Windowed A-K-MDP

Abstract:Markov decision processes (MDPs) are used to support decision-making in conservation of biodiversity, but policies, even over small state spaces, can be difficult to interpret for conservation managers. K-MDP methods address this problem by building simpler MDPs with at most K abstract states. We show that the previously proposed A-K-MDP algorithm that relies on selecting a discretisation divisor using binary search can skip better abstract states. To fix this issue, we propose Windowed A-K-MDP, an algorithm that generates every distinct feasible partition induced within a declared divisor window and evaluates candidates until reaching the ideal value loss (J = 0) or exhausting the family of candidates. Across 33 K-MDP instances, Windowed improved 25 and tied 8.

Artificial Intelligence (cs.AI)

arXiv:2609.13676 [cs.AI]

(or arXiv:2609.13676v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.13676 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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