Online optimization in Χ-armed bandits

Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference

Volumn , Issue , 2009, Pages 201-208

  Bubeck, Sébastien ^a   Munos, Rémi ^a   Stoltz, Gilles ^b   Szepesvári, Csaba ^c  

^a INRIA   (France)

^b ECOLE NORMALE SUPÉRIEURE   (France)

^c UNIVERSITY OF ALBERTA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

GLOBAL OPTIMIZATION;

BANDIT PROBLEMS; DISSIMILARITY FUNCTION; GENERALISATION; LIPSCHITZ; MEAN PAYOFF; ONLINE OPTIMIZATION; PAYOFF FUNCTION; SELECTION POLICIES; STOCHASTICS; TOPOLOGICAL SPACES;

STOCHASTIC SYSTEMS;

EID: 77952027689     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (8)

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2. P. Auer, R. Ortner, and Cs. Szepesvári. Improved rates for the stochastic continuum-armed bandit problem. 20th Conference on Learning Theory, pages 454-468, 2007.
3. E. Cope. Regret and convergence bounds for immediate-reward reinforcement learning with continuous action spaces. Preprint, 2004.
4. P.-A. Coquelin and R. Munos. Bandit algorithms for tree search. In Proceedings of 23rd Conference on Uncertainty in Artificial Intelligence, 2007.
5. S. Gelly, Y. Wang, R. Munos, and O. Teytaud. Modification of UCT with patterns in Monte-Carlo go. Technical Report RR-6062, INRIA, 2006.
6. R. Kleinberg. Nearly tight bounds for the continuum-armed bandit problem. In 18th Advances in Neural Information Processing Systems, 2004.
7. R. Kleinberg, A. Slivkins, and E. Upfal. Multi-armed bandits in metric spaces. In Proceedings of the 40th ACM Symposium on Theory of Computing, 2008.
8. L. Kocsis and Cs. Szepesvári. Bandit based Monte-Carlo planning. In Proceedings of the 15th European Conference on Machine Learning, pages 282-293, 2006. (Pubitemid 44618839)