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Lightweight multi-agent gridworld Gym environment

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Env.render() returns the observation of each agent in the environment. This observation is used to choose the best policy in q-learing. However the env.render() returns an array of arrays containing...

- Adds partial_obs param to CollectGameEnv and SoccerGameEnv - Replaces wrapped collect and soccer envs with unwrapped ones - Showcases the new interface in the test script

I am trying to train the agents in the multigrid environment using the torch-rl repository (https://github.com/lcswillems/rl-starter-files) which is compatible with gym-minigrid. After registering the new environment in gym (multigrid-collect-v0) and...

Hey! Thank you for making this extension to minigrid. Do you plan to add more multi-agent environments in the future such as pursuit evasion, warehouse environment or more environments from...