Schwartz, Howard M., <br/><br/>
Multi-agent machine learning :  a reinforcement approach / 
Howard M. Schwartz, Department of Systems and Computer Engineering, Carleton University. 
 - New Jersey:  Wiley,  2014. 
 - xi, 242 pages : 
<br/><br/> "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"-- 
<br/><br/><label>ISBN: </label>9781118362082 (hardback) 111836208X (hardback) 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Reinforcement learning.<br/>Differential games.<br/>Swarm intelligence.<br/>Machine learning.<br/>TECHNOLOGY & ENGINEERING / Electronics / General.
<br/><br/><label>Dewey Class. No.: </label>004.85  / SCH