CS 285 is UC Berkeley's graduate course on deep reinforcement learning, taught by Sergey Levine, covering policy gradients, value-based and actor-critic methods, and model-based RL. Its slides, lectures, and assignments are publicly available.

Commenters call it extremely insightful and highly recommend it, and point out that the slides, lectures, and assignments are all public.

What commenters said (2)

1. Strongly recommendsthepablohansen2024in “Reinforcement Learning Kinda Works Now”

   In case this sparked anybody's interest, I went through the tweet's author's course on RL [1] recently and it was extremely insightful. I heartily recommend it (slides, lectures, assignments are all public)

   [1] https://rail.eecs.berkeley.edu/deeprlcourse/

2. Strongly recommendsblahblahblah102020in “Spinning Up in Deep RL”

   I would highly recommend Sergey Levine's course:

   http://rail.eecs.berkeley.edu/deeprlcourse/

   For a more mathematical treatment, there's a beautiful book by Puterman:

   https://www.amazon.com/Markov-Decision-Processes-Stochastic-...

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