Follower Agnostic Learning in Stackelberg Games

In this paper, we present an efficient algorithm to solve online Stackelberg games, featuring multiple followers, in a follower-agnostic manner. Unlike previous works, our approach works even when leader has no knowledge about the followers’ utility functions, strategy space or learning algorithm. Our algorithm introduces a unique gradient estimator, leveraging specially designed strategies to probe followers. In a departure from traditional assumptions of optimal play, we model followers’ responses using a convergent adaptation rule, allowing for realistic and dynamic interactions. The leader constructs the gradient estimator solely based on observations of followers’ actions. We provide both non-asymptotic convergence rates to stationary points of the leader’s objective and demonstrate asymptotic convergence to a local Stackelberg equilibrium. To validate the effectiveness of our algorithm, we use this algorithm to solve the problem of incentive design on a largescale transportation network, showcasing its robustness even when the leader lacks access to followers’ demand information.