No course that I know of exists in RL like fast.ai is for DL.
There is no course that closely knits proper theory (I can still hear Jeremy saying, “This is not a version of gradient descent, this is gradient descent”) with hands-on practice and make learners ready to tackle serious problems as fast.ai does. So, what path would you suggest for someone wanting to learn RL, “the fast.ai way”?
So, I am looking for resources, that teach you the fundamentals of RL and also teach you how to get your hands dirty with RL experimentations (with available options such as Spinning Up) and plotting graphs?
NB: I have tried the University of Alberta’s RL Specialization on Coursera (and plan to finish it). But it, to me, seems like a video version of Sutton and Barto’s book w/ some added case studies.