  Decision tree learning is a popular classification technique most commonly
used in machine learning applications. Recent work has shown that decision
trees can be used to represent provably-correct controllers concisely. Compared
to representations using lookup tables or binary decision diagrams, decision
trees are smaller and more explainable. We present dtControl, an easily
extensible tool for representing memoryless controllers as decision trees. We
give a comprehensive evaluation of various decision tree learning algorithms
applied to 10 case studies arising out of correct-by-construction controller
synthesis. These algorithms include two new techniques, one for using arbitrary
linear binary classifiers in the decision tree learning, and one novel approach
for determinizing controllers during the decision tree construction. In
particular the latter turns out to be extremely efficient, yielding decision
trees with a single-digit number of decision nodes on 5 of the case studies.
