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Reinforcement Learning: An Introduction
(Adaptive Computation and Machine Learning)
by
Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach ...more
Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach ...more
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Start your review of Reinforcement Learning: An Introduction
Takeouts: thoughts on
- Mone-Karlo methods,
- dynamic programming,
- learning from temporal differences.
- Mone-Karlo methods,
- dynamic programming,
- learning from temporal differences.
Jan 21, 2014
Jon Gauthier
marked it as read-part
Despite its age, this book is still the canonical introduction to reinforcement learning.
I'm reading parts as necessary — not sure if I'll ever read cover-to-cover. In any case this has been an indispensable resource in my research career.
From the outside, RL seems mathy and somewhat stilted; from the inside, there is a lot of room for creativity and the core concepts are quite straightforward. I credit this book (along with some incredibly talented mentors) for introducing me to that beautiful ...more
I'm reading parts as necessary — not sure if I'll ever read cover-to-cover. In any case this has been an indispensable resource in my research career.
From the outside, RL seems mathy and somewhat stilted; from the inside, there is a lot of room for creativity and the core concepts are quite straightforward. I credit this book (along with some incredibly talented mentors) for introducing me to that beautiful ...more
Really good textbook.
I was surprised about how well we understand much of RL. Coming from ML this was a welcome novelty.
Although, I would have like to see a few more of the proofs and for there to be exercises.
I was surprised about how well we understand much of RL. Coming from ML this was a welcome novelty.
Although, I would have like to see a few more of the proofs and for there to be exercises.
One of the best book I ever read. A big step toward AI. The book inspired me to dig deeper.
A good supplementary would be an online course by Sutton's student and a former lead at Deepmind David Silver.
A good supplementary would be an online course by Sutton's student and a former lead at Deepmind David Silver.
The book I spent my Christmas holidays with was Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto. The authors are considered the founding fathers of the field. And the book is an often-referred textbook and part of the basic reading list for AI researchers. Given my own interest and fledgling attempts in the area (I trained my first models in 2017), I thought worthwhile to spend some time learning some basics.
Reinforcement learning is one of the hottest fields in ...more
Reinforcement learning is one of the hottest fields in ...more
Great book explaining the basic concepts of reinforcement learning. Parts I and II are very well explained. Part III I didn't like much but still quite informative, seems to be oriented for future research.
I' not finished this book but already want to leave a review. This is a very readable and still rigorous description of reinforcement learning. The main difference between this book and many others in the field of machine learning is that the author really tries to make his work approachable by others. Reading this is a joy, highly recommended.
For me, this is one of the best books on AI. Even though the material is not that simple, everything is clearly explained and the book is comprehensible even for people who are not familiar with the concepts. Even though this is an older book, it is still the best I've seen on the topic.
Concise introduction to the field that is fueling the development of autonomous agents.
A little dated, but in terms of learning the basics without a whole lot of digging, this is probably the best book out there. If you are thinking about getting into RL, I would recommend reading this first, then maybe Decision Making Under Uncertainty, reading some papers, reading the white paper on OpenAI's gym, and then messing around with gym. Sutton gives some excellent resources for understanding the history of RL and the maths behind it all, and if you have the time, it's worth reading all
...more
I only had enough knowledge to follow this book up until about chapter 10. Even so, so far one of my favourite books on machine learning. Clear, well-described problems within well-structured chapters, which build on each other in a logical way. Some folks have a working directory of the most illustrative problems from the book here: https://github.com/ShangtongZhang/rei...
worth re-reading.
great illustration on fundamental conceptual ideas.
needs some time to internalize all the methods and tricks about RL.
once you really got the idea, reinforcement learning becomes very intuitive.
yep, that's the most sensible way to build an automatic learning/optimizing robot.
great illustration on fundamental conceptual ideas.
needs some time to internalize all the methods and tricks about RL.
once you really got the idea, reinforcement learning becomes very intuitive.
yep, that's the most sensible way to build an automatic learning/optimizing robot.
Since its arrival it has been considered the bible for reinforcement learning. Sutton and Barto explain everything very well. I recommend this book to everyone who wants to start in the field of reinforcement learning. I do have to say that the first edition is missing some new developments, but a second edition is on the way (free pdf can be found online).
