Multi-Agent Machine Learning (PDF)

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