Multi-Agent Machine Learning
by Schwartz, H.
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 cover…
Read More ↓Title Statements
- Full title :Multi-Agent Machine Learning / Schwartz, H.
Summary
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.
Read More ↓Edition Statements
- Publication Date:2014.
- Edition:1st edition
Publication Statements
- Publisher(s):Wiley
- Place of Publication:Place of publication not identified
- Language:eng
- Publication Date:2014.
The Physical Object
- Description:1 online resource (256 pages)
Edition Identifiers
- ISBN:9781118362082, 111836208X
- oclc:on1228513052
Subjects
Writers
No reviews yet. Be the first to rate this work.
Related Works
more →
Short-range micro-motion sensing with radar technology
AI and Machine Learning for on-Device Development: a programmer's guide
Moroney, Laurence
Rate & Review