  We present a novel form of explanation for Reinforcement Learning, based
around the notion of intended outcome. These explanations describe the outcome
an agent is trying to achieve by its actions. We provide a simple proof that
general methods for post-hoc explanations of this nature are impossible in
traditional reinforcement learning. Rather, the information needed for the
explanations must be collected in conjunction with training the agent. We
derive approaches designed to extract local explanations based on intention for
several variants of Q-function approximation and prove consistency between the
explanations and the Q-values learned. We demonstrate our method on multiple
reinforcement learning problems, and provide code to help researchers
introspecting their RL environments and algorithms.
