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Monte Carlo Tree Search Guided by Symbolic Advice for MDPs

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

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Author Details

Damien Busatto-Gaston
  • Université Libre de Bruxelles, Brussels, Belgium
Debraj Chakraborty
  • Université Libre de Bruxelles, Brussels, Belgium
Jean-Francois Raskin
  • Université Libre de Bruxelles, Brussels, Belgium

Funding

This work is partially supported by the ARC project Non-Zero Sum Game Graphs: Applications to Reactive Synthesis and Beyond (Fédération Wallonie-Bruxelles), the EOS project Verifying Learning Artificial Intelligence Systems (F.R.S.-FNRS & FWO), and the COST Action 16228 GAMENET (European Cooperation in Science and Technology).

Acknowledgements

Computational resources have been provided by the Consortium des Équipements de Calcul Intensif (CÉCI), funded by the Fonds de la Recherche Scientifique de Belgique (F.R.S.-FNRS) under Grant No. 2.5020.11.

Cite As Get BibTex

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) https://doi.org/10.4230/LIPIcs.CONCUR.2020.40

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.

Subject Classification

ACM Subject Classification
  • Theory of computation → Theory of randomized search heuristics
  • Theory of computation → Convergence and learning in games
Keywords
  • Markov decision process
  • Monte Carlo tree search
  • symbolic advice
  • simulation

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References

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