  Visual attention serves as a means of feature selection mechanism in the
perceptual system. Motivated by Broadbent's leaky filter model of selective
attention, we evaluate how such mechanism could be implemented and affect the
learning process of deep reinforcement learning. We visualize and analyze the
feature maps of DQN on a toy problem Catch, and propose an approach to combine
visual selective attention with deep reinforcement learning. We experiment with
optical flow-based attention and A2C on Atari games. Experiment results show
that visual selective attention could lead to improvements in terms of sample
efficiency on tested games. An intriguing relation between attention and batch
normalization is also discovered.
