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.

通过多任务模仿学习的表征转移，可以比从零开始学习新任务更加高效地利用样本。本文提供了一个统计保证，表明在使用足够多样的源任务训练表征时，我们确实可以提高目标任务的样本利用效率。我们的理论结果可以轻松扩展到基于现实假设的常用神经网络结构。通过符合我们理论结果的实证分析，我们在四个模拟环境中得出结论：特别是更多地利用源任务的数据可以提高学习新任务的样本利用效率。