Title
Reinforcement Learning: An Introduction (Adaptive Computation And Machine Learning),Used
Delivery time: 8-12 business days (International)
Richard Sutton And Andrew Barto Provide A Clear And Simple Account Of The Key Ideas And Algorithms Of Reinforcement Learning. Their Discussion Ranges From The History Of The Field'S Intellectual Foundations To The Most Recent Developments And Applications.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 When Interacting With A Complex, Uncertain Environment. In Reinforcement Learning, Richard Sutton And Andrew Barto Provide A Clear And Simple Account Of The Key Ideas And Algorithms Of Reinforcement Learning. Their Discussion Ranges From The History Of The Field'S Intellectual Foundations To The Most Recent Developments And Applications. The Only Necessary Mathematical Background Is Familiarity With Elementary Concepts Of Probability.The Book Is Divided Into Three Parts. Part I Defines The Reinforcement Learning Problem In Terms Of Markov Decision Processes. Part Ii Provides Basic Solution Methods: Dynamic Programming, Monte Carlo Methods, And Temporaldifference Learning. Part Iii Presents A Unified View Of The Solution Methods And Incorporates Artificial Neural Networks, Eligibility Traces, And Planning; The Two Final Chapters Present Case Studies And Consider The Future Of Reinforcement Learning.
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Frequently Asked Questions
- Q: What is the main focus of 'Reinforcement Learning: An Introduction'? A: The book focuses on the key ideas and algorithms of reinforcement learning, covering its historical foundations as well as recent developments and applications in artificial intelligence.
- Q: What prior knowledge is required to understand this book? A: A basic understanding of elementary probability concepts is the only prerequisite for readers to grasp the content of this book.
- Q: How is the book structured? A: The book is divided into three parts: Part I defines the reinforcement learning problem; Part II discusses basic solution methods; and Part III presents a unified view of these methods along with advanced concepts.
- Q: Who are the authors of this book? A: The book is authored by Richard S. Sutton and Andrew Barto, both well-respected figures in the field of reinforcement learning.
- Q: What is the publication date of this book? A: The book was published on March 1, 1998.
- Q: What is the condition of the book? A: The book is listed in good condition, indicating it may have some wear but is still fully usable.
- Q: What type of binding does this book have? A: This edition features a hardcover binding, which provides durability and a professional appearance.
- Q: How many pages are in this book? A: The book contains a total of 322 pages.
- Q: Is this book suitable for beginners in reinforcement learning? A: Yes, this book is suitable for beginners as it explains fundamental concepts clearly and requires minimal prior knowledge.
- Q: What topics are covered in the case studies included in the book? A: The case studies in the book illustrate practical applications of reinforcement learning techniques and explore future trends in the field.