PDF Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series)

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Contents

  1. Amazon Price History
  2. A Beginner's Guide to Deep Reinforcement Learning
  3. Reinforcement Learning: An Introduction - Richard S. Sutton, Andrew G. Barto - Google книги
  4. We’re listening — tell us what you think
  5. Reinforcement Learning: An Introduction

Amazon Price History

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. 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.

The final chapter discusses the future societal impacts of reinforcement learning. Richard S. In Reinforcement Learning , Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms.

A Beginner's Guide to Deep Reinforcement Learning

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. 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. The final chapter discusses the future societal impacts of reinforcement learning. 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.

ISBN 10: 0262193981

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. The widely acclaimed work of Sutton and Barto on reinforcement learning applies some essentials of animal learning, in clever ways, to artificial learning systems.

Reinforcement Learning: An Introduction - Richard S. Sutton, Andrew G. Barto - Google книги

This is a very readable and comprehensive account of the background, algorithms, applications, and future directions of this pioneering and far-reaching work. No one with an interest in the problem of learning to act - student, researcher, practitioner, or curious nonspecialist - should be without it. The second edition is guaranteed to please previous and new readers: 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 disentangling various aspects of this immense topic.

At the same time, the new edition retains the simplicity and directness of explanations, thus retaining the great accessibility of the book to readers of all kinds of backgrounds. A fantastic book that I wholeheartedly recommend those interested in using, developing, or understanding reinforcement learning.

This second edition expands on the popular first edition, 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.

Required reading for anyone seriously interested in the science of AI! The appetite for reinforcement learning among machine learning researchers has never been stronger, as the field has been moving tremendously in the last twenty years. If you want to fully understand the fundamentals of learning agents, this is the textbook to go to and get started with. Ithas been extended with modern developments in deep reinforcement learning while extending the scholarly history of the field to modern days. I will certainly recommend it to all my students and the many other graduate students and researchers who want to get the appropriate context behind the current excitement for RL.

We’re listening — tell us what you think

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. 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. Seller Inventory AAU Bookseller Inventory ST Seller Inventory ST Book Description Shipped from UK within 10 to 14 business days.

New copy - Usually dispatched within 2 working days. Seller Inventory B Num Pages: pages, Dimension: x x Weight in Grams: Seller Inventory V Sutton; Andrew G. Sutton ; Andrew G. Publisher: A Bradford Book , This specific ISBN edition is currently not available. View all copies of this ISBN edition:.

Reinforcement Learning: An Introduction

Synopsis About this title Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. About the Author : Richard S. Review : 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. 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.

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