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This is a highly intuitive and accessible introduction to the recent major developments in reinforcement learning, written by two of the field's pioneering contributors.(Dimitri P. Bertsekas and John N. Tsitsiklis, Professors, Department of Electrical Engineering andn Computer Science, Massachusetts Institute of Technology)
This book not only provides an introduction to learning theory but also serves as a tremendous source of ideas for further development and applications in the real world.(Toshio Fukuda, Nagoya University, Japan; President, IEEE Robotics and Automantion Society)
Reinforcement learning has always been important in the understanding of the driving force behind biological systems, but in the last two decades it has become increasingly important, owing to the development of mathematical algorithms. Barto and Sutton were the prime movers in leading the development of these algorithms and have described them with wonderful clarity in this new text. I predict it will be the standard text.(Dana Ballard, Professor of Computer Science, University of Rochester)
The widely acclaimed work of Sutton and Barto on reinforcement learning applies some essentials of animal learning, in clever ways, to artificial learning systems. This is a very readable and comprehensive account of the background, algorithms, applications, and future directions of this pioneering and far-reaching work.(Wolfram Schultz, University of Fribourg, Switzerland)
A must read if you're taking your first steps into RL. It covers the basics in an easy-to-understand language. Covers MDP, Q Learning, Sarsa, TD-Lamda etc.Published 14 months ago by Nahas Pareekutty
Pros: Great book about reinforcement learning. Easy to understand.
Cons: It has a hard cover with a detached paper cover.
This book was a great help at writing and programming for my thesis. It has a ton of information, and even I was able to understand and follow the math. Read morePublished on January 21, 2009 by William J. Andrus
I agree with reviewer Frank "Good introduction but not well structured, May 8, 2005" the authors are over-anxious to establish the credentials of RL in older research traditions. Read morePublished on February 19, 2007 by _eam 0 n_
This book provides an easy to read introduction in reinforcement learning. It covers several approaches (dynamic programming, monte carlo, temproal differnce) and gives a lot of... Read morePublished on May 8, 2005 by Zac
As a subfield of artificial intelligence, reinforcement learning has shown great success from both a theoretical and practical viewpoint. Read morePublished on November 5, 2004 by Dr. Lee D. Carlson