Multi-agent machine learning : a reinforcement approach / Howard M. Schwartz, Department of Systems and Computer Engineering, Carleton University.
By: Schwartz, Howard M [editor.].
Publisher: Hoboken, New Jersey : Wiley, ©2014Description: 242 pages ; illustrations ; 25 cm.Content type: text Media type: unmediated Carrier type: volumeISBN: 9781118362082 (hardback).Subject(s): Reinforcement learning | Differential games | Swarm intelligence | Machine learning | TECHNOLOGY & ENGINEERING / Electronics / GeneralGenre/Form: Print books.Online resources: Cover image 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"--Summary: "Provide an in-depth coverage of multi-player, differential games and Gam theory"--| Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|
| On Shelf | Q325.6 .S39 2014 (Browse shelf) | Available | AU00000000014582 |
Includes bibliographical references and index.
"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"--