  Imitation learning (IL) algorithms use expert demonstrations to learn a
specific task. Most of the existing approaches assume that all expert
demonstrations are reliable and trustworthy, but what if there exist some
adversarial demonstrations among the given data-set? This may result in poor
decision-making performance. We propose a novel general frame-work to directly
generate a policy from demonstrations that autonomously detect the adversarial
demonstrations and exclude them from the data set. At the same time, it's
sample, time-efficient, and does not require a simulator. To model such
adversarial demonstration we propose a min-max problem that leverages the
entropy of the model to assign weights for each demonstration. This allows us
to learn the behavior using only the correct demonstrations or a mixture of
correct demonstrations.
