  We study the problem of training a principal in a multi-agent general-sum
game using reinforcement learning (RL). Learning a robust principal policy
requires anticipating the worst possible strategic responses of other agents,
which is generally NP-hard. However, we show that no-regret dynamics can
identify these worst-case responses in poly-time in smooth games. We propose a
framework that uses this policy evaluation method for efficiently learning a
robust principal policy using RL. This framework can be extended to provide
robustness to boundedly rational agents too. Our motivating application is
automated mechanism design: we empirically demonstrate our framework learns
robust mechanisms in both matrix games and complex spatiotemporal games. In
particular, we learn a dynamic tax policy that improves the welfare of a
simulated trade-and-barter economy by 15%, even when facing previously unseen
boundedly rational RL taxpayers.
