Schwartz Multi-Agent Machine Learning

A Reinforcement Approach
1. Auflage 2014
ISBN: 978-1-118-88448-5
Verlag: John Wiley & Sons
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 256 Seiten, E-Book

The book begins with a chapter on traditional methods ofsupervised learning, covering recursive least squares learning,mean square error methods, and stochastic approximation. Chapter 2covers single agent reinforcement learning. Topics include learningvalue functions, Markov games, and TD learning with eligibilitytraces. Chapter 3 discusses two player games including two playermatrix games with both pure and mixed strategies. Numerousalgorithms and examples are presented. Chapter 4 covers learning inmulti-player games, stochastic games, and Markov games, focusing onlearning multi-player grid games--two player grid games,Q-learning, and Nash Q-learning. Chapter 5 discusses differentialgames, including multi player differential games, actor critiquestructure, 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 andthe innovative idea of the evolution of personality traits.
* Framework for understanding a variety of methods andapproaches in multi-agent machine learning.
* Discusses methods of reinforcement learning such as anumber of forms of multi-agent Q-learning
* Applicable to research professors and graduatestudents studying electrical and computer engineering, computerscience, and mechanical and aerospace engineering

Autoren/Hrsg.

Schwartz, H. M.

Über Autor(innen)

Howard M. Schwartz, PhD, received his B.Eng. Degree from McGill University, Montreal, Canada in une 1981 and his MS Degree and PhD Degree from MIT, Cambridge, USA in 1982 and 1987 respectively. He is currently a professor in systems and computer engineering at Carleton University, Canada. His research interests include adaptive and intelligent control systems, robotic, artificial intelligence, system modelling, system identification, and state estimation.