  Embracing the pursuit of intrinsically explainable reinforcement learning
raises crucial questions: what distinguishes explainability from
interpretability? Should explainable and interpretable agents be developed
outside of domains where transparency is imperative? What advantages do
interpretable policies offer over neural networks? How can we rigorously define
and measure interpretability in policies, without user studies? What
reinforcement learning paradigms,are the most suited to develop interpretable
agents? Can Markov Decision Processes integrate interpretable state
representations? In addition to motivate an Interpretable RL community centered
around the aforementioned questions, we propose the first venue dedicated to
Interpretable RL: the InterpPol Workshop.
