Reinforcement Learning Environments
• Authors: J. K. Terry, Benjamin Black, Ananth Hari
• Abstract summary: SuperSuit is a Python library that includes all popular wrappers and wrappers that can easily apply 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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