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Multi-Agent Machine Learning
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Description
Description| Publisher | John Wiley & Sons Inc (US) |
| Author(s) | H. M. Schwartz |
| Subtitle | A Reinforcement Approach |
| Edition | 1 |
| Published | 1st August 2014 |
| Related course codes |
A Reinforcement Approach
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
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