We consider the problem of efficiently learning to play single-leader multi-follower Stackelberg games when the leader lacks knowledge of the lower-level game. Such games arise in hierarchical decision-making problems involving self-interested agents. For example, in electric ride-hailing markets, a central authority aims to learn optimal charging prices to shape fleet distributions and charging patterns of ride-hailing companies. Existing works typically apply gradient-based methods to find the leader's optimal strategy. Such methods are impractical as they require that the followers share private utility information with the leader. Instead, we treat the lower-level game as a black box, assuming only that the followers' interactions approximate a Nash equilibrium while the leader observes the realized cost of the resulting approximation. Under kernel-based regularity assumptions on the leader's cost function, we develop a no-regret algorithm that converges to an -Stackelberg equilibrium in rounds. Finally, we validate our approach through a numerical case study on optimal pricing in electric ride-hailing markets.
View on arXiv@article{maddux2025_2504.03745,
title={ No-Regret Learning in Stackelberg Games with an Application to Electric Ride-Hailing },
author={ Anna Maddux and Marko Maljkovic and Nikolas Geroliminis and Maryam Kamgarpour },
journal={arXiv preprint arXiv:2504.03745},
year={ 2025 }
}