Multi-Agent Machine Learning. A Reinforcement Approach

Multi-Agent Machine Learning. A Reinforcement Approach
Автор книги: id книги: 1058271     Оценка: 0.0     Голосов: 0     Отзывы, комментарии: 0 11442,8 руб.     (111,81$) Купить и читать книгу Купить бумажную книгу Электронная книга Жанр: Техническая литература Правообладатель и/или издательство: John Wiley & Sons Limited Дата добавления в каталог КнигаЛит: ISBN: 9781118884478 Возрастное ограничение: 0+

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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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