Reinforcement Learning
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 reinforcement learning. Reinforcement learning is both a new and a very old topic in AI. The term appears to have been coined by Minsk (1961), and independently in control theory by Walz and Fu (1965). The earliest machine learning research now viewed as directly relevant was Samuel's (1959) checker player, which used temporal-difference learning to manage delayed reward much as it is used today. Of course learning and reinforcement have been studied in psychology for almost a century, and that work has had a very strong impact on the AI/engineering work. One could in fact consider all of reinforcement learning to be simply the reverse engineering of certain psychological learning processes (e.g. operant conditioning and secondary reinforcement). Reinforcement Learning is an edited volume of original research, comprising seven invited contributions by leading researchers.
Stats for nerds
18recommend
34mentions
30people
2010–2026years
59% recommend41% neutral
Mentions per year
What Hacker News says
- Reinforcement Learning by Sutton & Barto is an excellent introduction by two of the founders of the field.
Read here: http://incompleteideas.net/book/the-book-2nd.html
- Don't have a book but here's some quick thoughts: 1. Biographies on, e.g., Chinese pianist Lang Lang. When he was ~9 he 'retired' (it's an extreme case but telling, can recommend). 2. If you want formal/mathematical/CS perspective, study Reinforcement Learning (e.g. Rick Sutton).
- Huge congratulations to Andrew Barto and Richard Sutton on the well-deserved Turing Award! as a student, their textbook Reinforcement Learning: An Introduction was my gateway into the field. I still remembered that how Chapter 6 on ‘Temporal Difference Learning’ fundamentally reshaped the way I thought about sequential decision-making.
a timeless classic that I still highly recommend reading today!
- I'd also like to mention the classic book "Reinforcement Learning" by Sutton & Barto, which goes into some relevant mathematical aspects for choosing the "best" among a set of options. They have a full link of the PDF for free on their website [1]. Chapter 2 on "Multi-Armed Bandits" is where to start.
- Reinforcement Learning, second edition: An Introduction by Richard S. Sutton & Andrew G. Barto is like one of the, if not the, best book to learn reinforcement learning. I really enjoyed it!
- I spend a lot of time sniffing them out.
One thing I'll do once in a while is visit a university bookstore to get a sense of what's being used in classrooms. I ended up with a lot of my law textbooks like that. In law, I've really loved:
- Canadian Administrative Law (Guy Régimbald): https://store.lexisnexis.ca/en/categories/shop-by-jurisdicti...
- Tort Law (Horsey & Rackley): https://www.amazon.ca/Tort-Law-Kirsty-Horsey-dp-019886776X/d...
- Handbook of Canadian Higher Education Law (Shanahan, Nilso & Broshko): https://www.amazon.ca/Handbook-Canadian-Higher-Education-Law...
I've been on a maths, stats and ML/AI binge in the last year, and so far my top picks are:
- Calculus (Stewart): https://www.amazon.ca/Calculus-James-Stewart/dp/1337624187/
- Linear Algebra Done Right (Sheldon Axler): https://www.amazon.ca/Linear-Algebra-Right-Undergraduate-Mat...
- An Illustrative Guide to Multivariable and Vector Calculus (Miklavcic): https://www.amazon.ca/gp/product/3030334589/
- Probability: A Graduate Course (Allan Gut): https://www.amazon.ca/gp/product/1461447070/
- Statistical Rethinking (McElreath): https://www.amazon.ca/gp/product/036713991X/
- An Introduction to Statistical Learning (James, Witten, Hastie & Tibshirani): https://www.amazon.ca/Introduction-Statistical-Learning-Appl...
- Regression and Other Stories (Gelman, Hill & Vehtari): https://www.amazon.ca/gp/product/1107676517/
- Data Analysis Using Regression and 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 (Mark Newman): https://www.amazon.ca/Networks-Mark-Newman/dp/0198805098/
I have a ton of textbooks in my wishlist that seem noteworthy and interesting, but that I haven't gotten the chance to read yet: no promises, but you might find something in there https://www.amazon.ca/hz/wishlist/ls/1FWW046Q3RWWM
My area of expertise is biochemistry, life science, immunology, cell bio... So if anyone wants good recommendations in these topics I have plenty! :)
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