HIGHLIGHTS
- who: LEONAR L. CUSTODE et al. from the Department of Information Engineering and Computer Science, University of Trento, Via Sommarive, Povo (Trento), Italy have published the research work: Evolutionary learning of interpretable decision trees, in the Journal: (JOURNAL)
- what: The authors propose a novel optimization approach to interpretable RL that builds decision trees. The authors propose a two-level optimization approach that optimizes both the topology of the tree and the decisions made for each state. Please note that in the Supplementary Material the authors provide further details on this comparison, and perform an ablation . . .
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