The parallelization of monte-carlo planning parallelization of MC-planning

ICINCO 2008 - Proceedings of the 5th International Conference on Informatics in Control, Automation and Robotics

Volumn ICSO, Issue , 2008, Pages 244-249

  Gelly, S ^a   Hoock, J B ^a   Rimmel, A ^a   Teytaud, O ^a   Kalemkarian, Y ^b  

^a UFR 919 Laboratoire d'Informatique Pour la Mécanique et les Sciences de l'Ingénieur   (France)

^b High Performance Calculus   (France)

Author keywords

Bandit based monte carlo planning; Message passing; Multi core machines; Parallelization

Indexed keywords

BANDIT-BASED MONTE-CARLO PLANNING; MONTE CARLO; MULTI CORE; PARALLELIZATIONS; TREE-SEARCH;

ROBOTICS;

MESSAGE PASSING;

EID: 55849142990     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. Agrawal, R. (1995). The continuum-armed bandit problem. SIAMJ. Control Optim., 33(6):1926-1951.
2. Auer, P., Cesa-Bianchi, N., and Gentile, C. (2001). Adaptive and self-confident on-line learning algorithms. Machine Learning Journal.
3. Banks, J. S. and Sundaram, R. K. (1992). Denumerable-armed bandits. Econometrica, 60(5): 1071-96. Available at http://ideas.repec.Org/a/ecm/ emetrp/v60yl992i5pl071-96.html.
4. Barto, A., Bradtke, S., and Singh, S. (1993). Learning to act using real-time dynamic programming. Technical Report UM-CS-1993-002.
5. Bellman, R. (1957). Dynamic Programming. Princeton Univ. Press.
6. Berry, D. A., Chen, R. W., Zame, A., Heath, D. C, and Shepp, L. A. (1997). Bandit problems with infinitely many arms. Ann. Statist., 25(5):2103-2116.
7. Bertsekas, D. (1995). Dynamic Programming and Optimal Control, vols I and II. Athena Scientific.
8. Bruegmann, B. (1993). Monte carlo go. Unpublished.
9. Cazenave, T. and Helmstetter, B. (2005). Combining tactical search and monte-carlo in the game of go. IEEE CIG2005, pages 171-175.
10. Coquelin, P.-A. and Munos, R. (2007). Bandit algorithms for tree search. In Proceedings of UAI'07.
11. Coulom, R. (2006). Efficient selectivity and backup operators in monte-carlo tree search. In P. Ciancarini and H. J. van den Herik, editors, Proceedings of the 5th International Conference on Computers and Games, Turin, Italy.
12. Coulom, R. (2007). Computing elo ratings of move patterns in the game of go. In van den Herik, H. J., Uiterwijk, J. W. H. M., Winands, M., and Schadd, M., editors, Computer Games Workshop, Amsterdam.
13. Dani, V. and Hayes, T. P. (2006). Robbing the bandit: less regret in online geometric optimization against an adaptive adversary. In SODA '06: Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm, pages 937-943, New York, NY, USA. ACM Press.
14. Gelly, S. and Silver, D. (2007). Combining online and offline knowledge in uct. In ICML '07: Proceedings of the 24th international conference on Machine learning, pages 273-280, New York, NY, USA. ACM Press.
15. Hussain, Z., Auer, P., Cesa-Bianchi, N., Newnham, L., and Shawe-Taylor, J. (2006). Exploration vs. exploitation challenge. Pascal Network of Excellence.
16. Kocsis, L. and Szepesvari, C. (2005). Reduced-variance payoff estimation in adversarial bandit problems. In Proceedings of the ECML-2005 Workshop on Reinforcement Learning in Non-Stationary Environments.
17. Kocsis, L. and Szepesvari, C. (2006a). Bandit-based monte-carlo planning. ECML '06.
18. Kocsis, L. and Szepesvari, C. (2006b). Discounted-ucb. In 2nd Pascal-Challenge Workshop.
19. Lai, T. and Robbins, H. (1985). Asymptotically efficient adaptive allocation rules. Advances in Applied Mathematics, 6:4-22.
20. Powell, W.-B. (2007). Approximate Dynamic Programming. Wiley.
21. Wang, Y. and Gelly, S. (2007). Modifications of UCT and sequence-like simulations for Monte-Carlo Go. In IEEE Symposium on Computational Intelligence and Games, Honolulu, Hawaii, pages 175-182.