Multi-Agent Machine Learning: A Reinforcement Approach
The book begins with a chapter on traditional methods of supervised learning, covering recursive least squares learning, mean square error methods, and stochastic approximation. Chapter 2 covers single agent reinforcement learning. Topics include learning value functions, Markov games, and TD learning with eligibility traces. Chapter 3 discusses two player games including two player matrix games with both pure and mixed strategies. Numerous algorithms and examples are presented. Chapter 4 covers learning in multi-player games, stochastic games, and Markov games, focusing on learning multi-player grid games-two player grid games, Q-learning, and Nash Q-learning. Chapter 5 discusses differential games, including multi player differential games, actor critique structure, adaptive fuzzy control and fuzzy interference systems, the evader pursuit game, and the defending a territory games. Chapter 6 discusses new ideas on learning within robotic swarms and the innovative idea of the evolution of personality traits. 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.
Schwartz, H.M. (2014). Multi-Agent Machine Learning: A Reinforcement Approach. Multi-Agent Machine Learning: A Reinforcement Approach, 1–242. doi:10.1002/9781118884614