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  • Reinforcement Learning, second edition: An Introduction (Adaptive Computation and Machine Learning series)

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Reinforcement Learning, second edition: An Introduction (Adaptive Computation and Machine Learning series)

by Richard S. Sutton (Author), Andrew G. Barto (Author) Format: Hardcover
4.6 out of 5 stars (636)
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The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence.

Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.

Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

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  1. ISBN-10
    0262039249
  2. ISBN-13
    978-0262039246
  3. Edition
    2nd
  4. Publisher
    Bradford Books
  5. Publication date
    November 13, 2018
  6. Language
    English
  7. Dimensions
    7.24 x 1.5 x 9.29 inches
  8. Print length
    552 pages
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Editorial Reviews

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.

Product details

  • Publisher ‏ : ‎ Bradford Books
  • Publication date ‏ : ‎ November 13, 2018
  • Edition ‏ : ‎ 2nd
  • Language ‏ : ‎ English
  • Print length ‏ : ‎ 552 pages
  • ISBN-10 ‏ : ‎ 0262039249
  • ISBN-13 ‏ : ‎ 978-0262039246
  • Item Weight ‏ : ‎ 2.64 pounds
  • Dimensions ‏ : ‎ 7.24 x 1.5 x 9.29 inches
  • Best Sellers Rank: #26,371 in Books (See Top 100 in Books)
  • Customer Reviews:
    4.6 out of 5 stars (636)

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Richard S. Sutton

Richard S. Sutton

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Customer reviews

4.6 out of 5 stars
636 global ratings

Customers say

Customers find this book to be an excellent resource for reinforcement learning, with rich examples and comprehensive coverage of new developments. The writing style receives positive feedback, and one customer notes it's particularly clear compared to other Python RL books. Customers disagree on the book's readability.
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37 customers mention content, 30 positive, 7 negative
Customers find the book to be an excellent resource for reinforcement learning, explaining concepts adequately and serving as the best reference material in the field.
good bookRead more
Amazing book, the bible of RLRead more
Great book, it covers the fundamentals in such a way you can construct your algorithms.Read more
Phenomenal book (so far), was nervous about the reviews about low-quality editions but decided to try anyways....Read more
12 customers mention depth of content, 12 positive, 0 negative
Customers appreciate the depth of content in the book, which includes rich examples and comprehensive coverage of new developments in reinforcement learning.
The perfect mix of theory and examples to learn one of the most fascinating areas of modern AI.Read more
...In-depth but explained well. There's a reason this is the gold standard for reinforcement learning texts.Read more
...This second edition brings everything up to date. It reminds me why I love RL so much.Read more
...It lays the foundation of reinforcement learning and ventures into advanced material. Matching for students or ones with academic background....Read more
6 customers mention writing style, 5 positive, 1 negative
Customers appreciate the writing style of the book.
It is the classic text and it was much clearer than all the python rl books and individual papers.Read more
...So, be careful. The book is well written and it is very helpful to establish understanding of RL....Read more
...It's an excellent text, and also something you'll want in your reference library.Read more
...deals with the fundamentals of RL in a simplified setting, is carefully worded in order to convey understanding in the same accessible, intuitive...Read more
9 customers mention readability, 5 positive, 4 negative
Customers have mixed opinions about the book's readability, with some finding it clear, while one customer notes that chapter 13 is completely unreadable.
Sutton has a free PDF online but I like the readability and quality of the print edition....Read more
I like the book. It explains things adequately. The representations take a little bit to digest....Read more
...of the book is fine; text and images are crisp, making it easy to read....Read more
...It is formatted terribaly so none of the formulea can be read.Read more
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