Multi-Agent Machine Learning (ePub)
A Reinforcement Approach
(Sprache: Englisch)
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...
supervised learning, covering recursive least squares learning,
mean square error methods, and stochastic approximation. Chapter 2
covers single agent reinforcement learning. Topics include...
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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
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
Autoren-Porträt von H. M. Schwartz
Howard M. Schwartz, PhD, received his B.Eng. Degree from McGill University, Montreal, Canada in une 1981 and his MS Degree and PhD Degree from MIT, Cambridge, USA in 1982 and 1987 respectively. He is currently a professor in systems and computer engineering at Carleton University, Canada. His research interests include adaptive and intelligent control systems, robotic, artificial intelligence, system modelling, system identification, and state estimation.
Bibliographische Angaben
- Autor: H. M. Schwartz
- 2014, 1. Auflage, 256 Seiten, Englisch
- Verlag: John Wiley & Sons
- ISBN-10: 1118884485
- ISBN-13: 9781118884485
- Erscheinungsdatum: 26.08.2014
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
eBook Informationen
- Dateiformat: ePub
- Größe: 12 MB
- Mit Kopierschutz
Sprache:
Englisch
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