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In explainable artificial intelligence, there is increasing interest inunderstanding the behaviour of autonomous agents to build trust and validateperformance. Modern agent architectures, such as those trained by deepreinforcement learning, are currently so lacking in interpretable structure asto effectively be black boxes, but insights may still be gained from anexternal, behaviourist perspective. Inspired by conceptual spaces theory, wesuggest that a versatile first step towards general understanding is todiscretise the state space into convex regions, jointly capturing similaritiesover the agent's action, value function and temporal dynamics within a datasetof observations. We create such a representation using a novel variant of theCART decision tree algorithm, and demonstrate how it facilitates practicalunderstanding of black box agents through prediction, visualisation andrule-based explanation.

TripleTree: A Versatile Interpretable Representation of Black Box Agents and their Environments