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 avatar commented on May 22, 2024

You can do this pretty straight-forwardly if you use one of the API interfaces for DeepMind Lab. Just build some interface that a human can use to navigate within the environment. At each step, your human agent will output an action vector (the 7-element ndarray detailed in the /docs directory); from the environment, you'll receive a state representation (with its contents based on how you initialized the environment, as you can supply the observation type) and a reward. All you need to do at that point is just store this information (and probably the time-step or just step) somewhere as you receive it- shouldn't have any timing issues here (unless you use a really slow storage method) as odds are you'll only have one process running at once since you've got a human agent navigating the environment.

Without knowing the specifics of your imitation learning/inverse RL/etc. model, I'd venture that this would be sufficient data for your model to learn from.

from lab.

tkoeppe avatar tkoeppe commented on May 22, 2024

knyte's answer sounds reasonable. You can use the API to create a human interface and also save a trace of actions and rewards.

from lab.

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