EconPapers    
Economics at your fingertips  
 

Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems

Tatiana Tatarenko ()
Additional contact information
Tatiana Tatarenko: TU Darmstadt

in Springer Books from Springer

Date: 2017
ISBN: 978-3-319-65479-9
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Chapters in this book:

Ch Chapter 1 Introduction
Tatiana Tatarenko
Ch Chapter 2 Game Theory and Multi-Agent Optimization
Tatiana Tatarenko
Ch Chapter 3 Logit Dynamics in Potential Games with Memoryless Players
Tatiana Tatarenko
Ch Chapter 4 Stochastic Methods in Distributed Optimization and Game-Theoretic Learning
Tatiana Tatarenko
Ch Chapter 5 Conclusion
Tatiana Tatarenko

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprbok:978-3-319-65479-9

Ordering information: This item can be ordered from
http://www.springer.com/9783319654799

DOI: 10.1007/978-3-319-65479-9

Access Statistics for this book

More books in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2026-02-19
Handle: RePEc:spr:sprbok:978-3-319-65479-9