Feb. 15, 2024, 5:43 a.m. | Dani\"el Vos, Sicco Verwer

cs.LG updates on arXiv.org arxiv.org

arXiv:2301.13185v2 Announce Type: replace-cross
Abstract: Interpretability of reinforcement learning policies is essential for many real-world tasks but learning such interpretable policies is a hard problem. Particularly rule-based policies such as decision trees and rules lists are difficult to optimize due to their non-differentiability. While existing techniques can learn verifiable decision tree policies there is no guarantee that the learners generate a decision that performs optimally. In this work, we study the optimization of size-limited decision trees for Markov Decision Processes …

abstract arxiv cs.ai cs.lg decision decision trees interpretability learn lists markov processes reinforcement reinforcement learning rules tasks tree trees type world

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