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Multi-Agent Machine Learning - A Reinforcement Approach (Cód: 9236499)

Schwartz,H M; Schwartz,Howard M

John Wiley & Sons

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Descrição

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. 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

Características

Peso 0.48 Kg
Produto sob encomenda Sim
Marca John Wiley & Sons
I.S.B.N. 9781118362082
Referência 025888879
Altura 23.62 cm
Largura 15.49 cm
Profundidade 1.78 cm
Número de Páginas 256
Idioma Inglês
Acabamento Capa dura
Cód. Barras 9781118362082
Ano da edição 2014
AutorSchwartz,H M; Schwartz,Howard M