Multi-agent machine learning : a reinforcement approach / Howard M. Schwartz, Department of Systems and Computer Engineering, Carleton University.
Publisher: Hoboken, New Jersey : Wiley, ©2014Description: 242 pages illustrations ; 25 cmContent type:- text
- unmediated
- volume
- 9781118362082
- Q325.6 .S39 2014
BOOKS
| Current library | Home library | Call number | Status | Barcode | |
|---|---|---|---|---|---|
| Alfaisal University On Shelf | Alfaisal University On Shelf | Q325.6 .S39 2014 (Browse shelf(Opens below)) | Available | AU00000000014582 |
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| Q325.5 .Z44 2018 Feature engineering for machine learning : | Q325.6 .G73 2020 Foundations of deep reinforcement learning : | Q325.6 .R45 2018 Reinforcement learning : | Q325.6 .S39 2014 Multi-agent machine learning : a reinforcement approach / | Q325.7 .C43 2018 Introduction to deep learning / | Q325.73 .P75 2023 Understanding deep learning / | Q327 .B52 2006 Pattern recognition and machine learning / |
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"--