+1 to Sutton & Barto. There is a second edition of their book available; see a link at https://webdocs.cs.ualberta.ca/~sutton/book/the-book.html https://webdocs

+1 to Sutton & Barto. There is a second edition of their book available; see a link at https://webdocs.cs.ualberta.ca/~sutton/book/the-book.html https://webdocs.cs.ualberta.ca/~sutton/book/the-book.html. https://www.udacity.com/course/reinforcement-learning--ud600 https://www.udacity.com/course/reinforcement-learning--ud600 is also good; it is not super-modern, but it is easily accessible, and it covers a large part of classical RL - this helps if you want to read recent RL papers because terminology and ideas become more familiar. I'm not sure Andrey Karpaty's blog post (linked in the article) is a good intro to RL - it starts with Policy Gradient which is on a complex side of RL techniques spectrum. From other sources I've heard Policy Gradient is harder to get working on an arbitrary problem than e.g. Q-Learning, but don't quote me on that :)