Multi-Agent Machine Learning

Multi-Agent Machine Learning
Author :
Publisher :
Total Pages : 256
Release :
ISBN-10 : OCLC:1137164992
ISBN-13 :
Rating : 4/5 ( Downloads)

Book Synopsis Multi-Agent Machine Learning by : H. Schwartz

Download or read book Multi-Agent Machine Learning written by H. Schwartz and published by . This book was released on 2014 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.


Multi-Agent Machine Learning Related Books

Multi-Agent Machine Learning
Language: en
Pages: 256
Authors: H. Schwartz
Categories:
Type: BOOK - Published: 2014 - Publisher:

DOWNLOAD EBOOK

The book begins with a chapter on traditional methods of supervised learning, covering recursive least squares learning, mean square error methods, and stochast
Layered Learning in Multiagent Systems
Language: en
Pages: 300
Authors: Peter Stone
Categories: Computers
Type: BOOK - Published: 2000-03-03 - Publisher: MIT Press

DOWNLOAD EBOOK

This book looks at multiagent systems that consist of teams of autonomous agents acting in real-time, noisy, collaborative, and adversarial environments. This b
Multi-Agent Machine Learning
Language: en
Pages: 273
Authors: H. M. Schwartz
Categories: Technology & Engineering
Type: BOOK - Published: 2014-08-26 - Publisher: John Wiley & Sons

DOWNLOAD EBOOK

The book begins with a chapter on traditional methods of supervised learning, covering recursive least squares learning, mean square error methods, and stochast
Multi-Agent Coordination
Language: en
Pages: 320
Authors: Arup Kumar Sadhu
Categories: Computers
Type: BOOK - Published: 2020-12-01 - Publisher: John Wiley & Sons

DOWNLOAD EBOOK

Discover the latest developments in multi-robot coordination techniques with this insightful and original resource Multi-Agent Coordination: A Reinforcement Lea
Readings in Agents
Language: en
Pages: 552
Authors: Michael N. Huhns
Categories: Computers
Type: BOOK - Published: 1998 - Publisher: Morgan Kaufmann

DOWNLOAD EBOOK

This book collects the most significant literature on agents in an attempt top forge a broad foundation for the field. Includes papers from the perspectives of