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Multi-agent machine learning : a rei...

Schwartz, Howard M.

Multi-agent machine learning : a reinforcement approach

紀錄類型:	書目-語言資料,印刷品 : 單行本
副題名:	a reinforcement approach
作者:	SchwartzHoward M.,
出版地:	Hoboken, NJ
出版者:	John Wiley & Sons Inc.;
出版年:	c2014
面頁冊數:	xi, 242 p.ill. : 24 cm.;
標題:	Differential games -
標題:	Machine learning -
標題:	Reinforcement learning -
標題:	Swarm intelligence -
電子資源:	http://catalogimages.wiley.com/images/db/jimages/9781118362082.jpg
附註:	Includes bibliographical references and index
摘要註:	"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"--Provided by publisher
ISBN:	978-1-11-836208-2

Multi-agent machine learning : a reinforcement approach
Schwartz, Howard M.

Multi-agent machine learning : a reinforcement approach / Howard M. Schwartz - Hoboken, NJ : John Wiley & Sons Inc., c2014. - xi, 242 p. ; ill. ; 24 cm..
Includes bibliographical references and index.
ISBN 978-1-11-836208-2ISBN 1-11-836208-X
Differential gamesMachine learningReinforcement learningSwarm intelligence

Multi-agent machine learning : a reinforcement approach
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215 1 $a xi, 242 p. $c ill. $d 24 cm.
300 $a Includes bibliographical references and index
330 $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"--Provided by publisher
330 $a "Provide an in-depth coverage of multi-player, differential games and Gam theory"--Provided by publisher
606 $a Differential games $2 lc $3 321879
606 $a Machine learning $2 lc $3 73096
606 $a Reinforcement learning $2 lc $3 321880
606 $a Swarm intelligence $2 lc $3 170315
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