Multi-Agent Machine Learning: A Reinforcement Approach

By H.M. Schwartz

Book Overview

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

• Format: Hardcover
• Language: English
• ISBN: 111836208X
• ISBN13: 9781118362082
• Release Date: August 2014
• Publisher: Wiley
• Length: 256 Pages
• Weight: 1.05 lbs.
• Dimensions: 0.7" x 6.1" x 9.3"