  We study the inverse optimal control problem in social sciences: we aim at
learning a user's true cost function from the observed temporal behavior. In
contrast to traditional phenomenological works that aim to learn a generative
model to fit the behavioral data, we propose a novel variational principle and
treat user as a reinforcement learning algorithm, which acts by optimizing his
cost function. We first propose a unified KL framework that generalizes
existing maximum entropy inverse optimal control methods. We further propose a
two-step Wasserstein inverse optimal control framework. In the first step, we
compute the optimal measure with a novel mass transport equation. In the second
step, we formulate the learning problem as a generative adversarial network. In
two real world experiments - recommender systems and social networks, we show
that our framework obtains significant performance gains over both existing
inverse optimal control methods and point process based generative models.
