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Product Details
Series: Adaptive Computation and Machine Learning series
This is probably one of the best book I have read in the past 1 year. The authors present the subject with an excellent balance of mathematical, computational and intuitive material. The book also includes plenty of real-life examples to explain the concepts and motivations for the algorithms. The book starts with examples and intuitive introduction and definition of reinforcement learning. It follows with 3 chapters on the 3 fundamental approaches to reinforcement learning: Dynamic programming, Monte Carlo and Temporal Difference methods. Subsequent chapters build on these methods to generalize to a whole spectrum of solutions and algorithms. The book is very readable by average computer students. Possibly the only difficult one is chapter 8, which deals with some neural network concepts. I highly recommend this book to anyone who wants to learn about this subject.
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16 of 17 people found the following review helpful
I have this book more than a year now and I am going through it for the second time, so I think I have a pretty good picture about it. The book consists of three parts. In the first part, "The Problem", the authors define the scope of issues reinfocement learning is dealing with and they give some interesting introductory examples. Then, they move on to the concept of evaluative feedback and, eventually, define the reinforcement learning problem formally. The second part, "Elementary Solution Methods" consists of three more-less independent subparts: Dynamic Programming, Monte Carlo Methods and Temporal Difference Learning. All three fundamental reinforcement learning methods are presented in an interesting way and using good examples. Personally, I liked the TD-Learning part best and I agree that this method is indeed the central method and an original contribution of reinforecement learning to the field of machine learning. The third part, "A Unified View" present more advanced techniques. The last chapter gives the most important case studies in reinforcement learning including Samuel's Checkers Player and Thesauro's TD-Gammon. The book is very readable and every chapter ends with illustrative exercises (many of them actually are real programming projects!), always useful summary and very valuable bibliographical and historical remarks. Some subchapters are more advanced and therefore marked with '*'. I really recommend first two parts to any student ofd computer science or anyone interested in machine learning and fuzzy computing. The third part is much more advanced but it would be definitely interesting for advanced computer scientists and graduate students.Read more ›
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15 of 16 people found the following review helpful
Reinforcement Learning is an exceptionally clear introduction to a field that also goes under names such as approximate dynamic programming, adaptive dynamic programming and neuro-dynamic programming. The book is written entirely from the perspective of computer science, where problems tend to have discrete states (although potentially large state spaces) and (typically) small action spaces.
The book provides numerous step-by-step algorithms that makes it relatively easy to get started writing algorithms. The presentation uses minimal mathematics, and avoids the difficult theory supporting the convergence proofs, making it a nice introduction for undergraduates and graduates alike. But throughout the presentation is evidence of extensive experience with applying these methods to a range of classical problems in artificial intelligence.
I am a software developer and worked on applying Reinforcement Learning (RL) in cognitive fields for my patent work (pending).
This book is highly regarded in RL literature and is probably one of the few hand counted books that explicitly address RL as a subject. The book has good balance between subject matter and theory which makes it unique.
However this book has many serious drawbacks. Had there been excellent books on this subject I would have discouraged you to have this one. Rather my advice would be referring to the book "Approximate Dynamic Programming" by "Warren B. Powell" as well. This DP book has formalized the terms used in Sutton's book. This might save you from the ambiguous terminologies used in Sutton's book.
You might like to refer to these two excellent & precise works by Abhijjit Gosavi: //web.mst.edu/ ~gosavia/ tutorial.pdf and //web.mst.edu/ ~gosavia/ joc.pdf.
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This item: Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning)
