Multi-agent machine learning : a reinforcement approach /
spine title : Multi-agent machine learning.
Howard M. Schwartz.
- xi, 242 pages : illustrations ; 24 cm.
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'--