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

Learning to predict where to look in interactive environments using deep recurrent q-learning