Pretty cool, this is actually a great reference for a lot of things. Even if you're familiar with RL, you might be reminded of something or learn something new.

Sutton and Barto's book is also good if you want to do more than dip your toes into RL: http://incompleteideas.net/book/the-book-2nd.html That page has the PDF with links to code, problem solutions, and course material.

If you just want an idea of what an RL algorithm looks like, I've got a (heavily) documented version of TD(λ) for linear function approximation here: https://github.com/rldotai/rl-algorithms/blob/master/py3/td....