  We study how a principal can efficiently and effectively intervene on the
rewards of a previously unseen learning agent in order to induce desirable
outcomes. This is relevant to many real-world settings like auctions or
taxation, where the principal may not know the learning behavior nor the
rewards of real people. Moreover, the principal should be few-shot adaptable
and minimize the number of interventions, because interventions are often
costly. We introduce MERMAIDE, a model-based meta-learning framework to train a
principal that can quickly adapt to out-of-distribution agents with different
learning strategies and reward functions. We validate this approach
step-by-step. First, in a Stackelberg setting with a best-response agent, we
show that meta-learning enables quick convergence to the theoretically known
Stackelberg equilibrium at test time, although noisy observations severely
increase the sample complexity. We then show that our model-based meta-learning
approach is cost-effective in intervening on bandit agents with unseen
explore-exploit strategies. Finally, we outperform baselines that use either
meta-learning or agent behavior modeling, in both $0$-shot and $K=1$-shot
settings with partial agent information.
