PettingZoo VS Arcade-Learning-Environment

Release notes: https://github.com/Farama-Foundation/PettingZoo/releases/tag/1.24.0

To address this issue, we are excited to announce the release of Shimmy as a mature Farama Foundation project. Shimmy is an API compatibility tool for converting external RL environments to the Gymnasium and PettingZoo APIs. This allows users to access a wide range of single and multi-agent environments, all under a single standard API.

I am currently trying to implement my own version of a Connect Four Environment based on the version available on the PettingZoo Library github (https://github.com/Farama-Foundation/PettingZoo/blob/master/pettingzoo/classic/connect_four/connect_four.py).

The Farama Foundation is always looking for contributors to PettingZoo, the largest open source MARL library out there (https://github.com/Farama-Foundation/PettingZoo, farama.org). If you might be interested in that you can message them in their discord server

I am trying to implement a custom PettingZoo environment, and a shared policy with Stable Baselines 3. I am running into trouble with the action spaces not being compatible, since PettingZoo has started using gymnasium instead of gym. Does anyone know if these libraries no longer work together, and perhaps if there is a work-around?

I'd say this is a great path but I'd also look at the basic on-policy gradient actor critic methods like A2C and eventually PPO. Someone recommended SAC which also really good. There are tons of environments in the https://github.com/Farama-Foundation/PettingZoo as well if you want to mess with those. You can also check out stable baselines https://github.com/DLR-RM/stable-baselines3 which is pretty popular. If you want to get into the theory more I recommend reading the Sutton and Barto book on reinforcement learning.

Might want to check out petting zoo: https://github.com/Farama-Foundation/PettingZoo

Not that I know of, most I've seen are based on gym. I did see PettingZoo which is for multi-agent RL. https://github.com/Farama-Foundation/PettingZoo

Thank you for your comment. This is certainly helpful. I have since read more about this domain and turns out mathematically it is modeled using Markov/Stochastic game representations. I also found PettingZoo which can be used with RayLib for MARL problems.

This includes single-agent Gymnasium wrappers for DM Control, DM Lab, Behavior Suite, Arcade Learning Environment, OpenAI Gym V21 & V26. Multi-agent PettingZoo wrappers support DM Control Soccer, OpenSpiel and Melting Pot. For more information, read the release notes here:

They are talking about this: https://github.com/mgbellemare/Arcade-Learning-Environment

The code for Atari envs is not on gym, but on ALE

If you're more interested in playing games directly from pixel-level input, check out the Arcade Learning Environment, which lets you do this with all the old Atari games. You can find lots of tutorials online about using "reinforcement learning" to play these games.

I glanced over everything in this post, for a more detailed explainer check out the following blog post: https://brosa.ca/blog/ale-release-v0.7 and the release notes at https://github.com/mgbellemare/Arcade-Learning-Environment/releases/tag/v0.7.0.

I'm running some experiments on Atari via gym and have noticed that the MD5 checksums on around half of the ROMs supplied by gym[atari] differ from the MD5s listed here. Has anyone noticed this before, and would it make a difference to the results?