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One major barrier to applications of deep Reinforcement Learning (RL) bothinside and outside of games is the lack of explainability. In this paper, wedescribe a lightweight and effective method to derive explanations for deep RLagents, which we evaluate in the Atari domain. Our method relies on atransformation of the pixel-based input of the RL agent to an interpretable,percept-like input representation. We then train a surrogate model, which isitself interpretable, to replicate the behavior of the target, deep RL agent.Our experiments demonstrate that we can learn an effective surrogate thataccurately approximates the underlying decision making of a target agent on asuite of Atari games.

Explaining Deep Reinforcement Learning Agents In The Atari Domain through a Surrogate Model