Work

Scalable Verification of Markov Decision Processes

Year: 2013

Type: preprint

Abstract: Markov decision processes (MDP) are useful to model concurrent process optimisation problems, but verifying them with numerical methods is often intractable. Existing approximative approaches do not scale well and are limited to memoryless schedulers. Here we present the basis of scalable verification for MDPSs, using an O(1) memory representation of history-dependent schedulers. We thus facilitate scalable learning techniques and the use of massively parallel verification.

DOI: 10.48550/arxiv.1310.3609

Authors: Legay, Axel, Sedwards, Sean, Traonouez, Louis-Marie

Institution: Centre Inria de l'Université de Rennes

Language: English

Topic: Formal Methods in Verification

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2950144282

Cites: 25

Cited by:

Related to: 20