Summarising and Comparing Agent Dynamics with Contrastive Spatiotemporal Abstraction

While such single-timestep explanations produce valuable insight, they lack any representation We introduce a data-driven, model-agnostic technique of the dynamics that differentiate control from other learning for generating a human-interpretable summary domains. A complementary direction for explaining agent of the salient points of contrast within an behaviour would be to facilitate human understanding of the evolving dynamical system, such as the learning dynamics over two timescales: (1) short-term sequences of process of a control agent. It involves the aggregation state transitions ("when you go here, what happens next?") of transition data along both spatial and and (2) long-term trends in policy evolution ("what did you temporal dimensions according to an informationtheoretic do in the past, and when, how and why did that change?").