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

"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...

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Bibliographic Details
Other Authors: Schwartz, Howard M. (Editor)
Format: eBook
Language:English
Published: Hoboken, NJ : John Wiley & Sons, [2014]
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Online Access:Click for online access
Description
Summary:"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"--
"Provide an in-depth coverage of multi-player, differential games and Gam theory"--
Physical Description:1 online resource
Bibliography:Includes bibliographical references and index.
ISBN:9781118884485
1118884485
9781118884478
1118884477
9781118884614
1118884612
9781322094762
1322094764
Source of Description, Etc. Note:Print version record and CIP data provided by publisher.