Reinforcement Learning: An Introduction (2nd Edition)

Download Reinforcement Learning: An Introduction (2nd Edition) written by Richard S. Sutton, Andrew G. Barto, Francis Bach in PDF format. This book is under the category Computers and bearing the isbn/isbn13 number 262039249/9780262039246. You may reffer the table below for additional details of the book.


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Richard S. Sutton, Andrew G. Barto, Francis Bach


A Bradford Book; 2nd edition




552 pages









Book Description

Reinforcement learning is one of the most active research areas in artificial intelligence, and this new edition of a widely used textbook on the subject has been significantly expanded and updated to create its new 2nd edition. One of the most active research areas in artificial intelligence, reinforcement learning is a computational approach to learning in which an agent tries to maximize the total amount of reward it receives while interacting with a complex and uncertain environment. Reinforcement learning is one of the most exciting research areas in artificial intelligence. Richard Sutton and Andrew Barto, authors of Reinforcement Learning: An Introduction, 2nd Edition PDF, present a straightforward and understandable account of the most important ideas and algorithms in the field of reinforcement learning. This second edition has seen significant expansion and updating, including the addition of new topics as well as updates to the coverage of existing topics.

– An Exposition on the Concept of Reinforcement Learning (2nd Edition) Adaptive Computation and Machine Learning series, a PDF document.

The primary emphasis of this second edition, just like that of the first, is placed on fundamental online learning algorithms, with the more mathematical content being separated into shaded boxes.

The first part of this series covers as much of reinforcement learning as it is possible to cover while still staying within the confines of a tabular case that has exact solutions. The second edition introduces several new algorithms, including Double Learning, Expected Sarsa, and UCB, all of which are presented in this section.

Part 2 expands upon these ideas by applying them to the concept of function approximation. It also contains new chapters on topics such as artificial neural networks and the Fourier basis, as well as a more in-depth discussion of off-policy learning and policy-gradient methods.

In Part 3, there are new chapters that discuss the connections between reinforcement learning and neuroscience and psychology. Additionally, there is a chapter that discusses updated case studies, which includes AlphaGo and AlphaGo Zero, game playing on Atari, and IBM Watson’s wagering strategy. The implications of reinforcement learning on society in the future are discussed in the final chapter.


Reinforcement learning is becoming an increasingly important technique that is the foundation of many of the most advanced AI systems available today. This book is still considered the seminal textbook on the subject. Reading that is required for anyone who is even remotely interested in the scientific field of AI! Cofounder and current Chief Executive Officer of DeepMind, Demis Hassabis

“This is a ground-breaking work; dealing with a subject that you would have expected to have been figured out right at the beginning of AI… This is not a straightforward theory, but many of the concepts and procedures are applicable in real-world settings, and if you have an interest in neural networks or learning systems, then you need to study this ebook for the full six months that it requires. November 1998, Mike James, Computer Shopper.

“This ebook is the bible of reinforcement learning; and the new 2nd edition is particularly timely given the burgeoning activity in the field,” the author writes in the introduction to the book’s second edition. There is no one who should be interested in the problem of learning to act who should be without it, whether they are researchers, practitioners, students, or curious nonspecialists. ? Author of “The Master Algorithm” and Professor of Computer Science at the University of Washington

Everyone who is interested in learning more about this family of machine learning methods, which is becoming increasingly important, should pick up the latest edition of Reinforcement Learning by Sutton and Barto.

This second edition expands on the highly successful first edition by covering today’s key algorithms and theory; illustrating these concepts using real-world applications that range from learning to control robots to learning to defeat the human world-champion Go player; and discussing fundamental connections between these computer algorithms and research on human learning from psychology and neuroscience. “This second edition expands on the highly successful first edition by covering today’s key algorithms and theory; covering today’s key algorithms and theory; illustrating these concepts using real-world applications that range from learning ? Carnegie-Mellon University’s Tom Mitchell is a professor in the department of computer science.

The publication of the Reinforcement Learning 2nd edition (PDF) by Sutton and Barto couldn’t have come at a better time. Researchers in machine learning have never shown a greater interest in reinforcement learning than they do right now. This is likely due to the rapid advancements made in the field over the past 20 years. This is the textbook you should go to in order to get started with if you want to have a complete understanding of the fundamentals of learning agents. It has been expanded with recent developments in deep reinforcement learning, in addition to the scholarly history of the field being brought up to date to include contemporary events. I will most definitely suggest it to all of my college students in addition to the numerous other graduate students and researchers who are interested in gaining an understanding of the background of the recent excitement surrounding RL. ? Yoshua Bengio is a professor at the University of Montreal, where he teaches computer science and operations research.

“The first edition of Sutton and Barto’s ebook was a formative text for several generations of researchers who worked in the field of reinforcement learning. While the new edition significantly expands the range of topics covered (new topics covered include artificial neural networks; Monte-Carlo tree search; average reward maximization; and a chapter on classic and new applications), thus increasing breadth, the authors also managed to increase the depth of the presentation by using cleaner notation and untangling various aspects of this enormous topic. The 2nd edition is guaranteed to please both previous readers and new readers. However, the new edition keeps the explanations as straightforward and easy to understand as the previous one did, ensuring that the book is still very accessible to readers from a wide range of different kinds of backgrounds. An outstanding ebook that comes highly recommended from me to anyone who is interested in making use of, developing, or understanding reinforcement learning. ? Professor of Computer Science at the University of Alberta and Research Scientist at DeepMind Csaba Szepesvari


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