gym VS PettingZoo

A co-founder announced they disbanded their robots team a couple years ago: https://venturebeat.com/business/openai-disbands-its-robotic...

That was the same time they depreciated OpenAI Gym: https://github.com/openai/gym

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:

When I browse fetch_env.py, I have a question about the following code snippet:

Nvm, this works for me '!pip install setuptools==65.5.0' Source: https://github.com/openai/gym/issues/3176

how would this interact/compare with https://github.com/openai/gym?

If you'd like to learn more about reinforcement learning or play with a number of samples in controlled environments, I highly recommend you look at the documentation for OpenAI's Gym library and particularly the basic usage page. OpenAI's Gym provides a standardized environment for performing reinforcement learning on classic Atari games and a few other platforms and should be an educational resource. If you'd like a more detailed example, check out this tutorial on Paperspace's blog.

Frozen Lake is an OpenAI Gym environment in which an agent is rewarded for traversing a frozen surface from a start position to a goal position without falling through any perilous holes in the ice.

typically a more relaxed approach is taken via reinforcement learning, but it requires that you can simulate the game via a given gamestate. take a look at e.g. https://www.gymlibrary.dev/

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.