  We formulate the problem of learning to imitate multiple, non-deterministic
teachers with minimal interaction cost. Rather than learning a specific policy
as in standard imitation learning, the goal in this problem is to learn a
distribution over a policy space. We first present a general framework that
efficiently models and estimates such a distribution by learning continuous
representations of the teacher policies. Next, we develop Active
Performance-Based Imitation Learning (APIL), an active learning algorithm for
reducing the learner-teacher interaction cost in this framework. By making
query decisions based on predictions of future progress, our algorithm avoids
the pitfalls of traditional uncertainty-based approaches in the face of teacher
behavioral uncertainty. Results on both toy and photo-realistic navigation
tasks show that APIL significantly reduces the numbers of interactions with
teachers without compromising on performance. Moreover, it is robust to various
degrees of teacher behavioral uncertainty.
