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October 19, 2025Open Access

Convergence Rate of Generalized Nash Equilibrium Learning in Strongly Monotone Games with Linear Constraints

Key Points

The study presents a convergent procedure for generalized Nash equilibrium learning in strongly monotone games.
It reveals the convergence rate of iterates when using one- and two-point payoff-based feedback methods.
Uniquely, it characterizes the speed of convergence within the context of games with linear constraints.
The findings enhance understanding of learning dynamics in multi-agent systems under specific conditions.

Abstract

We consider payoff-based learning of a generalized Nash equilibrium (GNE) in multi-agent systems. Our focus is on games with jointly convex constraints of a linear structure and strongly monotone pseudo-gradients. We present a convergent procedure based on a partial regularization technique and establish the convergence rate of its iterates under one- and two-point payoff-based feedback. To the best of our knowledge, this work is the first one characterizing the convergence speed of iterates to a variational GNE in the class of games under consideration.

Convergence Rate of Generalized Nash Equilibrium Learning in Strongly Monotone Games with Linear Constraints

Key Points

Abstract

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