  Image-based Reinforcement Learning is known to suffer from poor sample
efficiency and generalisation to unseen visuals such as distractors
(task-independent aspects of the observation space). Visual domain
randomisation encourages transfer by training over visual factors of variation
that may be encountered in the target domain. This increases learning
complexity, can negatively impact learning rate and performance, and requires
knowledge of potential variations during deployment. In this paper, we
introduce Attention-Privileged Reinforcement Learning (APRiL) which uses a
self-supervised attention mechanism to significantly alleviate these drawbacks:
by focusing on task-relevant aspects of the observations, attention provides
robustness to distractors as well as significantly increased learning
efficiency. APRiL trains two attention-augmented actor-critic agents: one
purely based on image observations, available across training and transfer
domains; and one with access to privileged information (such as environment
states) available only during training. Experience is shared between both
agents and their attention mechanisms are aligned. The image-based policy can
then be deployed without access to privileged information. We experimentally
demonstrate accelerated and more robust learning on a diverse set of domains,
leading to improved final performance for environments both within and outside
the training distribution.
