  Explainable reinforcement learning (XRL) is an emerging subfield of
explainable machine learning that has attracted considerable attention in
recent years. The goal of XRL is to elucidate the decision-making process of
learning agents in sequential decision-making settings. In this survey, we
propose a novel taxonomy for organizing the XRL literature that prioritizes the
RL setting. We overview techniques according to this taxonomy. We point out
gaps in the literature, which we use to motivate and outline a roadmap for
future work.
