Symbiotic Local Search for Small Decision Tree Policies in MDPs

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F25%3A00143671" target="_blank" >RIV/00216224:14330/25:00143671 - isvavai.cz</a>

Výsledek na webu

<a href="https://proceedings.mlr.press/v286/andriushchenko25a.html" target="_blank" >https://proceedings.mlr.press/v286/andriushchenko25a.html</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Symbiotic Local Search for Small Decision Tree Policies in MDPs

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Symbiotic Local Search for Small Decision Tree Policies in MDPs

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10200 - Computer and information sciences

Projekt

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Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of Machine Learning Research, Volume 286: Conference on Uncertainty in Artificial Intelligence (UAI 2025)

ISBN

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ISSN

2640-3498

e-ISSN

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Počet stran výsledku

17

Strana od-do

132-148

Název nakladatele

ML Research Press

Místo vydání

Rio de Janeiro, Brazil

Místo konání akce

Rio de Janeiro, Brazil

Datum konání akce

1. 1. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001592914500007