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arXiv · 2202.11593

Finding Safe Zones of policies Markov Decision Processes

Lee Cohen·Yishay Mansour·Michal Moshkovitz
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Abstract

Given a policy of a Markov Decision Process, we define a SafeZone as a subset of states, such that most of the policy's trajectories are confined to this subset. The quality of a SafeZone is parameterized by the number of states and the escape probability, i.e., the probability that a random trajectory will leave the subset. SafeZones are especially interesting when they have a small number of states and low escape probability. We study the complexity of finding optimal SafeZones, and show that in general, the problem is computationally hard. Our main result is a bi-criteria approximation learning algorithm with a factor of almost $2$ approximation for both the escape probability and SafeZone size, using a polynomial size sample complexity.

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Lee Cohen, Yishay Mansour, Michal Moshkovitz. 2022-02-23. Finding Safe Zones of policies Markov Decision Processes. https://arxiv.org/abs/2202.11593

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