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Multi-Agent Machine Learning: A Reinforcement Approach
ASIN번호 111836208X
상품상태 New   
상품구분 Books / Engineering & Transportation
총페이지수 256 Pages
판매자 Amazon.com
판매자위치 미국
현지 판매 가격
$99.87
상품가격 상세보기
관련상품



상품설명
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








상품설명
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