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imitation-learning
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Hi,
I've installed the library successfully and the basic commands in the readme work perfectly fine. My plan is to build my own custom MDP envs to use with AIRL. However, since my MDP is a discrete space one, I wanted to first test out the Cliffworld example given under imitation/envs/examples/model_envs.py. I'm trying to use the mce_irl.ipynb in experiments folder. However, it still imports t
In the README, could you provide the information on the observation/action spaces that are supported by each algorithm?
Specifically, do BC and GAIL implementations support image and dict observation spaces?
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