A fork of gym-retro ('lets you turn classic video games into Gym environments for reinforcement learning with additional games'). Since gym-retro is in maintenance now and doesn't accept new games, plateforms or bug fixes, you can instead submit PRs with new games or features here in stable-retro.
Currently added games on top of gym-retro:
PvP games that support two models fighting each other:
As well as additional states on already integrated games.
pip3 install git+https://github.com/MatPoliquin/stable-retro.gitVideo on how to setup on Ubuntu and Windows: https://youtu.be/LRgGSQGNZeE
Docker image for M1 Macs: https://github.com/arvganesh/stable-retro-docker
@misc{stable-retro,
author = {Mathieu and Poliquin},
title = {Stable Retro, a fork of OpenAI's gym-retro},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/MatPoliquin/stable-retro}},
}Game Integration tool: https://youtube.com/playlist?list=PLmwlWbdWpZVtH6NXqWbrnWOf6SWv9nJBY
Join here: https://discord.gg/dXuBSg3B4D
Gym Retro lets you turn classic video games into Gym environments for reinforcement learning and comes with integrations for ~1000 games. It uses various emulators that support the Libretro API, making it fairly easy to add new emulators.
Supported platforms:
CPU with SSSE3 or better
Supported Pythons:
Each game integration has files listing memory locations for in-game variables, reward functions based on those variables, episode end conditions, savestates at the beginning of levels and a file containing hashes of ROMs that work with these files.
Please note that ROMs are not included and you must obtain them yourself. Most ROM hashes are sourced from their respective No-Intro SHA-1 sums.
Documentation is available at https://retro.readthedocs.io/en/latest/
You should probably start with the Getting Started Guide.
There is an effort to get this project to the Farama Foundation Project Standards. These development efforts are being coordinated in the stable-retro channel of the Farama Foundation's Discord. Click here for the invite
See LICENSES.md for information on the licenses of the individual cores.
The following non-commercial ROMs are included with Gym Retro for testing purposes:
Please cite using the following BibTeX entry:
@article{nichol2018retro,
title={Gotta Learn Fast: A New Benchmark for Generalization in RL},
author={Nichol, Alex and Pfau, Vicki and Hesse, Christopher and Klimov, Oleg and Schulman, John},
journal={arXiv preprint arXiv:1804.03720},
year={2018}
}