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Multi-agent machine learninga reinfo...

Schwartz, Howard M.

Multi-agent machine learninga reinforcement approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Multi-agent machine learningHoward M. Schwartz.
Reminder of title:	a reinforcement approach /
Author:	Schwartz, Howard M.
Published:	Hoboken, New Jersey :Wiley,2014.
Description:	1 online resource (xi, 242 p.)
Subject:	Reinforcement learning.
Online resource:	http://onlinelibrary.wiley.com/book/10.1002/9781118884614
ISBN:	9781118884614$qelectronic bk.

Multi-agent machine learninga reinforcement approach /
Schwartz, Howard M.

Multi-agent machine learninga reinforcement approach /[electronic resource] :Howard M. Schwartz. - Hoboken, New Jersey :Wiley,2014. - 1 online resource (xi, 242 p.)

Includes bibliographical references and index.

"Multi-Agent Machine Learning: A Reinforcement Learning Approach is a framework to understanding different methods and approaches in multi-agent machine learning. It also provides cohesive coverage of the latest advances in multi-agent differential games and presents applications in game theory and robotics. Framework for understanding a variety of methods and approaches in multi-agent machine learning. Discusses methods of reinforcement learning such as a number of forms of multi-agent Q-learning Applicable to research professors and graduate students studying electrical and computer engineering, computer science, and mechanical and aerospace engineering"--

ISBN: 9781118884614$qelectronic bk.

LCCN: 2014021985Subjects--Topical Terms:

349131
Reinforcement learning.

LC Class. No.: Q325.6 / .S39 2014

Dewey Class. No.: 519.3

Multi-agent machine learninga reinforcement approach /
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010 $a 2014021985
020 $a 9781118884614$qelectronic bk.
020 $a 1118884612$qelectronic bk.
020 $a 9781118884478$qelectronic bk.
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020 $a 9781322094762$qMyiLibrary
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020 $z 1118884485
020 $z 9781118362082$qhardback
020 $z 111836208X$qhardback
035 $a (OCoLC)881065009
035 $a ocn881065009
040 $a DLC $b eng $c DLC $d YDX $d N $d EBLCP $d IDEBK $d OCLCF $d YDXCP $d E7B $d CDX $d RECBK $d COO $d DG1 $d OCLCQ
050 1 4 $a Q325.6 $b .S39 2014
082 0 0 $a 519.3 $2 23
100 1 $a Schwartz, Howard M. $3 721669
245 1 0 $a Multi-agent machine learning $h [electronic resource] : $b a reinforcement approach / $c Howard M. Schwartz.
260 $a Hoboken, New Jersey : $b Wiley, $c 2014.
300 $a 1 online resource (xi, 242 p.)
504 $a Includes bibliographical references and index.
520 $a "Multi-Agent Machine Learning: A Reinforcement Learning Approach is a framework to understanding different methods and approaches in multi-agent machine learning. It also provides cohesive coverage of the latest advances in multi-agent differential games and presents applications in game theory and robotics. Framework for understanding a variety of methods and approaches in multi-agent machine learning. Discusses methods of reinforcement learning such as a number of forms of multi-agent Q-learning Applicable to research professors and graduate students studying electrical and computer engineering, computer science, and mechanical and aerospace engineering"-- $c Provided by publisher.
520 $a "Provide an in-depth coverage of multi-player, differential games and Gam theory"-- $c Provided by publisher.
588 0 $a Online resource; title from digital title page (viewed on December 2, 2014).
650 0 $a Reinforcement learning. $3 349131
650 0 $a Differential games. $3 190628
650 0 $a Swarm intelligence. $3 237730
650 0 $a Machine learning. $3 188639
856 4 0 $u http://onlinelibrary.wiley.com/book/10.1002/9781118884614