TripleTree: A Versatile Interpretable Representation of Black Box Agents
  and their Environments
  In explainable artificial intelligence, there is increasing interest in
understanding the behaviour of autonomous agents to build trust and validate
performance. Modern agent architectures, such as those trained by deep
reinforcement learning, are currently so lacking in interpretable structure as
to effectively be black boxes, but insights may still be gained from an
external, behaviourist perspective. Inspired by conceptual spaces theory, we
suggest that a versatile first step towards general understanding is to
discretise the state space into convex regions, jointly capturing similarities
over the agent's action, value function and temporal dynamics within a dataset
of observations. We create such a representation using a novel variant of the
CART decision tree algorithm, and demonstrate how it facilitates practical
understanding of black box agents through prediction, visualisation and
rule-based explanation.
