Multi-Agent Machine Learning

A Reinforcement Approach

H. M. Schwartz

• Publisher: Wiley
• Year: 2014
• Print ISBN: 9781118362082
• eBook ISBN: 9781118884485
• Edition: 1
• Topic: Tecnología e ingeniería
• Subtopic: Ingeniería eléctrica y telecomunicaciones

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

Table of contents

• Cover
• Table of Contents
• Title
• Copyright
• Preface
• Chapter 1: A Brief Review of Supervised Learning
• Chapter 2: Single-Agent Reinforcement Learning
• Chapter 3: Learning in Two-Player Matrix Games
• Chapter 4: Learning in Multiplayer Stochastic Games
• Chapter 5: Differential Games
• Chapter 6: Swarm Intelligence and the Evolution of Personality Traits
• Index
• End User License Agreement