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Comment by subatomic | Hacker News Reader

I was heavily into reinforcement learning around the turn of the century, and at the time, "Reinforcement Learning - An introduction" (Barto and Sutton) https://mitpress.mit.edu/books/reinforcement-learning was an absolute goldmine for me getting started. I think parts of it are online somewhere including all their pseudocode and solutions.

https://mitpress.mit.edu/books/reinforcement-learning

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The complete first edition can be found here: http://incompleteideas.net/book/ebook/the-book.html

If you're interested in some well documented C++ implementations of the algorithms shown in the book, feel free to check out https://github.com/Svalorzen/AI-Toolbox. I started the project because when I was first reading the book I had no reference implementation to compare the book to, and personally I learn better with practical examples, so maybe it can help you too.

If you are going to start in RL, you should really consider reading the second edition even though it is not released yet. I am guessing that Sutton is getting closer to the finishing line as there have been numerous revisions already. The second edition has better notation and benefits from the field having matured a lot since the first book was written. http://incompleteideas.net/book/the-book-2nd.html

It's a fantastic book! The authors have been working on a second edition for a few years, and I think it's finally finished. A draft is generously available here: http://incompleteideas.net/book/the-book-2nd.html

As a self contained, foundational course, Georgia Tech's OMSCS offering [1] is solid. Charles Isbell and Michael Littman are great at building intuition into equations.

[1] https://www.udacity.com/course/reinforcement-learning--ud600

Isbell's course in person was great. And if the exams for the online version are anything like the in person ones, it really does test your understanding of foundational concepts.

Yup, just took the online RL class and the average grade for the final exam was 45 out of 100, high score of 76. The format was true/false with a short explanation for your answer. I never thought I'd be proud about getting a 53 on a true/false exam, but it was an extremely challenging and rewarding class.

I was wondering how you were over 120 years old there, for a moment, then I realized turn of the century doesn't mean that century any more.

Bartow & Sutton is excellent.

You can definitely find it online but be sure to find the right version - the latest version has great illustrations and is a lot clearer.

Also, check out the RL jupyter notebook here by my friend Ryan Sweke who does work on RL for quantum computing: https://github.com/R-Sweke/CrashCourseInNeuralNetworksWithKe...

Great suggestion! The blog was based on a large portion of the book. A friend of mine asked for a version of the first chapter that was digestible for an audience that is in high-school to undergrad college level. I wrote this blog with that in consideration, while adding my own observations as well. I am planning to write up some python solutions for the MDP chapter as well. Thanks for reading :)

Thanks for this, I have read a couple books on deep learning but struggled to find anything on Reinforcement Learning. Maybe an Ask HN is in order.

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