Solving MDPs with LTLf+ and PPLTL+ Temporal Objectives

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Main Authors: De Giacomo, Giuseppe, Li, Yong, Schewe, Sven, Weinhuber, Christoph, Yu, Pian
Format: Preprint
Published: 2025
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author De Giacomo, Giuseppe
Li, Yong
Schewe, Sven
Weinhuber, Christoph
Yu, Pian
author_facet De Giacomo, Giuseppe
Li, Yong
Schewe, Sven
Weinhuber, Christoph
Yu, Pian
contents The temporal logics LTLf+ and PPLTL+ have recently been proposed to express objectives over infinite traces. These logics are appealing because they match the expressive power of LTL on infinite traces while enabling efficient DFA-based techniques, which have been crucial to the scalability of reactive synthesis and adversarial planning in LTLf and PPLTL over finite traces. In this paper, we demonstrate that these logics are also highly effective in the context of MDPs. Introducing a technique tailored for probabilistic systems, we leverage the benefits of efficient DFA-based methods and compositionality. This approach is simpler than its non-probabilistic counterparts in reactive synthesis and adversarial planning, as it accommodates a controlled form of nondeterminism (``good for MDPs") in the automata when transitioning from finite to infinite traces. Notably, by exploiting compositionality, our solution is both implementation-friendly and well-suited for straightforward symbolic implementations.
format Preprint
id arxiv_https___arxiv_org_abs_2505_17264
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Solving MDPs with LTLf+ and PPLTL+ Temporal Objectives
De Giacomo, Giuseppe
Li, Yong
Schewe, Sven
Weinhuber, Christoph
Yu, Pian
Formal Languages and Automata Theory
Logic in Computer Science
The temporal logics LTLf+ and PPLTL+ have recently been proposed to express objectives over infinite traces. These logics are appealing because they match the expressive power of LTL on infinite traces while enabling efficient DFA-based techniques, which have been crucial to the scalability of reactive synthesis and adversarial planning in LTLf and PPLTL over finite traces. In this paper, we demonstrate that these logics are also highly effective in the context of MDPs. Introducing a technique tailored for probabilistic systems, we leverage the benefits of efficient DFA-based methods and compositionality. This approach is simpler than its non-probabilistic counterparts in reactive synthesis and adversarial planning, as it accommodates a controlled form of nondeterminism (``good for MDPs") in the automata when transitioning from finite to infinite traces. Notably, by exploiting compositionality, our solution is both implementation-friendly and well-suited for straightforward symbolic implementations.
title Solving MDPs with LTLf+ and PPLTL+ Temporal Objectives
topic Formal Languages and Automata Theory
Logic in Computer Science
url https://arxiv.org/abs/2505.17264