gym-multigrid
PettingZoo
| gym-multigrid | PettingZoo | |
|---|---|---|
| 1 | 19 | |
| 181 | 2,392 | |
| - | 3.0% | |
| 0.0 | 8.6 | |
| 8 months ago | 8 days ago | |
| Python | Python | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
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gym-multigrid
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INVALID_ARGUMENT: Received a label value of 8 which is outside the valid range of [0, 8). Label values: 8
So i am training an IPPO (Independent Proximal Policy Optimization) on the environment gym-multigrid, on the collect game (https://github.com/ArnaudFickinger/gym-multigrid). Actually i have 3 agents, each of them has its own actor and critic, and the actor has the following structure:
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.
What are some alternatives?
agents-aea - A framework for autonomous economic agent (AEA) development
open_spiel - OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
gym-macm - 2D, physics based gym environment for multi-agent combat & movement tasks
SuperSuit - A collection of wrappers for Gymnasium and PettingZoo environments (being merged into gymnasium.wrappers and pettingzoo.wrappers
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
gym - A toolkit for developing and comparing reinforcement learning algorithms.
Mava - 🦁 A research-friendly codebase for fast experimentation of multi-agent reinforcement learning in JAX
tmrl - Reinforcement Learning for real-time applications - host of the TrackMania Roborace League
lab - A customisable 3D platform for agent-based AI research
dm_control - Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
meltingpot - A suite of test scenarios for multi-agent reinforcement learning.