Monte carlo tree search for verifying reachability in markov decision processes

Pranav Ashok, Tomáš Brázdil, Jan Křetínský, Ondřej Slámečka

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

The maximum reachability probabilities in a Markov decision process can be computed using value iteration (VI). Recently, simulation-based heuristic extensions of VI have been introduced, such as bounded real-time dynamic programming (BRTDP), which often manage to avoid explicit analysis of the whole state space while preserving guarantees on the computed result. In this paper, we introduce a new class of such heuristics, based on Monte Carlo tree search (MCTS), a technique celebrated in various machine-learning settings. We provide a spectrum of algorithms ranging from MCTS to BRTDP. We evaluate these techniques and show that for larger examples, where VI is no more applicable, our techniques are more broadly applicable than BRTDP with only a minor additional overhead.

Original languageEnglish
Title of host publicationLeveraging Applications of Formal Methods, Verification and Validation. Verification - 8th International Symposium, ISoLA 2018, Proceedings
EditorsTiziana Margaria, Bernhard Steffen
PublisherSpringer Verlag
Pages322-335
Number of pages14
ISBN (Print)9783030034207
DOIs
StatePublished - 2018
Event8th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation, ISoLA 2018 - Limassol, Cyprus
Duration: 5 Nov 20189 Nov 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11245 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation, ISoLA 2018
Country/TerritoryCyprus
CityLimassol
Period5/11/189/11/18

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