dtControl2+$\varepsilon$: Trading Optimality for Explainability in MDPs via Decision Trees

Abstract:Over the past decade, decision trees have been used to represent controllers (a.k.a. policies) in an explainable way, with dtControl2 as a current state-of-the-art tool. However, for systems that are large or have many corner cases, even such representations tend to be too complex and not human-comprehensible. Unfortunately, reducing the size of the decision tree is not straightforward, as missing just a single crucial case might result in an incorrect controller. We tackle this issue in the setting of Markov decision processes, extending dtControl2 by "$\varepsilon$" functionality: Given an allowed imprecision $\varepsilon \geq 0$, we construct a smaller decision tree, distilling the essence of the controller, while still guaranteeing its $\varepsilon$-optimality. This enables us to provide tunably simpler explanations, omitting a controllable amount of detail. Our tool constructs decision trees that are orders of magnitude smaller than the state of the art.

Artificial Intelligence (cs.AI)

arXiv:2607.25925 [cs.AI]

(or arXiv:2607.25925v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.25925 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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