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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...

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Détails bibliographiques
Cote:Libro Electrónico
Autres auteurs: Schwartz, Howard M. (Éditeur intellectuel)
Format: Électronique eBook
Langue:Inglés
Publié: Hoboken, NJ : John Wiley & Sons, [2014]
Sujets:
Accès en ligne:Texto completo
Texto completo
Description
Résumé:"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"--
Description matérielle:1 online resource
Bibliographie:Includes bibliographical references and index.
ISBN:9781118884485
1118884485
9781118884478
1118884477
9781118884614
1118884612
9781322094762
1322094764