The Bio-Web: Resources for Molecular and Cell Biologists

The Bio-Web: Molecular and Cell Biology and Bioinformatics news, tools, books, resources and web applications development

JustBio: Bioinformatics at the tips of your fingers

In association with Amazon.com
  

by: Richard S. Sutton, Andrew G. Barto

 : Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning series)
variant image
List Price: $75.00
Amazon.com's Price: $39.41
You Save: $35.59 (47%)
as of 12/10/2018 02:41 EST



Availability: Usually ships in 1-2 business days



Binding: Hardcover
Brand: Bradford Book
EAN: 9780262193986
Edition: 1st Edition
Feature: Bradford Book
ISBN: 9780262193986
Item Dimensions: 900700177106
Label: A Bradford Book
Languages: EnglishPublishedEnglishOriginal LanguageEnglishUnknown
Manufacturer: A Bradford Book
MPN: 21613186
Number Of Items: 1
Number Of Pages: 322
Publication Date: March 01, 1998
Publisher: A Bradford Book
Release Date: February 26, 1998
Studio: A Bradford Book

Features:


Related Items: Alternate Versions: Click to Display

Browse for similar items by category: Click to Display



Editorial Review:

Product 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.





Customer Reviews
Average Rating: none