"Also related to TD learning are Holland's (1975, 1976) early ideas about consistency among value predictions. These influenced one of the authors (Barto), who was a graduate student from 1970 to 1975 at the University of Michigan, where Holland was teaching. Holland's ideas led to a number of TD-related systems..."
Really natural progression and base covreage along the book, as you develpe from the broad to central, to the perticular, to the future.
Really recommend it to everyone who wants to build a knowledge around reinforcment lrearning and have a strong clear foundations.
Really recommend it to everyone who wants to build a knowledge around reinforcment lrearning and have a strong clear foundations.
Read the 2017 draft in parts. Provides rigorous descriptions of RL algorithms in a readable manner. One of my favorite books on Machine Learning.
I recommend anyone interested in learning RL to start with this and then moving into papers.
I recommend anyone interested in learning RL to start with this and then moving into papers.
Excellent introduction to reinforcement learning. Well written, and does a good job of walking the reader through the algorithms and building a depth and breadth of knowledge. A lot of interesting examples included as well.
Best reference book for Reinforcement Learning
I used the first edition of this book as one of the key reference for my graduation thesis back in the 90s. This second edition brings everything up to date. It reminds me why I love RL so much.
I used the first edition of this book as one of the key reference for my graduation thesis back in the 90s. This second edition brings everything up to date. It reminds me why I love RL so much.
Constantly building from simple algorithms to more complex ones and their variations. A beautiful taxonomy of RL is built. The examples are simple enough to try oneself and yet complex enough to distinguish the different approaches. Highly recommend.
Second edition, in progress
****Draft****
Richard S. Sutton and Andrew G. Barto c 2014, 2015, 2016, 2017
Skipped Chap 14 - 16 (Use Case) ...more
****Draft****
Richard S. Sutton and Andrew G. Barto c 2014, 2015, 2016, 2017
Skipped Chap 14 - 16 (Use Case) ...more
Done 4 chapters. I already feel like a Ninja! Damn!
The absolute bible from the RL guru. RL beautifully explained. Heavy stuff, full of maths as you would expect from a textbook, but still quite approachable.
Excellent book for beginners on Reinforcement learning. The language may be little verbose but the examples are sufficient to grasp the concept.
The only RL textbook, and lucky for me it got its first update in 20 years just as I began.
Reinforcement Learning Introduction
I saw this citation pop up a few times in some recent reinforcement/decision-making literature and I figured it was about time to read about one of the computational methods that has influenced how neurobiologists have framed decision-making. My background is predominantly in behavioral neuroscience and I have some background in psychology and cellular and development biology. I got through the first section "The Problem" with ease, but getting through the second has been challenging solely
...more
The two editions ought to be listed as two different books: the second edition is 200 pages longer, and was almost entirely rewritten.
The first edition (1998) was a classic and a masterpiece: it's about as engaging and entertaining as a textbook on AI can be, and that's in no small part because the prose is precise and leads to an intuitive grasp of the maths.
The second edition (2018) is worthy of the first, but integrates 20 years of progress in Reinforcement Learning. As a result the ...more
The first edition (1998) was a classic and a masterpiece: it's about as engaging and entertaining as a textbook on AI can be, and that's in no small part because the prose is precise and leads to an intuitive grasp of the maths.
The second edition (2018) is worthy of the first, but integrates 20 years of progress in Reinforcement Learning. As a result the ...more
I read the Second Edition (which is still in progress) and although I haven't read another book on reinforcement learning, I think this is a good one. However, one problem is that there are a lot of incomplete chapters, specially in the advanced topics. I recommend seeing the videos from the RL course by David Silver while reading this book, it helps a lot to understand many of the concepts there.
An excellent introduction to reinforcement learning. Clearly written and concise, it provides excellent examples of problems that reinforcement learning can solve, and provides the reader with a way to think about how to solve them.
This book is amazing. And there is a free draft of the 2nd edition online. Not only that it provides a lot of technical details needed to fully understand the matter, it also includes some very insightful links from RL to other sciences, e.g. Psychology. Recommended to people in CS.
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