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Reinforcement Learning: An Introduction (2nd Edition), by Sutton and Barto.

You can find a link to the online version of the book here

I am following this book as part of a self-learning exercise, which will later be useful in one of my courses. I have used three guides when writing the code that is found here, and going through the exercises found in the book.

Solutions to theoretical exercises by John Weatherwax can be found here

Solutions to theoretical, and some programming exercises can be found in this GitHub repo

Guides on how to create the figures/examples found in the book can be found in this GitHub repo

Please note that in some chapters I have multiple solutions to the same problem. This is as I follow the solutions linked above. For practice and undestanding, I find this method to be good for aquiring good coding practices in ML.