Coordinated online learning for multiagent systems with coupled constraints and perturbed utility observations

Ezra Tampubolon, Holger Boche

Publikation: Beitrag in FachzeitschriftArtikelBegutachtung

1 Zitat (Scopus)

Abstract

Competitive noncooperative online decision-making agents whose actions increase congestion of scarce resources constitute a model for widespread modern large-scale applications. To ensure sustainable resource behavior, we introduce a novel method to steer the agents toward a stable population state, fulfilling the given coupled resource constraints. The proposed method is a decentralized resource pricing method based on the resource loads resulting from the augmentation of the game's Lagrangian. Assuming that the online learning agents have only noisy first-order utility feedback, we show that for a polynomially decaying agents step size/learning rate, the population's dynamic will almost surely converge to generalized Nash equilibrium. A particular consequence of the latter is the fulfillment of resource constraints in the asymptotic limit. Moreover, we investigate the finite-time quality of the proposed algorithm by giving a nonasymptotic time decaying bound for the expected amount of resource constraint violation.

OriginalspracheEnglisch
Seiten (von - bis)5080-5095
Seitenumfang16
FachzeitschriftIEEE Transactions on Automatic Control
Jahrgang66
Ausgabenummer11
DOIs
PublikationsstatusVeröffentlicht - 1 Nov. 2021

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