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.
"Sinopsis" puede pertenecer a otra edición de este libro.
Richard S. Sutton is Professor of Computing Science and AITF Chair in Reinforcement Learning and Artificial Intelligence at the University of Alberta, and also Distinguished Research Scientist at DeepMind.
Andrew G. Barto is Professor Emeritus in the College of Computer and Information Sciences at the University of Massachusetts Amherst.
"Sobre este título" puede pertenecer a otra edición de este libro.
Librería: BooksRun, Philadelphia, PA, Estados Unidos de America
Hardcover. Condición: Fair. First Edition. The item might be beaten up but readable. May contain markings or highlighting, as well as stains, bent corners, or any other major defect, but the text is not obscured in any way. Nº de ref. del artículo: 0262193981-7-1
Cantidad disponible: 1 disponibles
Librería: ZBK Books, Carlstadt, NJ, Estados Unidos de America
Condición: good. Fast & Free Shipping â" Good condition with a solid cover and clean pages. Shows normal signs of use such as light wear or a few marks highlighting, but overall a well-maintained copy ready to enjoy. Supplemental items like CDs or access codes may not be included. Nº de ref. del artículo: ZWV.0262193981.G
Cantidad disponible: 1 disponibles
Librería: HPB-Red, Dallas, TX, Estados Unidos de America
Hardcover. Condición: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority! Nº de ref. del artículo: S_453245387
Cantidad disponible: 1 disponibles
Librería: Half Price Books Inc., Dallas, TX, Estados Unidos de America
hardcover. Condición: Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority! Nº de ref. del artículo: S_457084918
Cantidad disponible: 1 disponibles
Librería: 3Brothers Bookstore, Egg harbor township, NJ, Estados Unidos de America
Condición: good. Books may contain some notes and highlighting. Supplements such as Access Codes, Cd's Dust Jackets, etc. are not guaranteed with used book purchases. Nº de ref. del artículo: EVV.0262193981.G
Cantidad disponible: 1 disponibles
Librería: Sekkes Consultants, North Dighton, MA, Estados Unidos de America
Hardcover. Condición: Near fine. Estado de la sobrecubierta: Near fine. 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. InReinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. The only necessary mathematical background is familiarity with elementary concepts of probability. Owner Signature on ffep, fine otherwise. 7¼" - 9¼". Book. Nº de ref. del artículo: 278286
Cantidad disponible: 1 disponibles
Librería: Anybook.com, Lincoln, Reino Unido
Condición: Fair. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In fair condition, suitable as a study copy. Dust jacket in fair condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,900grams, ISBN:9780262193986. Nº de ref. del artículo: 4315704
Cantidad disponible: 1 disponibles
Librería: Anybook.com, Lincoln, Reino Unido
Condición: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. Dust jacket in fair condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,900grams, ISBN:9780262193986. Nº de ref. del artículo: 4315703
Cantidad disponible: 1 disponibles
Librería: ReviBlio, Barcelona, B, España
Condition: 15 pages with some highlighted text, the rest excellent. The book provides a clear and simple account of the key ideas and algorithms in this area of artificial intelligence, where an agent learns to maximize a cumulative reward by interacting with a complex, uncertain environment. It covers the history of the field's intellectual foundations and proceeds to the core algorithms and concepts, including: The Reinforcement Learning Problem framed in terms of Markov Decision Processes (MDPs). Basic Solution Methods like Dynamic Programming, Monte Carlo methods, and the influential Temporal-Difference (TD) learning (e.g., Q-learning and SARSA). Function Approximation for handling large state spaces, including the use of artificial neural networks. More advanced topics like policy-gradient methods and a discussion of RL's relationships to psychology and neuroscience. Often referred to as the "bible" of the field, it is a foundational text suitable for students, researchers, and practitioners with a basic understanding of probability. Nº de ref. del artículo: ABE-1760107744142
Cantidad disponible: 1 disponibles
Librería: YESIBOOKSTORE, MIAMI, FL, Estados Unidos de America
hardcover. Condición: As New. Nº de ref. del artículo: 0262193981-VB
Cantidad disponible: 1 disponibles