Convergence to Nash equilibrium in continuous games with noisy first-order feedback

Panayotis Mertikopoulos*, Mathias Staudigl

*Corresponding author for this work

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


This paper examines the convergence of a broad class of distributed learning dynamics for games with continuous action sets. The dynamics under study comprise a multi-agent generalization of Nesterov's dual averaging (DA) method, a primal-dual mirror descent method that has recently seen a major resurgence in the field of large-scale optimization and machine learning. To account for settings with high temporal variability and uncertainty, we adopt a continuous-time formulation of dual averaging and we investigate the dynamics' long-run behavior when players have either noiseless or noisy information on their payoff gradients. In both the deterministic and stochastic regimes, we establish sublinear rates of convergence of actual and averaged trajectories to Nash equilibrium under a variational stability condition.
Original languageEnglish
Title of host publication2017 IEEE 56th Conference on Decision and Control (CDC)
Number of pages6
ISBN (Print)9781509028733
Publication statusPublished - 2017
Event56th IEEE Conference on Decision and Control - Melbourne, Australia
Duration: 12 Dec 201715 Dec 2017

Publication series

SeriesIEEE Conference on Decision and Control


Conference56th IEEE Conference on Decision and Control
Abbreviated titleIEEE CDC
Internet address




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