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Multi-Agent Machine Learning




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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

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