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arXiv · 1904.11573

B\"uchi Objectives in Countable MDPs

Stefan Kiefer·Richard Mayr·Mahsa Shirmohammadi·Patrick Totzke
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Abstract

We study countably infinite Markov decision processes with B\"uchi objectives, which ask to visit a given subset of states infinitely often. A question left open by T.P. Hill in 1979 is whether there always exist $\varepsilon$-optimal Markov strategies, i.e., strategies that base decisions only on the current state and the number of steps taken so far. We provide a negative answer to this question by constructing a non-trivial counterexample. On the other hand, we show that Markov strategies with only 1 bit of extra memory are sufficient.

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BibTeXRIS

Stefan Kiefer, Richard Mayr, Mahsa Shirmohammadi, Patrick Totzke. 2019-04-25. B\"uchi Objectives in Countable MDPs. https://arxiv.org/abs/1904.11573

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