A standard model that arises in several applications in sequential decision making is partially observable Markov decision processes (POMDPs) where a decision-making agent interacts with an uncertain environment. A basic objective in such POMDPs is the reachability objective, where given a target set of states, the goal is to eventually arrive at one of them. The limit-sure problem asks whether reachability can be ensured with probability arbitrarily close to 1. In general, the limit-sure reachability problem for POMDPs is undecidable. However, in many practical cases the most relevant question is the existence of policies with a small amount of memory. In this work, we study the limit-sure reachability problem for POMDPs with a fixed amount of memory. We establish that the computational complexity of the problem is NP-complete.
@article{arxiv.2412.00941,
title = {Limit-sure reachability for small memory policies in POMDPs is NP-complete},
author = {Ali Asadi and Krishnendu Chatterjee and Raimundo Saona and Ali Shafiee},
journal= {arXiv preprint arXiv:2412.00941},
year = {2025}
}