Learning a Game by Paying the Agents
Brian Hu Zhang, Tao Lin, Yiling Chen, Tuomas Sandholm
Abstract
We study the problem of learning the utility functions of no-regret learning agents in a repeated normal-form game. Differing from most prior literature, we introduce a principal with the power to observe the agents playing the game, send agents signals, and give agents *payments* as a function of their actions. We show that the principal can, using a number of rounds polynomial in the size of the game, learn the utility functions of all agents to any desired precision $\varepsilon > 0$, for any no-regret learning algorithms of the agents. Our main technique is to formulate a zero-sum game between the principal and the agents, where the principal's strategy space is the set of all payment functions. Finally, we discuss implications for the problem of *steering* agents to a desired equilibrium: in particular, we introduce, using our utility-learning algorithm as a subroutine, the first algorithm for steering arbitrary no-regret learning agents without prior knowledge of their utilities.
BibTeX
@inproceedings{
zhang2026learning,
title={Learning a Game by Paying the Agents},
author={Brian Hu Zhang and Tao Lin and Yiling Chen and Tuomas Sandholm},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=8yRtP2n8OK}
}