Multi-agent machine learning : a reinforcement approach - Schwartz, Howard M. (Hoboken, NJ : John Wiley & Sons Inc., 2014)
'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'--
Includes bibliographical references and index.
Keywords: Reinforcement learning; Differential games; Swarm intelligence; Machine learning