Multi-Agent Machine Learning: A Reinforcement Approach

Synopsis

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-- Howard M. Schwartz. Includes Bibliographical References And Index.