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[question] Model Based RL with Stable Baselines?

See original GitHub issue

I would like to do some model based RL with stable baselines. From what I’ve read, it seems that to do this, I just have to train a model f to predict the next state, given the current state and action, i.e. s{t+1} <- f(s{t}, a{t}). I want to do this so that the RL can plan several steps into the future.

Anyways, I need access to the obs and actions from the learn method in PPO2. Basically, after each rollout, I want to use the data collected to train f in a supervised manner. I am trying to use a callback to do this. Is this possible?

Thanks!

Issue Analytics

State:
Created 3 years ago
Comments:11

Top GitHub Comments

1reaction

araffincommented, Jul 6, 2020

Thanks again for commenting. In case anyone wants to use the new patch, you can use !pip install git+https://github.com/hill-a/stable-baselines.git

this is actually already in the documentation 😉 https://stable-baselines.readthedocs.io/en/master/guide/install.html#bleeding-edge-version

1reaction

PartiallyTypedcommented, Jul 6, 2020

It’s not on pypi, the patch #787 was completed recently (^^’).

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