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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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