Advertisement

Distributed Nested Rollout Policy for SameGame

  • Benjamin Negrevergne
  • Tristan Cazenave
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 818)

Abstract

Nested Rollout Policy Adaptation (NRPA) is a Monte Carlo search heuristic for puzzles and other optimization problems. It achieves state-of-the-art performance on several games including SameGame. In this paper, we design several parallel and distributed NRPA-based search techniques, and we provide a number of experimental insights about their execution. Finally, we use our best implementation to discover 15 better scores for 20 standard SameGame boards.

Notes

Acknowledgments

Experiments presented in this paper were carried out using the Grid’5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several universities as well as other organizations (see https://www.grid5000.fr).

References

  1. 1.
    Biedl, T.C., Demaine, E.D., Demaine, M.L., Fleischer, R., Jacobsen, L., Munro, J.I.: The complexity of clickomania. In: More Games No Chance, vol. 42, pp. 389–404 (2002)Google Scholar
  2. 2.
    Breuker, D.M.: Memory versus search in games. Ph.D. thesis, Universiteit Maastricht, The Netherlands (1998)Google Scholar
  3. 3.
    Cazenave, T.: Nested Monte-Carlo search. In: Boutilier, C. (ed.) IJCAI, pp. 456–461 (2009)Google Scholar
  4. 4.
    Cazenave, T.: Playout policy adaptation with move features. Theor. Comput. Sci. 644, 43–52 (2016)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Cazenave, T., Teytaud, F.: Application of the nested rollout policy adaptation algorithm to the traveling salesman problem with time windows. In: Hamadi, Y., Schoenauer, M. (eds.) LION 2012. LNCS, pp. 42–54. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-34413-8_4 CrossRefGoogle Scholar
  6. 6.
    Edelkamp, S., Cazenave, T.: Improved diversity in nested rollout policy adaptation. In: Friedrich, G., Helmert, M., Wotawa, F. (eds.) KI 2016. LNCS (LNAI), vol. 9904, pp. 43–55. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46073-4_4 CrossRefGoogle Scholar
  7. 7.
    Edelkamp, S., Gath, M., Cazenave, T., Teytaud, F.: Algorithm and knowledge engineering for the TSPTW problem. In: 2013 IEEE Symposium on Computational Intelligence in Scheduling (SCIS), pp. 44–51. IEEE (2013)Google Scholar
  8. 8.
    Edelkamp, S., Gath, M., Greulich, C., Humann, M., Herzog, O., Lawo, M.: Monte-Carlo tree search for logistics. In: Clausen, U., Friedrich, H., Thaller, C., Geiger, C. (eds.) Commercial Transport. LNL, pp. 427–440. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-21266-1_28 CrossRefGoogle Scholar
  9. 9.
    Edelkamp, S., Gath, M., Rohde, M.: Monte-Carlo tree search for 3D packing with object orientation. In: Lutz, C., Thielscher, M. (eds.) KI 2014. LNCS (LNAI), vol. 8736, pp. 285–296. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-11206-0_28 Google Scholar
  10. 10.
    Edelkamp, S., Greulich, C.: Solving physical traveling salesman problems with policy adaptation. In: 2014 IEEE Conference on Computational Intelligence and Games (CIG), pp. 1–8. IEEE (2014)Google Scholar
  11. 11.
    Edelkamp, S., Tang, Z.: Monte-Carlo tree search for the multiple sequence alignment problem. In: Eighth Annual Symposium on Combinatorial Search (2015)Google Scholar
  12. 12.
    Graf, T., Platzner, M.: Adaptive playouts in Monte-Carlo Tree search with policy-gradient reinforcement learning. In: Plaat, A., van den Herik, J., Kosters, W. (eds.) ACG 2015. LNCS, vol. 9525, pp. 1–11. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-27992-3_1 CrossRefGoogle Scholar
  13. 13.
    Rimmel, A., Teytaud, F., Cazenave, T.: Optimization of the nested Monte-Carlo algorithm on the traveling salesman problem with time windows. In: Di Chio, C., et al. (eds.) EvoApplications 2011. LNCS, vol. 6625, pp. 501–510. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-20520-0_51 CrossRefGoogle Scholar
  14. 14.
    Rosin, C.D.: Nested rollout policy adaptation for Monte Carlo Tree Search. In: IJCAI, pp. 649–654 (2011)Google Scholar
  15. 15.
    Zobrist, A.L.: A new hashing method with application for game playing. ICCA J. 13(2), 69–73 (1990)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.PSL Université Paris-Dauphine, LAMSADE UMR CNRS 7243Paris Cedex 16France

Personalised recommendations