  We introduce a data-driven, model-agnostic technique for generating a
human-interpretable summary of the salient points of contrast within an
evolving dynamical system, such as the learning process of a control agent. It
involves the aggregation of transition data along both spatial and temporal
dimensions according to an information-theoretic divergence measure. A
practical algorithm is outlined for continuous state spaces, and deployed to
summarise the learning histories of deep reinforcement learning agents with the
aid of graphical and textual communication methods. We expect our method to be
complementary to existing techniques in the realm of agent interpretability.
