9780262193986-0262193981-Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning series)

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

ISBN-13: 9780262193986
ISBN-10: 0262193981
Edition: second edition
Author: Richard S. Sutton, Andrew G. Barto
Publication date: 1998
Publisher: A Bradford Book
Format: Hardcover 322 pages
Category: Computers
FREE shipping on ALL orders
Marketplace
from $30.45

Book details

ISBN-13: 9780262193986
ISBN-10: 0262193981
Edition: second edition
Author: Richard S. Sutton, Andrew G. Barto
Publication date: 1998
Publisher: A Bradford Book
Format: Hardcover 322 pages
Category: Computers

Summary

Acknowledged authors Richard S. Sutton , Andrew G. Barto wrote Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning series) comprising 322 pages back in 1998. Textbook and eTextbook are published under ISBN 0262193981 and 9780262193986. Since then Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning series) textbook was available to sell back to BooksRun online for the top buyback price of $ 11.12 or rent at the marketplace.

Description

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 temporal-difference 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.

Rate this book Rate this book

We would LOVE it if you could help us and other readers by reviewing the book