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