Algebraically Explainable Controllers: Decision Trees and Support Vector Machines Join Forces. (arXiv:2208.12804v1 [cs.LG])

Recently, decision trees (DT) have been used as an explainable representation
of controllers (a.k.a. strategies, policies, schedulers). Although they are
often very efficient and produce small and understandable controllers for
discrete systems, complex continuous dynamics still pose a challenge. In
particular, when the relationships between variables take more complex forms,
such as polynomials, they cannot be obtained using the available DT learning
procedures. In contrast, support vector machines provide a more powerful
representation, capable of discovering many such relationships, but …

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