My recommendations on how to start learning about RL in 2020
Since I've been asked this a few times now, I figured I might as well turn it into a blog post to link people towards it.
To understand perspective: I've now been reading about various learning algorithms for 3+ years in my PhD at EPFL, and actively working on RL research for about a year now. Before that I was working as an ML freelancer and during that time I've given a few workshops trying to vulgarize ML.
My recommended progression would be
- Read A (long) peek into Reinforcement Learning to get started, but don't get hung up on the details
- Then, concurrently, play with SpinningUp and follow the references to get into Deep RL and read Reinforcement Learning which focuses more on the pre-Deep Learning RL methods and theory. You might also want to check out Stable Baselines, Berkeleys rlpyt and the other RL libraries as references.
- Then, once you are actually getting into research reading the notes from CS598, the draft of RL Theory and maybe the excellent Algorithms for Reinforcement Learning are something to keep close for references and deeper understanding. Lillian Wengs excellent blog is also something to keep revisiting.
Some other links (might update this in the future) are: