  We propose a new approach to visualize saliency maps for deep neural network
models and apply it to deep reinforcement learning agents trained on Atari
environments. Our method adds an attention module that we call FLS (Free Lunch
Saliency) to the feature extractor from an established baseline (Mnih et al.,
2015). This addition results in a trainable model that can produce saliency
maps, i.e., visualizations of the importance of different parts of the input
for the agent's current decision making. We show experimentally that a network
with an FLS module exhibits performance similar to the baseline (i.e., it is
"free", with no performance cost) and can be used as a drop-in replacement for
reinforcement learning agents. We also design another feature extractor that
scores slightly lower but provides higher-fidelity visualizations. In addition
to attained scores, we report saliency metrics evaluated on the Atari-HEAD
dataset of human gameplay.
