# Convergence Rate of Payoff-based Generalized Nash Equilibrium Learning

arXiv: https://arxiv.org/abs/2411.08595 · HTML: https://mlsanity.com/paper/2411.08595/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2024.

## Built on by (most cited first, 1 of 1)

- [Nash Equilibria in Games with Playerwise Concave Coupling Constraints: Existence and Computation](https://mlsanity.com/paper/2509.14032/) (2025)

## Abstract (arXiv)

We consider generalized Nash equilibrium (GNE) problems in games with strongly monotone pseudo-gradients and jointly linear coupling constraints. We establish the convergence rate of a payoff-based approach intended to learn a variational GNE (v-GNE) in such games. While convergent algorithms have recently been proposed in this setting given full or partial information of the gradients, rate of convergence in the payoff-based information setting has been an open problem. Leveraging properties of a game extended from the original one by a dual player, we establish a convergence rate of O(frac1t4/7) to a v-GNE of the game.