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MultiAgent Machine Learning: A Reinforcement Approach,Used
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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 multiplayer games, stochastic games, and Markov games, focusing on learning multiplayer grid gamestwo player grid games, Qlearning, and Nash Qlearning. 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 multiagent machine learning. Discusses methods of reinforcement learning such as a number of forms of multiagent Qlearning Applicable to research professors and graduate students studying electrical and computer engineering, computer science, and mechanical and aerospace engineering
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