해외구매대행 N0.1 쇼핑365
Multi-Agent Machine Learning: A Reinforcement Approach

상품번호 111836208X
상품상태 New    
상품구분 Books / Engineering & Transportation
총페이지수 256 Pages
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Review “This is an interesting book both as research reference as well as teaching material for Master and PhD students.”  (Zentralblatt MATH, 1 April 2015)   . Read more From the Back Cover 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. Read more See all Editorial Reviews




2019-04-04 01:17:09


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