Finding Safe Zones of policies Markov Decision Processes

• URL: http://arxiv.org/abs/2202.11593v2
• Date: Mon, 9 Oct 2023 17:48:32 GMT
• Title: Finding Safe Zones of policies Markov Decision Processes
• Authors: Lee Cohen, Yishay Mansour, Michal Moshkovitz
• Abstract summary: Given a policy of a 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.
• Score: 53.93816835129937
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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