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
Keywords: Intelligent Systems & Agents, Multi-Agent Machine Learnings, Mobile Robotics, Multi-Agent Systems, Game Theoretics, Single-Agent Reinforcement Learning, Multi-Agent Q-Learning, Learning Differential Games, Learning in Robotic Swarms