dtControl2+ε: 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 dtControl 2 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 dtControl 2 by “ε” functionality: Given an allowed imprecision ε ≥ 0, we construct a smaller decision tree, distilling the essence of the controller, while still guaranteeing its ε-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.