Year: 2016
Type: preprint
Abstract: We study a specific \textit{combinatorial pure exploration stochastic bandit problem} where the learner aims at finding the set of arms whose means are above a given threshold, up to a given precision, and \textit{for a fixed time horizon}. We propose a parameter-free algorithm based on an original heuristic, and prove that it is optimal for this problem by deriving matching upper and lower bounds. To the best of our knowledge, this is the first non-trivial pure exploration setting with \textit{fixed budget} for which optimal strategies are constructed.
Source: arXiv (Cornell University)
Authors: Locatelli, Andrea, Gutzeit, Maurilio, Carpentier, Alexandra
Institution: University of Potsdam
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
Funder: Deutsche Forschungsgemeinschaft
OpenAlex ID: W2404446105