  Imitation learning often needs a large demonstration set in order to handle
the full range of situations that an agent might find itself in during
deployment. However, collecting expert demonstrations can be expensive. Recent
work in vision, reinforcement learning, and NLP has shown that auxiliary
representation learning objectives can reduce the need for large amounts of
expensive, task-specific data. Our Empirical Investigation of Representation
Learning for Imitation (EIRLI) investigates whether similar benefits apply to
imitation learning. We propose a modular framework for constructing
representation learning algorithms, then use our framework to evaluate the
utility of representation learning for imitation across several environment
suites. In the settings we evaluate, we find that existing algorithms for
image-based representation learning provide limited value relative to a
well-tuned baseline with image augmentations. To explain this result, we
investigate differences between imitation learning and other settings where
representation learning has provided significant benefit, such as image
classification. Finally, we release a well-documented codebase which both
replicates our findings and provides a modular framework for creating new
representation learning algorithms out of reusable components.
