This tutorial is great, thanks for sharing, I have a question, there is an implicit assumption that the system dynamics is non-stochastic, except for the zero-mean noise disturbance e, does these observations / results about sample complexity hold for stochastic MDP dynamics as well?
Not quite. I am assuming you are referring to slide number 42/80 titled discrete MDPs. The sample complexity is modified by the number of iterations, which is not one for the case that you point out.
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Really clear! Best tutorial to talk about the relationship between optimal control and reinforcement learning I've ever seen!
Special thanks to the uploader. What a gem of a talk! The link on Brecht's webpage is not 720p (it is 360p), so this is a lifesaver.
Great upload! Thanks!
This tutorial is great, thanks for sharing, I have a question, there is an implicit assumption that the system dynamics is non-stochastic, except for the zero-mean noise disturbance e, does these observations / results about sample complexity hold for stochastic MDP dynamics as well?
Not quite. I am assuming you are referring to slide number 42/80 titled discrete MDPs. The sample complexity is modified by the number of iterations, which is not one for the case that you point out.
On 67/80, sounds something like receding horizon control.
sweet talk