Leveraging Machine-Learned Advice in Strategic Interactions with No-Regret Learners

2026-06-08

AI Summary

This study investigates how to effectively leverage potentially imperfect machine learning advice to enhance strategic performance in repeated games against no-regret learners. By introducing a pseudometric that quantifies the quality of advice, the work systematically analyzes two types of recommendations—simulators and payoff matrix predictors—in two-player repeated settings. The theoretical results demonstrate that when advice comes with correctness guarantees, an approximate Stackelberg strategy can be computed efficiently; without such guarantees, it is impossible to simultaneously achieve near-optimality and no regret, though weakly dominant utilities can still be attained within certain (coarse) correlated equilibria. This work establishes the first theoretical limits on the use of unverified advice and reveals that high-quality advice can substantially reduce interaction complexity.

Abstract

We study how an agent in a two-player repeated game can effectively utilize potentially imperfect advice when interacting with a no-regret learner. We characterize the advice landscape by introducing a pseudo-metric to quantify the usefulness of an advice instance. We demonstrate the pseudo-metric's applicability through two forms of advice: simulators and payoff matrix predictions. We then show how an optimizing player, equipped with correctness guarantees on the advice, could leverage simulators to compute approximate Stackelberg strategies more efficiently, reducing the interaction complexity traditionally required and illustrating the power of good advice. Finally, we extend our analysis to settings where the advice does not have any guarantee of correctness. We find that, in general, a player cannot simultaneously guarantee near Stackelberg performance when the advice is approximately accurate and a no-regret condition when the advice is inaccurate. We do show, however, that it is possible for an advice-aided player to weakly dominate their utility in some (coarse)-correlated equilibria.

Problem

• no-regret learning
• Stackelberg equilibrium
• machine-learned advice
• repeated games
• advice reliability

Innovation

• no-regret learning
• Stackelberg strategy
• machine-learned advice
• pseudo-metric
• repeated games