Multi-Agent Machine Learning: A Reinforcement Approach

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

Howard M. Schwartz, PhD, received his B.Eng. Degree from McGill University, Montreal, Canada in une 1981 and his MS Degree and PhD Degree from MIT, Cambridge, USA in 1982 and 1987 respectively. He is currently a professor in systems and computer engineering at Carleton University, Canada. His research interests include adaptive and intelligent control systems, robotic, artificial intelligence, system modelling, system identification, and state estimation.