Multi-Agent Machine Learning: A Reinforcement Approach
Authors: H. M. Schwartz
ISBN-10: 111836208X
ISBN-13: 9781118362082
Edition 版次: 1
Publication Date 出版日期: 2014-08-11
Print Length 页数: 256 pages
Book Description
By finelybook
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
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 Ganes
Chapter 6: Swarm Intelligence and the Evolution of Personality Traits
Index
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