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Reinforcement Learning: An Introduction (Adaptive Computation And Machine Learning),Used
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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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- Q: What is the page count of the book? A: The book contains three hundred twenty-two pages, providing a thorough exploration of reinforcement learning concepts.
- Q: What are the dimensions of the book? A: The book measures seven point twenty-five inches in length, one point twenty-six inches in width, and nine point five inches in height.
- Q: What type of binding does the book have? A: The book features a hardcover binding, ensuring durability and longevity for readers.
- Q: How do I use this book effectively? A: You can use this book as a foundational text for understanding reinforcement learning, suitable for both beginners and advanced learners.
- Q: Is this book suitable for self-study? A: Yes, this book is designed for self-study, providing clear explanations and examples of reinforcement learning algorithms.
- Q: What reading level is this book aimed at? A: The book is suitable for readers with a basic understanding of probability, making it accessible to undergraduates and professionals alike.
- Q: How do I care for this hardcover book? A: To keep the book in good condition, store it upright on a shelf away from direct sunlight and handle it with clean hands.
- Q: Is this book safe for children? A: The book is not specifically designed for children, as it covers complex topics in artificial intelligence.
- Q: Can I easily clean the book's cover? A: Yes, you can gently wipe the hardcover cover with a soft, dry cloth to remove dust and maintain its appearance.
- Q: What makes this book different from other machine learning books? A: This book focuses specifically on reinforcement learning, providing a comprehensive overview from foundational concepts to advanced applications.
- Q: Is this book more suitable for beginners or experts? A: This book caters to both beginners and experts, making it a versatile resource for understanding reinforcement learning.
- Q: How does this book compare to other reinforcement learning texts? A: This book is praised for its clarity and thoroughness, making it a preferred choice for readers seeking a solid introduction to reinforcement learning.
- Q: What if I find a page damaged? A: If you receive a damaged book, contact the seller or publisher for a replacement or return options.
- Q: Does this book come with any supplementary materials? A: No, this book does not include supplementary materials, but it offers comprehensive content on its own.
- Q: Can I return this book if I'm not satisfied? A: Return policies may vary by seller, so check the specific retailer's policy for returns or exchanges.