English

Steering No-Regret Learners to a Desired Equilibrium

Computer Science and Game Theory 2026-03-18 v5

Abstract

A mediator observes no-regret learners playing an extensive-form game repeatedly across TT rounds. The mediator attempts to steer players toward some desirable predetermined equilibrium by giving (nonnegative) payments to players. We call this the steering problem. The steering problem captures problems several problems of interest, among them equilibrium selection and information design (persuasion). If the mediator's budget is unbounded, steering is trivial because the mediator can simply pay the players to play desirable actions. We study two bounds on the mediator's payments: a total budget and a per-round budget. If the mediator's total budget does not grow with TT, we show that steering is impossible. However, we show that it is enough for the total budget to grow sublinearly with TT, that is, for the average payment to vanish. When players' full strategies are observed at each round, we show that constant per-round budgets permit steering. In the more challenging setting where only trajectories through the game tree are observable, we show that steering is impossible with constant per-round budgets in general extensive-form games, but possible in normal-form games or if the per-round budget may itself depend on TT. We also show how our results can be generalized to the case when the equilibrium is being computed online while steering is happening. We supplement our theoretical positive results with experiments highlighting the efficacy of steering in large games.

Keywords

Cite

@article{arxiv.2306.05221,
  title  = {Steering No-Regret Learners to a Desired Equilibrium},
  author = {Brian Hu Zhang and Gabriele Farina and Ioannis Anagnostides and Federico Cacciamani and Stephen Marcus McAleer and Andreas Alexander Haupt and Andrea Celli and Nicola Gatti and Vincent Conitzer and Tuomas Sandholm},
  journal= {arXiv preprint arXiv:2306.05221},
  year   = {2026}
}