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As deep reinforcement learning (RL) is applied to more tasks, there is a needto visualize and understand the behavior of learned agents. Saliency mapsexplain agent behavior by highlighting the features of the input state that aremost relevant for the agent in taking an action. Existing perturbation-basedapproaches to compute saliency often highlight regions of the input that arenot relevant to the action taken by the agent. Our proposed approach, SARFA(Specific and Relevant Feature Attribution), generates more focused saliencymaps by balancing two aspects (specificity and relevance) that capturedifferent desiderata of saliency. The first captures the impact of perturbationon the relative expected reward of the action to be explained. The seconddownweighs irrelevant features that alter the relative expected rewards ofactions other than the action to be explained. We compare SARFA with existingapproaches on agents trained to play board games (Chess and Go) and Atari games(Breakout, Pong and Space Invaders). We show through illustrative examples(Chess, Atari, Go), human studies (Chess), and automated evaluation methods(Chess) that SARFA generates saliency maps that are more interpretable forhumans than existing approaches. For the code release and demo videos, seehttps://nikaashpuri.github.io/sarfa-saliency/.

Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution