  Adversarial Imitation Learning (AIL) is a broad family of imitation learning
methods designed to mimic expert behaviors from demonstrations. While AIL has
shown state-of-the-art performance on imitation learning with only small number
of demonstrations, it faces several practical challenges such as potential
training instability and implicit reward bias. To address the challenges, we
propose Support-weighted Adversarial Imitation Learning (SAIL), a general
framework that extends a given AIL algorithm with information derived from
support estimation of the expert policies. SAIL improves the quality of the
reinforcement signals by weighing the adversarial reward with a confidence
score from support estimation of the expert policy. We also show that SAIL is
always at least as efficient as the underlying AIL algorithm that SAIL uses for
learning the adversarial reward. Empirically, we show that the proposed method
achieves better performance and training stability than baseline methods on a
wide range of benchmark control tasks.
