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

Gespeichert in:
1. Verfasser:
Schwartz, Howard M.
Format:
Elektronisch E-Book
Sprache:
Englisch
Veröffentlicht:
Hoboken, New Jersey : John Wiley & Sons, Inc., 2014.
Ausgabe:
1st edition
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"--
Umfang:
1 online resource (458 p.)
text file
Anmerkungen:
Description based upon print version of record.
Bibliografie:
Includes bibliographical references at the end of each chapters and index.
ISBN:
9781118884485
1118884485
9781118884614
1118884612
9781118884478
1118884477
Schlagworte:
Reinforcement learning.
Differential games.
Swarm intelligence.
Machine learning.
Bezugswerke:
Links: