Multi-Agent Machine Learning - A Reinforcement Approach (Hardcover)

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

General

Imprint:	John Wiley & Sons
Country of origin:	United States
Release date:	September 2014
First published:	2014
Authors:	H M Schwartz
Dimensions:	234 x 156 x 15mm (L x W x T)
Format:	Hardcover - Cloth over boards
Pages:	256
ISBN-13:	978-1-118-36208-2
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	1-118-36208-X
Barcode:	9781118362082

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