  Transferring representation for multitask imitation learning has the
potential to provide improved sample efficiency on learning new tasks, when
compared to learning from scratch. In this work, we provide a statistical
guarantee indicating that we can indeed achieve improved sample efficiency on
the target task when a representation is trained using sufficiently diverse
source tasks. Our theoretical results can be readily extended to account for
commonly used neural network architectures with realistic assumptions. We
conduct empirical analyses that align with our theoretical findings on four
simulated environments$\unicode{x2014}$in particular leveraging more data from
source tasks can improve sample efficiency on learning in the new task.
