Chapter 1: Traditional Methods of Supervised Learning

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: Single Agent Reinforcement Learning

Chapter 2 covers single agent reinforcement learning. Topics include learning value functions, Markov games, and TD learning with eligibility traces.

Chapter 3: Two Player Games

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: Learning in Multi-Player Games

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: Differential Games

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: Learning in Robotic Swarms

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.