PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Multi-Bandit Best Arm Identification
Victor Gabillon, Mohammad Ghavamzadeh, Alessandro Lazaric and Sebastien Bubeck
In: Twenty-Fifth Annual Conference on Advances in Neural Information Processing Systems (NIPS-2011), Granada, Spain(2011).


We study the problem of identifying the best arm in each of the bandits in a multi-bandit multi-armed setting. We first propose an algorithm called Gap-based Exploration (GapE) that focuses on the arms whose mean is close to the mean of the best arm in the same bandit (i.e., small gap). We then introduce an algorithm, called GapE-V, which takes into account the variance of the arms in addition to their gap. We prove an upper-bound on the probability of error for both algorithms. Since GapE and GapE-V need to tune an exploration parameter that depends on the complexity of the problem, which is often unknown in advance, we also introduce variations of these algorithms that estimate this complexity online. Finally, we evaluate the performance of these algorithms and compare them to other allocation strategies on a number of synthetic problems.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:9078
Deposited By:Mohammad Ghavamzadeh
Deposited On:21 February 2012