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217182863
Reinforcement Learning second edition: An Introduction (Adaptive Computation and Machine Learning series)
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Item #:
217182863
GHS 2041
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The significantly expanded and updated new edition of a widely used text on reinforcement learning
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What Stands Out
Comprehensive Overview
This edition offers an in-depth exploration of reinforcement learning, bridging theory and practical applications, making it essential for both beginners and experienced practitioners in machine learning.
Updated Content
The second edition includes the latest advancements and methodologies in reinforcement learning, ensuring readers are equipped with current knowledge and techniques relevant to today’s advancements in AI.
Illustrative Examples
Richly illustrated with diagrams and examples, this book enhances understanding, allowing readers to visualize complex concepts effectively, thereby facilitating deeper learning and application of reinforcement learning principles.
Product Details
- 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.
| Publisher | Bradford Books |
| Publication date | 13 Nov. 2018 |
| Edition | 2. |
| Language | English |
| Print length | 552 pages |
| ISBN-10 | 0262039249 |
| ISBN-13 | 978-0262039246 |
| Reading age | 18 years and up |
| Dimensions | 18.4 x 3.8 x 23.6 cm |
Who Should Buy?
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Students
Ideal for undergraduate and graduate students seeking a comprehensive introduction to reinforcement learning concepts and techniques.
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Researchers
Researchers in AI and machine learning can benefit from the detailed theoretical insights and practical applications presented in the book.
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Practitioners
Individuals working in AI implementation will find practical examples and algorithms that can be utilized in real-world applications.
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Beginners
Absolute beginners in machine learning may find the content too advanced without prior knowledge of foundational concepts.
Product Description
About This Item
Introducing "Reinforcement Learning: An Introduction" - The Ultimate Guide to Understanding and Mastering Reinforcement Learning! Are you fascinated by the concept of machine learning and eager to explore its applications? Look no further! "Reinforcement Learning: An Introduction" is the comprehensive resource you’ve been searching for. As a part of the renowned Adaptive Computation and Machine Learning series, this hardcover book is an essential addition to any enthusiast's library. It provides a detailed and insightful introduction to reinforcement learning, a subfield of machine learning that focuses on decision-making and optimization in dynamic environments. In this illustrated edition, you'll find a wealth of knowledge and practical insights to help you navigate the world of reinforcement learning. Whether you're a beginner or an experienced practitioner, this book is designed to cater to your needs and enhance your understanding of this cutting-edge technology. With its release in November 2018, this hardcover edition ensures a durable and lasting companion for your learning journey.
Its solid construction and impeccable attention to detail make it a valuable collectible that you can reference time and time again. Designed for anyone eager to dive deeper into the world of reinforcement learning, this book covers topics such as value functions, Monte Carlo methods, temporal difference learning, and so much more. The authors provide clear explanations and intuitive examples to aid your understanding, allowing you to grasp complex concepts effortlessly. Harness the power of reinforcement learning and unlock its potential in various industries, including robotics, gaming, finance, and healthcare. Whether you're a student, researcher, or industry professional, this book will equip you with the knowledge and tools needed to bring your ideas to life. Don't miss out on this must-have resource for mastering reinforcement learning.
Order your hardcover copy of "Reinforcement Learning: An Introduction" today and get ready to embark on an exciting journey through the fascinating world of adaptive computation and machine learning!.
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English edition Richard S. Sutton Editorial Review
Reinforcement Learning Second Edition: An Introduction (Adaptive Computation And Machine Learning Series) offers a profound insight into the world of reinforcement learning, expertly balancing theory with practical applications. This comprehensive guide spans 552 pages and is designed for readers aged 18 and up, making it both accessible and rigorous for beginners and seasoned professionals. The book features fantastic illustrations and exercises that effectively aid in mastering complex concepts. However, while the content is highly regarded, there are mixed opinions on the print quality, with some users expressing concerns about font size, which may hinder comfortable reading. Overall, it's a valuable resource for anyone serious about exploring this dynamic field.
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Pros
- Excellent balance of theory and practice
- Illustrative examples enhance understanding
- Highly recommended for beginners and professionals
- Great hardcover quality with colorful printing
- In-depth explanations and exercises provided
Cons
- Small font size can make reading challenging for some
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GHS 2041
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Features & Benefits
- Reinforcement Learning is a widely used text in the field
- Provides a clear and simple account of the key ideas and algorithms
- Significantly expanded and updated second edition
- Includes new topics and updated coverage of other topics
- Focuses on core online learning algorithms
- Expands into function approximation and off-policy learning
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