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Conference papers
Optimal Best Arm Identification with Fixed Confidence
Abstract : We give a complete characterization of the complexity of best-arm identification in one-parameter bandit problems. We prove a new, tight lower bound on the sample complexity. We propose the `Track-and-Stop' strategy, which we prove to be asymptotically optimal. It consists in a new sampling rule (which tracks the optimal proportions of arm draws highlighted by the lower bound) and in a stopping rule named after Chernoff, for which we give a new analysis.
Cited literature [29 references]
https://hal.archives-ouvertes.fr/hal-01273838
Contributor : Aurélien Garivier <>
Submitted on : Wednesday, June 1, 2016 - 2:25:34 PM
Last modification on : Monday, December 14, 2020 - 5:20:12 PM
Long-term archiving on: : Friday, September 2, 2016 - 10:27:56 AM
Contributor : Aurélien Garivier <>
Submitted on : Wednesday, June 1, 2016 - 2:25:34 PM
Last modification on : Monday, December 14, 2020 - 5:20:12 PM
Long-term archiving on: : Friday, September 2, 2016 - 10:27:56 AM
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