Coupon
Reinforcement Learning : An Introduction
by Richard S. Sutton and Andrew G. Barto


Overview -

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.  Read more...


 
Hardcover
  • $75.00

Add to Cart + Add to Wishlist

Limited Availability. Allow Additional 2-4 Weeks for Shipping.

Free Shipping is not available for this item.
 
> Check In-Store Availability

In-Store pricing may vary

 
 
New & Used Marketplace 25 copies from $51.00
 
eBook
Retail Price: $74.99
$56.39

Add to Cart + Add to Wishlist

Download

This item is available only to U.S. and Canada billing addresses.
 
 
 

More About Reinforcement Learning by Richard S. Sutton; Andrew G. Barto
 
 
 
Overview

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.


 
Details
  • ISBN-13: 9780262193986
  • ISBN-10: 0262193981
  • Publisher: Bradford Book
  • Publish Date: March 1998
  • Page Count: 344
  • Reading Level: Ages 18-UP
  • Dimensions: 9.36 x 7.29 x 1.05 inches
  • Shipping Weight: 1.72 pounds

Series: Adaptive Computation and Machine Learning

Related Categories

Books > Computers & Internet > Intelligence (AI) & Semantics

 
BAM Customer Reviews