Multi-Agent Machine Learning: A Reinforcement Approach

Multi-Agent Machine Learning: A Reinforcement Approach
Multi-Agent Machine Learning: A Reinforcement Approach
By 作者: H. M. Schwartz
ISBN-10 书号: 111836208X
ISBN-13 书号: 9781118362082
Edition 版本: 1
Release Finelybook 出版日期: 2014-08-11
pages 页数: (256 )


Book Description to Finelybook sorting

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
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 Ganes
Chapter 6:Swarm Intelligence and the Evolution of Personality Traits
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