Publication details
Symbiotic Local Search for Small Decision Tree Policies in MDPs
| Authors | |
|---|---|
| Year of publication | 2025 |
| Type | Paper in proceedings |
| Conference | Proceedings of Machine Learning Research, Volume 286: Conference on Uncertainty in Artificial Intelligence (UAI 2025) |
| MU Faculty or unit | |
| Citation | |
| web | https://proceedings.mlr.press/v286/andriushchenko25a.html |
| Keywords | Computer Science; Artificial Intelligence; Theory& Methods |
| Description | We study decision making policies in Markov decision processes (MDPs). Two key performance indicators of such policies are their value and their interpretability. On the one hand, policies that optimize value can be efficiently computed via a plethora of standard methods. However, the representation of these policies may prevent their interpretability. On the other hand, policies with good interpretability, such as policies represented by a small decision tree, are computationally hard to obtain. This paper contributes a local search approach to find policies with good value, represented by small decision trees. Our local search symbiotically combines learning decision trees from valueoptimal policies with symbolic approaches that optimize the size of the decision tree within a constrained neighborhood. Our empirical evaluation shows that this combination provides drastically smaller decision trees for MDPs that are significantly larger than what can be handled by optimal decision tree learners. |
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