  Bottom-Up (BU) saliency models do not perform well in complex interactive
environments where humans are actively engaged in tasks (e.g., sandwich making
and playing the video games). In this paper, we leverage Reinforcement Learning
(RL) to highlight task-relevant locations of input frames. We propose a soft
attention mechanism combined with the Deep Q-Network (DQN) model to teach an RL
agent how to play a game and where to look by focusing on the most pertinent
parts of its visual input. Our evaluations on several Atari 2600 games show
that the soft attention based model could predict fixation locations
significantly better than bottom-up models such as Itti-Kochs saliency and
Graph-Based Visual Saliency (GBVS) models.
