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

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

 

Synopses & Reviews

Publisher Comments:

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.

Synopsis:

An account of key ideas and algorithms in reinforcement learning. The discussion ranges from the history of the field's intellectual foundations to recent developments and applications. Areas studied include reinforcement learning problems in terms of Markov decision problems and solution methods.

Synopsis:

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.

Synopsis:

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.

Synopsis:

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

Description:

Includes bibliographical references (p. [291]-312) and index.

Product Details

ISBN:
9780262193986
Author:
Sutton, Richard S.
Author:
Barto, Andrew G.
Publisher:
Bradford Book
Location:
Cambridge, Mass. :
Subject:
Artificial Intelligence
Subject:
Machine Theory
Subject:
Machine learning
Subject:
Reinforcement learning (Machine learning)
Subject:
Reinforcement learning
Subject:
Artificial Intelligence - General
Subject:
Intelligence (AI) & Semantics
Subject:
Computers-Reference - General
Copyright:
Series:
Adaptive Computation and Machine Learning series Reinforcement Learning
Series Volume:
v. 10
Publication Date:
19980231
Binding:
HARDCOVER
Grade Level:
Professional and scholarly
Language:
English
Illustrations:
Yes
Pages:
342
Dimensions:
9.1 x 7.1 x 1.2 in
Age Level:
from 18

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Related Subjects

Computers and Internet » Artificial Intelligence » General
Computers and Internet » Computers Reference » General
History and Social Science » World History » General
Reference » Science Reference » Technology

Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning) New Hardcover
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$79.50 In Stock
Product details 342 pages Bradford Book - English 9780262193986 Reviews:
"Synopsis" by , An account of key ideas and algorithms in reinforcement learning. The discussion ranges from the history of the field's intellectual foundations to recent developments and applications. Areas studied include reinforcement learning problems in terms of Markov decision problems and solution methods.
"Synopsis" by , 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.
"Synopsis" by , 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.
"Synopsis" by , 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
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