PettingZoo
Shimmy
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| PettingZoo | Shimmy | |
|---|---|---|
| 19 | 1 | |
| 2,370 | 114 | |
| 4.1% | 3.5% | |
| 8.8 | 6.2 | |
| 6 days ago | about 1 month ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
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PettingZoo
- CartPole equivalent enviroments in MARL?
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[P] PettingZoo 1.24.0 has been released (including Stable-Baselines3 tutorials)
Release notes: https://github.com/Farama-Foundation/PettingZoo/releases/tag/1.24.0
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Shimmy 1.0: Gymnasium & PettingZoo bindings for popular external RL environments
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.
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Petting Zoo Classic Environment
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).
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Ideas for MARL project?
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
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Is Stable Baselines 3 no longer compatible with PettingZoo?
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?
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New to reinforcement learning.
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.
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[Stable Baselines3] How do I train 3 model simultaneously?
Might want to check out petting zoo: https://github.com/Farama-Foundation/PettingZoo
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Are there any other high-quality pre-built Python training environments other than Open AI's GYM?
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
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Training with multiple agents.
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.
Shimmy
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Shimmy 1.0: Gymnasium & PettingZoo bindings for popular external RL environments
Check out our blog post or release notes for more details. Our documentation website at https://shimmy.farama.org/, containing an overview of each environment with screenshots, installation instructions, full usage scripts, and API information.
What are some alternatives?
open_spiel - OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
lab - A customisable 3D platform for agent-based AI research
gym-macm - 2D, physics based gym environment for multi-agent combat & movement tasks
bsuite - bsuite is a collection of carefully-designed experiments that investigate core capabilities of a reinforcement learning (RL) agent
SuperSuit - A collection of wrappers for Gymnasium and PettingZoo environments (being merged into gymnasium.wrappers and pettingzoo.wrappers
meltingpot - A suite of test scenarios for multi-agent reinforcement learning.
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
gym - A toolkit for developing and comparing reinforcement learning algorithms.
dm_control - Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.
Mava - 🦁 A research-friendly codebase for fast experimentation of multi-agent reinforcement learning in JAX
Arcade-Learning-Environment - The Arcade Learning Environment (ALE) -- a platform for AI research.