Attention-Privileged Reinforcement Learning

Understand

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.