Understanding multi agent learning in OpenAI gym and stable-baselines

• kaggle-environments

  55 273 6.6 Jupyter Notebook
  

The ks_env.train() method seems to be the one from kaggle_environments.Environment:

• stable-baselines3

  46 7,894 8.2 Python

  PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

  

Q1. However I got confused. Why does environment contains train() method? That is why environment should do training? I feel, train() should be part of the model: above article uses PPO algorithm which contains train() method. This PPO.train() gets called when we call PPO.learn() which makes sense.

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• stable-baselines

  10 4,000 0.0 Python

  A fork of OpenAI Baselines, implementations of reinforcement learning algorithms

I haven't read the code, but stable-baselines doesn't support multi-agent environments (https://github.com/hill-a/stable-baselines/issues/423), so I think they're trying to make learning multi-agent easier with Environment.train().

• SuperSuit

  4 430 8.0 Python

  A collection of wrappers for Gymnasium and PettingZoo environments (being merged into gymnasium.wrappers and pettingzoo.wrappers

  

Multi-agent isn’t supported by default in stable baselines, but you can make it work with PettingZoo. This example trains a single policy to control every agent in an environment (Parameter sharing). You could use these SuperSuit wrappers to work with other methods (self-play, independent learning, etc) but you would probably need to write some custom training code. https://github.com/PettingZoo-Team/SuperSuit#parallel-environment-vectorization

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