  Deep reinforcement learning (RL) algorithms are powerful tools for solving
visuomotor decision tasks. However, the trained models are often difficult to
interpret, because they are represented as end-to-end deep neural networks. In
this paper, we shed light on the inner workings of such trained models by
analyzing the pixels that they attend to during task execution, and comparing
them with the pixels attended to by humans executing the same tasks. To this
end, we investigate the following two questions that, to the best of our
knowledge, have not been previously studied. 1) How similar are the visual
representations learned by RL agents and humans when performing the same task?
and, 2) How do similarities and differences in these learned representations
explain RL agents' performance on these tasks? Specifically, we compare the
saliency maps of RL agents against visual attention models of human experts
when learning to play Atari games. Further, we analyze how hyperparameters of
the deep RL algorithm affect the learned representations and saliency maps of
the trained agents. The insights provided have the potential to inform novel
algorithms for closing the performance gap between human experts and RL agents.
