SuperSuit introduces a collection of small functions which can wrap reinforcement learning environments to do preprocessing ('microwrappers'). We support Gymnasium for single agent environments and PettingZoo for multi-agent environments (both AECEnv and ParallelEnv environments).
Using it with Gymnasium to convert space invaders to have a grey scale observation space and stack the last 4 frames looks like:
import gymnasium
from supersuit import color_reduction_v0, frame_stack_v1
env = gymnasium.make('SpaceInvaders-v0')
env = frame_stack_v1(color_reduction_v0(env, 'full'), 4)
Similarly, using SuperSuit with PettingZoo environments looks like
from pettingzoo.butterfly import pistonball_v0
env = pistonball_v0.env()
env = frame_stack_v1(color_reduction_v0(env, 'full'), 4)
Please note: Once the planned wrapper rewrite of Gymnasium is complete and the vector API is stabilized, this project will be deprecated and rewritten as part of a new wrappers package in PettingZoo and the vectorized API will be redone, taking inspiration from the functionality currently in Gymnasium.
To install SuperSuit from pypi:
python3 -m venv env
source env/bin/activate
pip install --upgrade pip
pip install supersuit
Alternatively, to install SuperSuit from source, clone this repo, cd to it, and then:
python3 -m venv env
source env/bin/activate
pip install --upgrade pip
pip install -e .
If you use this in your research, please cite:
@article{SuperSuit,
Title = {SuperSuit: Simple Microwrappers for Reinforcement Learning Environments},
Author = {Terry, J. K and Black, Benjamin and Hari, Ananth},
journal={arXiv preprint arXiv:2008.08932},
year={2020}
}