  The aim in imitation learning is to learn effective policies by utilizing
near-optimal expert demonstrations. However, high-quality demonstrations from
human experts can be expensive to obtain in large numbers. On the other hand,
it is often much easier to obtain large quantities of suboptimal or
task-agnostic trajectories, which are not useful for direct imitation, but can
nevertheless provide insight into the dynamical structure of the environment,
showing what could be done in the environment even if not what should be done.
We ask the question, is it possible to utilize such suboptimal offline datasets
to facilitate provably improved downstream imitation learning? In this work, we
answer this question affirmatively and present training objectives that use
offline datasets to learn a factored transition model whose structure enables
the extraction of a latent action space. Our theoretical analysis shows that
the learned latent action space can boost the sample-efficiency of downstream
imitation learning, effectively reducing the need for large near-optimal expert
datasets through the use of auxiliary non-expert data. To learn the latent
action space in practice, we propose TRAIL (Transition-Reparametrized Actions
for Imitation Learning), an algorithm that learns an energy-based transition
model contrastively, and uses the transition model to reparametrize the action
space for sample-efficient imitation learning. We evaluate the practicality of
our objective through experiments on a set of navigation and locomotion tasks.
Our results verify the benefits suggested by our theory and show that TRAIL is
able to improve baseline imitation learning by up to 4x in performance.
