Reinforcement Learning: An Introduction Hardcover – Illustrated, 13 November 2018
by
Richard S. Sutton
(Author),
Andrew G. Barto
(Author)
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Product details
- Language : English
- Hardcover : 552 pages
- ISBN-10 : 0262039249
- ISBN-13 : 978-0262039246
- Reading age : 18 years and up
-
Best Sellers Rank:
7,875 in Books (See Top 100 in Books)
- 83 in Computer Science
- 163 in Graphics & Multimedia Software
- Customer reviews:
Product description
About the Author
Richard S. Sutton is Professor of Computing Science and AITF Chair in Reinforcement Learning and Artificial Intelligence at the University of Alberta, and also Distinguished Research Scientist at DeepMind.
Andrew G. Barto is Professor Emeritus in the College of Computer and Information Sciences at the University of Massachusetts Amherst.
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Most helpful customer reviews on Amazon.com
Amazon.com:
3.8 out of 5 stars
37 reviews

Taewan Kim
1.0 out of 5 stars
Very low print quality.
29 January 2019 -
Published on Amazon.comVerified Purchase
I know that this book is a bible of RL. However, the print quality is extremely low. For example, all figures in color are blurry and faint as if the book was printed with low toners/inks. I've already tried "replace order" to fix the book's bad hardcover quality, but looks like that wasn't the only problem. Unfortunately, now I only have a single option to return with a $0.00 value because this book is a replacement of my original order. Curious whether it's a problem of MIT Press or someone else...

1.0 out of 5 stars
Very low print quality.
Reviewed in the United States on 29 January 2019
I know that this book is a bible of RL. However, the print quality is extremely low. For example, all figures in color are blurry and faint as if the book was printed with low toners/inks. I've already tried "replace order" to fix the book's bad hardcover quality, but looks like that wasn't the only problem. Unfortunately, now I only have a single option to return with a $0.00 value because this book is a replacement of my original order. Curious whether it's a problem of MIT Press or someone else...
Reviewed in the United States on 29 January 2019
Images in this review


65 people found this helpful

impersonal
5.0 out of 5 stars
Essential intuitions about Reinforcement Learning
29 November 2018 -
Published on Amazon.comVerified Purchase
I read the entire book cover to cover, doing every (non-programming) exercise, as part of a PhD involving RL. I was also familiar with the first edition (1998).
The 2nd edition (2018) has been entirely reworked; it is much longer, the structure has changed, the notation has changed, many new topics are discussed. Same as the first edition, the second edition is both a reference book and a pedagogic manual. The main differences are:
(1) Part I, which deals with the fundamentals of RL in a simplified setting, is carefully worded in order to convey understanding in the same accessible, intuitive manner as in the first edition, but also paying closer attention to mathematical rigor and systematicity.
(2) Part II, which deals with RL with function approximation, is at times somewhat involved mathematically compared to the first edition, reflecting the general evolution of the field of Machine Learning. The most difficult sections are clearly marked as such, and can be skipped. But taking on the difficulties straight on is always (positively) rewarding, in the end.
(3) Part III deals with related developments in psychology and neuroscience, which are a welcome addition to a field that has branched across disciplines. This should be useful to neuroscientists and computational neuroscientists, as well as to experimental/cognitive psychologists.
Many exercises are included. I am no math buff and found that I could do every single one of them on my own, which is unfortunately not usually the case in other A.I. books. These exercises serve to help the reader understand key issues by working them out for themselves, in a guided manner; they make self-study possible and enjoyable.
This is an indispensable book for anybody working in RL. My only issue is that I'm too late to be referenced in it, but I haven't lost hope of making it in the 3rd edition, which is expected in 2038.
The 2nd edition (2018) has been entirely reworked; it is much longer, the structure has changed, the notation has changed, many new topics are discussed. Same as the first edition, the second edition is both a reference book and a pedagogic manual. The main differences are:
(1) Part I, which deals with the fundamentals of RL in a simplified setting, is carefully worded in order to convey understanding in the same accessible, intuitive manner as in the first edition, but also paying closer attention to mathematical rigor and systematicity.
(2) Part II, which deals with RL with function approximation, is at times somewhat involved mathematically compared to the first edition, reflecting the general evolution of the field of Machine Learning. The most difficult sections are clearly marked as such, and can be skipped. But taking on the difficulties straight on is always (positively) rewarding, in the end.
(3) Part III deals with related developments in psychology and neuroscience, which are a welcome addition to a field that has branched across disciplines. This should be useful to neuroscientists and computational neuroscientists, as well as to experimental/cognitive psychologists.
Many exercises are included. I am no math buff and found that I could do every single one of them on my own, which is unfortunately not usually the case in other A.I. books. These exercises serve to help the reader understand key issues by working them out for themselves, in a guided manner; they make self-study possible and enjoyable.
This is an indispensable book for anybody working in RL. My only issue is that I'm too late to be referenced in it, but I haven't lost hope of making it in the 3rd edition, which is expected in 2038.
44 people found this helpful

N Howe
1.0 out of 5 stars
Poor Quality Fake
31 January 2019 -
Published on Amazon.comVerified Purchase
This book would barely pass as an EE version of a textbook. At $57, the quality is utterly unacceptable. The spine feels like it's made of cheap cardboard and is not straight not does it cover all the pages. The covers are shorter than the pages. The spine bends over backwards when I pick up the book. The pages are not straight. The print quality is extremely low. I don't know how this happened but I am returning asap and will try to find an official copy of this book.

1.0 out of 5 stars
Poor Quality Fake
Reviewed in the United States on 31 January 2019
This book would barely pass as an EE version of a textbook. At $57, the quality is utterly unacceptable. The spine feels like it's made of cheap cardboard and is not straight not does it cover all the pages. The covers are shorter than the pages. The spine bends over backwards when I pick up the book. The pages are not straight. The print quality is extremely low. I don't know how this happened but I am returning asap and will try to find an official copy of this book.
Reviewed in the United States on 31 January 2019
Images in this review







39 people found this helpful

Sergey S. Tambovskiy
1.0 out of 5 stars
Low quality print
2 February 2019 -
Published on Amazon.comVerified Purchase
Compared to Kindle version, printed version:
1. Contains figures of low-resolution with blurred colors: ex. in pages 29, 396 335, 422, 428, etc.
2. Thin & flimsy spine of the book
3. Pages contain paper defects
4. Looks like a fake print, definitely not from MIT Press
1. Contains figures of low-resolution with blurred colors: ex. in pages 29, 396 335, 422, 428, etc.
2. Thin & flimsy spine of the book
3. Pages contain paper defects
4. Looks like a fake print, definitely not from MIT Press

1.0 out of 5 stars
Low quality print
Reviewed in the United States on 2 February 2019
Compared to Kindle version, printed version:Reviewed in the United States on 2 February 2019
1. Contains figures of low-resolution with blurred colors: ex. in pages 29, 396 335, 422, 428, etc.
2. Thin & flimsy spine of the book
3. Pages contain paper defects
4. Looks like a fake print, definitely not from MIT Press
Images in this review









37 people found this helpful

Amazon Customer
1.0 out of 5 stars
Really bad
27 February 2019 -
Published on Amazon.comVerified Purchase
Book quality is so low, chapter 3 and 4 are repeated twice and only first 7 page of each chapter is in the book.

1.0 out of 5 stars
Really bad
Reviewed in the United States on 27 February 2019
Book quality is so low, chapter 3 and 4 are repeated twice and only first 7 page of each chapter is in the book.
Reviewed in the United States on 27 February 2019
Images in this review



26 people found this helpful