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DOI: 10.4230/LIPIcs.CONCUR.2020.40
Monte Carlo Tree Search Guided by Symbolic Advice for MDPs

Authors: Damien Busatto-Gaston, Debraj Chakraborty, and Jean-Francois Raskin

Published in: LIPIcs, Volume 171, 31st International Conference on Concurrency Theory (CONCUR 2020)

Abstract
In this paper, we consider the online computation of a strategy that aims at optimizing the expected average reward in a Markov decision process. The strategy is computed with a receding horizon and using Monte Carlo tree search (MCTS). We augment the MCTS algorithm with the notion of symbolic advice, and show that its classical theoretical guarantees are maintained. Symbolic advice are used to bias the selection and simulation strategies of MCTS. We describe how to use QBF and SAT solvers to implement symbolic advice in an efficient way. We illustrate our new algorithm using the popular game Pac-Man and show that the performances of our algorithm exceed those of plain MCTS as well as the performances of human players.
Cite as

Damien Busatto-Gaston, Debraj Chakraborty, and Jean-Francois Raskin. Monte Carlo Tree Search Guided by Symbolic Advice for MDPs. In 31st International Conference on Concurrency Theory (CONCUR 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 171, pp. 40:1-40:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{busattogaston_et_al:LIPIcs.CONCUR.2020.40,
  author =	{Busatto-Gaston, Damien and Chakraborty, Debraj and Raskin, Jean-Francois},
  title =	{{Monte Carlo Tree Search Guided by Symbolic Advice for MDPs}},
  booktitle =	{31st International Conference on Concurrency Theory (CONCUR 2020)},
  pages =	{40:1--40:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-160-3},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{171},
  editor =	{Konnov, Igor and Kov\'{a}cs, Laura},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CONCUR.2020.40},
  URN =		{urn:nbn:de:0030-drops-128523},
  doi =		{10.4230/LIPIcs.CONCUR.2020.40},
  annote =	{Keywords: Markov decision process, Monte Carlo tree search, symbolic advice, simulation}
}
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