  We propose Generative Predecessor Models for Imitation Learning (GPRIL), a
novel imitation learning algorithm that matches the state-action distribution
to the distribution observed in expert demonstrations, using generative models
to reason probabilistically about alternative histories of demonstrated states.
We show that this approach allows an agent to learn robust policies using only
a small number of expert demonstrations and self-supervised interactions with
the environment. We derive this approach from first principles and compare it
empirically to a state-of-the-art imitation learning method, showing that it
outperforms or matches its performance on two simulated robot manipulation
tasks and demonstrate significantly higher sample efficiency by applying the
algorithm on a real robot.
