a reinforcement approach Schwartz, Howard M. creator editor. text bibliography Print books. nju 2014 2014 monographic eng

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242 pages illustrations ; 25 cm "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"-- Howard M. Schwartz, Department of Systems and Computer Engineering, Carleton University. Includes bibliographical references and index. Reinforcement learning Differential games Swarm intelligence Machine learning TECHNOLOGY & ENGINEERING / Electronics / General Q325.6 .S39 2014 9781118362082 2014016950 DLC 140604 20260412091429.0 18175924 eng