01958cam a2200361 i 4500 18175924 20260412091429.0 140604t20142014njua b 001 0 eng 2014016950 9781118362082 (hardback) 18175924 DLC eng DLC rda DLC AU pcc Alfaisal Main Library Q325.6 .S39 2014 Schwartz, Howard M., editor. Multi-agent machine learning : a reinforcement approach / Howard M. Schwartz, Department of Systems and Computer Engineering, Carleton University. Hoboken, New Jersey : Wiley, ©2014 242 pages illustrations ; 25 cm text rdacontent txt unmediated rdamedia n volume rdacarrier nc 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"-- Reinforcement learning. Differential games. Swarm intelligence. Machine learning. TECHNOLOGY & ENGINEERING / Electronics / General. bisacsh Print books. local 4 lcc BOOKS 608494 608494 0 0 lcc 0 0 AU AU GEN 2026-04-12 1 Q325.6 .S39 2014 AU00000000014582 2026-04-12 09:43:42 2026-04-12 885.00 2026-04-12 BOOKS