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  4. Learning Nash Equilibria in Monotone Games
 
conference paper

Learning Nash Equilibria in Monotone Games

Tatarenko, Tatiana
•
Kamgarpour, Maryam  
December 2019
2019 IEEE 58th Conference on Decision and Control (CDC)
2019 IEEE 58th Conference on Decision and Control (CDC)

We consider multi-agent decision making where each agent's cost function depends on all agents' strategies. We propose a distributed algorithm to learn a Nash equilibrium, whereby each agent uses only obtained values of her cost function at each joint played action, lacking any information of the functional form of her cost or other agents' costs or strategy sets. In contrast to past work where convergent algorithms required strong monotonicity, we prove algorithm convergence under mere monotonicity assumption. This significantly widens algorithm's applicability, such as to games with linear coupling constraints.

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Type
conference paper
DOI
10.1109/CDC40024.2019.9029659
Author(s)
Tatarenko, Tatiana
Kamgarpour, Maryam  
Date Issued

2019-12

Publisher

IEEE

Publisher place

Nice, France

Published in
2019 IEEE 58th Conference on Decision and Control (CDC)
ISBN of the book

978-1-72811-398-2

Start page

3104

End page

3109

Editorial or Peer reviewed

REVIEWED

Written at

OTHER

EPFL units
SYCAMORE  
Event nameEvent placeEvent date
2019 IEEE 58th Conference on Decision and Control (CDC)

Nice, France

2019-12

Available on Infoscience
December 1, 2021
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/183316
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