Monte Carlo Tree Search for Verifying Reachability in Markov
Decision Processes

D 2018

Monte Carlo Tree Search for Verifying Reachability in Markov Decision Processes

ASHOK, Pranav, Tomáš BRÁZDIL, Jan KŘETÍNSKÝ and Ondřej SLÁMEČKA

Basic information

Original name

Monte Carlo Tree Search for Verifying Reachability in Markov Decision Processes

Authors

ASHOK, Pranav (356 India), Tomáš BRÁZDIL (203 Czech Republic, belonging to the institution), Jan KŘETÍNSKÝ (203 Czech Republic, guarantor, belonging to the institution) and Ondřej SLÁMEČKA (203 Czech Republic, belonging to the institution)

Edition

Cham, Leveraging Applications of Formal Methods, Verification and Validation (ISoLA 2018), p. 322-335, 14 pp. 2018

Publisher

Springer

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Switzerland

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

printed version "print"

Impact factor

Impact factor: 0.402 in 2005

RIV identification code

RIV/00216224:14330/18:00108292

Organization unit

Faculty of Informatics

ISBN

978-3-030-03420-7

ISSN

DOI

http://dx.doi.org/10.1007/978-3-030-03421-4_21

Keywords in English

Monte Carlo Tree Search; Reachability; Markov Decision Processes

Změněno: 28/4/2020 07:54, RNDr. Pavel Šmerk, Ph.D.

Abstract

V originále

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.

Links

GA18-11193S, research and development project

Name: Algoritmy pro diskrétní systémy a hry s nekonečně mnoha stavy

Investor: Czech Science Foundation

Citovat

ASHOK, Pranav, Tomáš BRÁZDIL, Jan KŘETÍNSKÝ and Ondřej SLÁMEČKA. Monte Carlo Tree Search for Verifying Reachability in Markov Decision Processes. In Leveraging Applications of Formal Methods, Verification and Validation (ISoLA 2018). Cham: Springer, 2018, p. 322-335. ISBN 978-3-030-03420-7. Available from: https://dx.doi.org/10.1007/978-3-030-03421-4_21.

@inproceedings{1649045,
   author = {Ashok, Pranav and Brázdil, Tomáš and Křetínský, Jan and Slámečka, Ondřej},
   address = {Cham},
   booktitle = {Leveraging Applications of Formal Methods, Verification and Validation (ISoLA 2018)},
   doi = {http://dx.doi.org/10.1007/978-3-030-03421-4_21},
   keywords = {Monte Carlo Tree Search; Reachability; Markov Decision Processes},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Cham},
   isbn = {978-3-030-03420-7},
   pages = {322-335},
   publisher = {Springer},
   title = {Monte Carlo Tree Search for Verifying Reachability in Markov Decision Processes},
   year = {2018}
}

TY  - JOUR
ID  - 1649045
AU  - Ashok, Pranav - Brázdil, Tomáš - Křetínský, Jan - Slámečka, Ondřej
PY  - 2018
TI  - Monte Carlo Tree Search for Verifying Reachability in Markov Decision Processes
PB  - Springer
CY  - Cham
SN  - 9783030034207
KW  - Monte Carlo Tree Search
KW  - Reachability
KW  - Markov Decision Processes
N2  - 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.
ER  -

ASHOK, Pranav, Tomáš BRÁZDIL, Jan KŘETÍNSKÝ and Ondřej SLÁMEČKA. Monte Carlo Tree Search for Verifying Reachability in Markov Decision Processes. In \textit{Leveraging Applications of Formal Methods, Verification and Validation (ISoLA 2018)}. Cham: Springer, 2018, p.~322-335. ISBN~978-3-030-03420-7. Available from: https://dx.doi.org/10.1007/978-3-030-03421-4\_{}21.

Detailed Information on Publication Record