  Robustness to adversarial perturbations has been explored in many areas of
computer vision. This robustness is particularly relevant in vision-based
reinforcement learning, as the actions of autonomous agents might be
safety-critic or impactful in the real world. We investigate the susceptibility
of vision-based reinforcement learning agents to gradient-based adversarial
attacks and evaluate a potential defense. We observe that Bottleneck Attention
Modules (BAM) included in CNN architectures can act as potential tools to
increase robustness against adversarial attacks. We show how learned attention
maps can be used to recover activations of a convolutional layer by restricting
the spatial activations to salient regions. Across a number of RL environments,
BAM-enhanced architectures show increased robustness during inference. Finally,
we discuss potential future research directions.
