Multi-Agent Machine Learning: A Reinforcement Approach

Multi-Agent Machine Learning: A Reinforcement Learning Approach is a framework to understanding different methods and approaches in multi-agent machine learning. It also provides cohesive coverage of the latest advances in multi-agent differential games and presents applications in game theory and robotics.

• 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

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