Learning pure-strategy Nash equilibria in networked multi-agent systems with uncertainty

Ceyhun Eksin, Brian Swenson, Soummya Kar, Alejandro Ribeiro

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A multi-agent system with uncertainty entails a set of agents intent on maximizing their local utility functions that depend on the actions of other agents and a state of the world while having partial and different information about actions of other agents and the state of the world. When agents repeatedly have to make decisions in these settings, we propose a general class of decision-making dynamics based on the Fictitious Play (FP) algorithm with inertia. We show convergence of the proposed algorithm to pure Nash equilibria for the class of weakly acyclic games - a structural assumption on local utility functions that guarantees existence of pure Nash equilibria - as long as the predictions of the agents of their local utilities satisfy a mild asymptotic accuracy condition. Using the results on the general dynamics, the paper proposes distributed implementations of the FP algorithm with inertia suited for networked multi-agent systems and shows its convergence to pure Nash equilibria. Numerical examples corroborate the analysis providing insights to convergence time.

Original languageEnglish (US)
Title of host publication2016 IEEE 55th Conference on Decision and Control, CDC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5292-5297
Number of pages6
ISBN (Electronic)9781509018376
DOIs
StatePublished - Dec 27 2016
Event55th IEEE Conference on Decision and Control, CDC 2016 - Las Vegas, United States
Duration: Dec 12 2016Dec 14 2016

Publication series

Name2016 IEEE 55th Conference on Decision and Control, CDC 2016

Other

Other55th IEEE Conference on Decision and Control, CDC 2016
Country/TerritoryUnited States
CityLas Vegas
Period12/12/1612/14/16

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Decision Sciences (miscellaneous)
  • Control and Optimization

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