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Multi-agent machine learning : a reinforcement approach /

"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...

Ausführliche Beschreibung

Bibliographische Detailangaben
1. Verfasser: Schwartz, Howard M.
Format: Printed Book
Sprache:English
Veröffentlicht: New Jersey: Wiley, 2014.
Schlagworte:
Online Zugang:http://catalogimages.wiley.com/images/db/jimages/9781118362082.jpg
Beschreibung
Zusammenfassung:"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"--
Beschreibung:xi, 242 pages :
ISBN:9781118362082 (hardback)
111836208X (hardback)