Reinforcement Learning
An Introduction
by Richard S. Sutton · first published 1992
60% recommend40% neutral
Why HN recommends it
Introduction to reinforcement learning by two founders of the field, free online. Covers foundations from multi-armed bandits to temporal difference learning, with a notation table; suits ML students and engineers.
Summarised by Claude from recommending comments
Reinforcement learning is the learning of a mapping from situations to actions so as to maximize a scalar reward or reinforcement signal. The learner is not told which action to take, as in most forms of machine learning, but instead must discover which actions yield the highest reward by trying them. In the most interesting and challenging cases, actions may affect not only the immediate reward, but also the next situation, and through that all subsequent rewards. These two characteristics -- trial-and-error search and delayed reward -- are the most important distinguishing features of reinfo
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…hands down these are some of the best related textbooks: - [0] Pattern Recognition and Machine Learning (Information Science and Statistics) and also: - [1] The Elements of Statistical Learning - [2] Reinforcement Learning: An Introduction by Barto and Sutton - [3] The Deep Learning by Aaron Courville, Ian Goodfellow, and Yoshua Bengio - [4] Neural Network Methods for Natural Language Processing (Synthesis Lectures on…
on “Ask HN: What are the best textbooks in your field of expertise?” ↗…Multilevel/Hierarchical Models (Gelman & Hill): https://www.amazon.ca/gp/product/052168689X/ - Statistical and Data Analysis for Financial Engineering (Ruppert & Matteson): https://www.amazon.ca/gp/product/1493926136/ - Reinforcement Learning (Sutton & Barto): https://www.amazon.ca/Reinforcement-Learning-Introduction-Ri... - Deep Learning (Godfellow, Bengio & Courville): https://www.amazon.ca/Deep-Learning-Ian-Goodfellow/dp/026203... - Networks…
on “Ask HN: What do you read for general knowledge?” ↗…surpassed all previous computer backgammon programs. It stopped improving after about 1,500,000 games (self-play) using 80 hidden units. For others: Richard Sutton, one of the pioneers of TD makes his Reinforcement Learning: An Introduction textbook available for free on his website: http://incompleteideas.net/sutton/ (MIT Press also links to it)
on “AlphaGo Zero: Learning from scratch” ↗Not sure if the authors will read this or not but I beg of you, please put a table of notation in the forward. The Sutton and Barto Reinforcement Learning book did that for basically every notation that wasn't basic algebra and it's been extremely helpful. Just labeling things I had never seen before, like indicator functions, was extremely valuable. Especially…
on “Book: Mathematics for Machine Learning” ↗Consider reading "Reinforcement Learning, an Introduction" by Rich Sutton[0]. It is very accessible (and probably the most used textbook in the field), and goes into detail about the connection between RL and MDPs. If you want a high-level…
on “The Markov Property, Chain, Reward Process and Decision Process” ↗Like others have said, this seems to be a generalization of the problem of reinforcement learning. For a good introduction to the subject, check out Reinforcement Learning by Sutton & Barto [1]. After reading the first few chapters, you'll be able to understand most of that equation. [1] http://webdocs.cs.ualberta.ca/~sutton/book/the-book.html
on “To create a super-intelligent machine, start with an equation” ↗…(bias vs variance, bayes theory etc.) and the lector is simply the best I've seen. Following that Ian Goodfellow book http://www.deeplearningbook.org/ is very good. If you want to learn foundations of Reinforcement Learning I'd recommend Sutton & Barto https://webdocs.cs.ualberta.ca/~sutton/book/ebook/the-book.h...
on “How to Start Learning Deep Learning” ↗…Neural Networks for Pattern Recognition ( Bishop). At this point you are in good shape to read any papers in NN. My reccomendations - anything by Yann LeCun and Geoffrey Hinton. Both do amazing research. Reinforcement Learning (s) Reinforcement Learning - An Introduction by Barto and Sutton (follow up with "Recent Advances In reinforcement Learning" (PDF) which is an old paper but a GREAT introduction to Hierarchical Reinforcement…
on “Learning About Statistical Learning” ↗…Intelligence: A Modern Approach"[0]. If you decide to look into deep learning, I've heard good thing about Bengio's book (see also Hugo Larochelle's lectures on YouTube) and if you want to learn more about reinforcement learning, "Reinforcement Learning: An Introduction" by Sutton and Barto is frankly amazing (and there's a great course on YouTube by David Silver). From reading your code, you're probably familiar with most of…
on “Show HN: Improved Seam Carving Using Forward Energy” ↗If you later want to drill down more into reinforcement learning specifically, check out "Reinforcement Learning: An Introduction" by Sutton & Barto, full version available online in HTML form: http://webdocs.cs.ualberta.ca/~sutton/book/the-book.html
on “Machine learning toolkit” ↗I tend to get most of my CS content from papers & web pages, but Sutton & Barto's Reinforcement Learning book is pretty good: https://webdocs.cs.ualberta.ca/~sutton/book/the-book-2nd.htm...
on “Ask HN: What's the best computer science book you've read recently?” ↗…http://www.inference.phy.cam.ac.uk/itila/p0.html Bayesian Reasoning & Machine Learning (Barber, 2012) http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=... Deep Learning (Goodfellowet al., 2016) http://www.deeplearningbook.org/ Reinforcement Learning: An Introduction (Sutton & Barto, 1998) http://webdocs.cs.ualberta.ca/~sutton/book/ebook/the-book.ht... ^^ the above books are used on many graduate courses in machine learning and are varied in their…
on “Ask HN: What is the right way to self-learn Machine learning?” ↗…and want to understand how modern AI really works, the two best books/shortcuts to go from beginner to expert are: - Ian Goodfellow's "Deep Learning" http://www.deeplearningbook.org/ - Richard Sutton's "Reinforcement Learning: An Introduction" http://incompleteideas.net/sutton/book/
on “Ask HN: Best books on AI” ↗…references and collect many insights & results that you'd otherwise have to trawl the literature for. I'm very interested to see what a book focused more narrowly on RL will be like-- Sutton's Introduction to Reinforcement Learning[0] is fantastic, but if you're going to do research on RL, another text such as this one is necessary. 0. http://www.incompleteideas.net/book/the-book-2nd.html
on “Reinforcement Learning and Optimal Control” ↗And if, after reading Montague and Glimcher, people are interested, they can follow up with deeper dives into some relevant topics, like: * Reinforcement Learning: An Introduction by Sutton and Barto (required reading in our lab, but its computer science book) * Decision Neuroscience: An Integrative Perspective by Jean-Claude Dreher and Leon Tremblay
on “Must-read psychology books” ↗