Optimal Decision Tree Policies for Markov Decision Processes

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