Fast routing under uncertainty: Adaptive learning in congestion games with exponential weights - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year :

Fast routing under uncertainty: Adaptive learning in congestion games with exponential weights

Abstract

We examine an adaptive learning framework for nonatomic congestion games where the players' cost functions may be subject to exogenous fluctuations (e.g., due to disturbances in the network, variations in the traffic going through a link). In this setting, the popular multiplicative/ exponential weights algorithm enjoys an $\mathcal{O}(1/\sqrt{T})$ equilibrium convergence rate; however, this rate is suboptimal in static environments – i.e., when the network is not subject to randomness. In this static regime, accelerated algorithms achieve an $\mathcal{O}(1/T^{2})$ convergence speed, but they fail to converge altogether in stochastic problems. To fill this gap, we propose a novel, adaptive exponential weights method – dubbed AdaWeight – that seamlessly interpolates between the $\mathcal{O}(1/T^{2})$ and $\mathcal{O}(1/\sqrt{T})$ rates in the static and stochastic regimes respectively. Importantly, this "best-of-both-worlds" guarantee does not require any prior knowledge of the problem's parameters or tuning by the optimizer; in addition, the method's convergence speed depends subquadratically on the size of the network (number of vertices and edges), so it scales gracefully to large, real-life urban networks.
Fichier principal
Vignette du fichier
AdaWeight.pdf (1.64 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03357716 , version 1 (29-09-2021)

Identifiers

  • HAL Id : hal-03357716 , version 1

Cite

Dong Quan Vu, Kimon Antonakopoulos, Panayotis Mertikopoulos. Fast routing under uncertainty: Adaptive learning in congestion games with exponential weights. NeurIPS 2021 - 35th International Conference on Neural Information Processing Systems, Dec 2021, Virtual, Unknown Region. pp.1-36. ⟨hal-03357716⟩
182 View
88 Download

Share

Gmail Facebook Twitter LinkedIn More