Tug of Peace: Distributed Learning for Quality of Service Guarantees

Siddharth Chandak, Ilai Bistritz, Nicholas Bambos

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Consider N players, where the action of player n is a number in the interval [0,\ Bn] that is interpreted as its 'pull'. Each player has a reward function that depends on all actions. We define Tug-of-War (ToW) games where increasing the action of one player decreases the rewards of all others. Tug-of-War games can model networking scenarios such as transmission power control and activation in sensor networks. We propose Tug-of-Peace algorithm, a simple stochastic approximation, and prove that in Tug-of-War games, it converges to a equilibrium that satisfies a target feasible Quality of Service reward vector for the players. Moreover, with high probability it converges to the 'minimal pull' equilibrium. Our algorithm uses infrequent 1-bit communication between the players, but we also propose a fully distributed modification that does not require any communication at all and achieves almost the same guarantees. We then simulate our algorithms in the power control and sensor activation scenarios.

Original languageEnglish
Title of host publication2023 62nd IEEE Conference on Decision and Control, CDC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2346-2351
Number of pages6
ISBN (Electronic)9798350301243
DOIs
StatePublished - 2023
Event62nd IEEE Conference on Decision and Control, CDC 2023 - Singapore, Singapore
Duration: 13 Dec 202315 Dec 2023

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370

Conference

Conference62nd IEEE Conference on Decision and Control, CDC 2023
Country/TerritorySingapore
CitySingapore
Period13/12/2315/12/23

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