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Progressive Strategies For Monte-Carlo Tree Search

Author

Listed:
  • GUILLAUME M. J-B. CHASLOT

    (MICC-IKAT, Games and AI Group, Faculty of Humanities and Sciences, Universiteit Maastricht, P.O. Box 616, 6200 MD Maastricht, The Netherlands)

  • MARK H. M. WINANDS

    (MICC-IKAT, Games and AI Group, Faculty of Humanities and Sciences, Universiteit Maastricht, P.O. Box 616, 6200 MD Maastricht, The Netherlands)

  • H. JAAP VAN DEN HERIK

    (MICC-IKAT, Games and AI Group, Faculty of Humanities and Sciences, Universiteit Maastricht, P.O. Box 616, 6200 MD Maastricht, The Netherlands)

  • JOS W. H. M. UITERWIJK

    (MICC-IKAT, Games and AI Group, Faculty of Humanities and Sciences, Universiteit Maastricht, P.O. Box 616, 6200 MD Maastricht, The Netherlands)

  • BRUNO BOUZY

    (Centre de Recherche en Informatique de Paris 5, Université Paris 5 Descartes, 45, rue des Saints Pères, 75270 Cedex 06, France)

Abstract

Monte-Carlo Tree Search (MCTS) is a new best-first search guided by the results of Monte-Carlo simulations. In this article, we introduce twoprogressive strategiesfor MCTS, called progressive bias and progressive unpruning. They enable the use of relatively time-expensive heuristic knowledge without speed reduction. Progressive bias directs the search according to heuristic knowledge. Progressive unpruning first reduces the branching factor, and then increases it gradually again. Experiments assess that the two progressive strategies significantly improve the level of our Go programMango. Moreover, we see that the combination of both strategies performs even better on larger board sizes.

Suggested Citation

  • Guillaume M. J-B. Chaslot & Mark H. M. Winands & H. Jaap Van Den Herik & Jos W. H. M. Uiterwijk & Bruno Bouzy, 2008. "Progressive Strategies For Monte-Carlo Tree Search," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 4(03), pages 343-357.
    • Handle: RePEc:wsi:nmncxx:v:04:y:2008:i:03:n:s1793005708001094
    DOI: 10.1142/S1793005708001094
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    Cited by:

    1. Afc{s}ar Onat Ayd{i}nhan & Xiaoyue Li & John M. Mulvey, 2022. "Solving Multi-Period Financial Planning Models: Combining Monte Carlo Tree Search and Neural Networks," Papers 2202.07734, arXiv.org, revised May 2022.
    2. Cullen, Andrew C. & Alpcan, Tansu & Kalloniatis, Alexander C., 2022. "Adversarial decisions on complex dynamical systems using game theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).

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