Multi-armed bandits with episode context

11th International Symposium on Artificial Intelligence and Mathematics, ISAIM 2010

Volumn , Issue , 2010, Pages

  Rosin, Christopher D ^a  

^a Parity Computing Inc   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER GO; CONTEXTUAL BANDITS; MULTI ARMED BANDIT; SIDE INFORMATION; WORSTCASE;

ARTIFICIAL INTELLIGENCE;

ALGORITHMS;

EID: 84874141248     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (30)

1. Audibert, J.-Y., and Bubeck, S. 2009. Minimax policies for adversarial and stochastic bandits. In COLT.
2. Audibert, J.-Y.; Munos, R.; and Szepesvári, C. 2009. Exploration-exploitation tradeoff using variance estimates in multi-armed bandits. Theor. Comput. Sci. 410(19):1876-1902.
3. Auer, P.; Cesa-Bianchi, N.; Freund, Y.; and Schapire, R. E. 2002. The nonstochastic multiarmed bandit problem. SIAM J. Comput. 32(1):48-77.
4. Auer, P.; Cesa-Bianchi, N.; and Fischer, P. 2002. Finitetime analysis of the multiarmed bandit problem. Machine Learning 47(2-3):235-256.
5. Bouzy, B., and Cazenave, T. 2001. Computer Go: An AI oriented survey. Artif. Intell. 132(1):39-103.
6. Bouzy, B., and Chaslot, G. 2005. Bayesian generation and integration of K-nearest-neighbor patterns for 19×19 Go. In IEEE Symposium on Computational Intelligence in Games, 176-181.
7. Bouzy, B., and Helmstetter, B. 2003. Monte-Carlo Go developments. In van den Herik, H. J.; Iida, H.; and Heinz, E. A., eds., ACG, volume 263 of IFIP, 159-174. Kluwer.
8. Bubeck, S.; Munos, R.; and Stoltz, G. 2009. Pure exploration in multi-armed bandits problems. In Gavaldà, R.; Lugosi, G.; Zeugmann, T.; and Zilles, S., eds., ALT, volume 5809 of Lecture Notes in Computer Science, 23-37. Springer.
9. Cai, X., and Wunsch, D. C. 2007. Computer Go: A grand challenge to AI. In Duch,W., and Mandziuk, J., eds., Challenges for Computational Intelligence, volume 63 of Studies in Computational Intelligence. Springer. 443-465.
10. Chaslot, G.; Winands, M.; Uiterwijk, J.; van den Herik, H.; and Bouzy, B. 2007. Progressive strategies for Monte-Carlo tree search. In Proceedings of the 10th Joint Conference on Information Sciences (JCIS 2007), 655-661.
11. Chaslot, G.; Chatriot, L.; Fiter, C.; Gelly, S.; Hoock, J.; Perez, J.; Rimmel, A.; and Teytaud, O. 2008. Combining expert, offline, transient and online knowledge in Monte-Carlo exploration. http://www.lri.fr/~teytaud/eg.pdf.
12. Coulom, R. 2006. Efficient selectivity and backup operators in Monte-Carlo tree search. In van den Herik, H. J.; Ciancarini, P.; and Donkers, H. H. L. M., eds., Computers and Games, volume 4630 of Lecture Notes in Computer Science, 72-83. Springer.
13. Coulom, R. 2007a. Computing Elo ratings of move patterns in the game of Go. In Computer Games Workshop.
14. Coulom, R. 2007b. Monte-Carlo tree search in Crazy Stone. In 12th Game Programming Workshop.
15. de Mesmay, F.; Rimmel, A.; Voronenko, Y.; and Püschel, M. 2009. Bandit-based optimization on graphs with application to library performance tuning. In Danyluk, A. P.; Bottou, L.; and Littman, M. L., eds., ICML, volume 382 of ACM International Conference Proceeding Series, 92. ACM.
16. Even-Dar, E.; Mannor, S.; and Mansour, Y. 2006. Action elimination and stopping conditions for the multi-armed bandit and reinforcement learning problems. Journal of Machine Learning Research 7:1079-1105.
17. Finnsson, H., and Björnsson, Y. 2008. Simulation-based approach to general game playing. In Fox and Gomes (2008), 259-264.
18. Fox, D., and Gomes, C. P., eds. 2008. Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, AAAI 2008, Chicago, Illinois, USA, July 13-17, 2008. AAAI Press.
19. Gelly, S., and Silver, D. 2007. Combining online and offline knowledge in UCT. In Ghahramani, Z., ed., ICML, volume 227 of ACM International Conference Proceeding Series, 273-280. ACM.
20. Gelly, S., and Silver, D. 2008. Achieving master level play in 9 × 9 computer Go. In Fox and Gomes (2008), 1537-1540.
21. Juditsky, A.; Nazin, A.; Tsybakov, A.; and Vayatis, N. 2008. Gap-free bounds for multi-armed stochastic bandit. In World Congr. of IFAC.
22. Kakade, S. M.; Shalev-Shwartz, S.; and Tewari, A. 2008. Efficient bandit algorithms for online multiclass prediction. In Cohen, W. W.; McCallum, A.; and Roweis, S. T., eds., ICML, volume 307 of ACM International Conference Proceeding Series, 440-447. ACM.
23. Kocsis, L., and Szepesvari, C. 2006. Bandit based Monte-Carlo planning. In ECML.
24. Langford, J., and Zhang, T. 2007. The epoch-greedy algorithm for multi-armed bandits with side information. In Platt, J. C.; Koller, D.; Singer, Y.; and Roweis, S. T., eds., NIPS. MIT Press.
25. Mannor, S., and Tsitsiklis, J. N. 2004. The sample complexity of exploration in the multi-armed bandit problem. Journal of Machine Learning Research 5:623-648.
26. Shalev-Shwartz, S.; Shamir, O.; Srebro, N.; and Sridharan, K. 2009. Stochastic convex optimization. In COLT.
27. Streeter, M. J., and Smith, S. F. 2006. A simple distribution-free approach to the max k-armed bandit problem. In Benhamou, F., ed., CP, volume 4204 of Lecture Notes in Computer Science, 560-574. Springer.
28. Strehl, A. L.; Mesterharm, C.; Littman, M. L.; and Hirsh, H. 2006. Experience-efficient learning in associative bandit problems. In Cohen, W. W., and Moore, A., eds., ICML, volume 148 of ACM International Conference Proceeding Series, 889-896. ACM.
29. Teytaud, O.; Gelly, S.; and Sebag, M. 2007. Anytime many-armed bandits. In CAP07.
30. Wang, C.-C.; Kulkarni, S.; and Poor, H. 2005. Bandit problems with side observations. IEEE Tr. Aut. Cont. 50:338-355.