Our goal is to learn a classifier that can accurately mimic the oracle's choices on previously unseen data.
at test time, when we run the learned policy classifier, the learned policy's state distribution may deviate from the optimal policy's state distribution due to imperfect imitation, arriving in states not on the oracle's path.