The Batch Complexity of Bandit Pure Exploration

Adrienne Tuynman, Rémy Degenne

Keywords: Bandits, Best arm identification, batched

Abstract:

In a fixed-confidence pure exploration problem in stochastic multi-armed bandits, an algorithm iteratively samples arms and should stop as early as possible and return the correct answer to a query about the arms distributions.
We are interested in batched methods, which change their sampling behaviour only a few times, between batches of observations.
We give an instance-dependent lower bound on the number of batches used by any sample efficient algorithm for any pure exploration task.
We then give a general batched algorithm and prove upper bounds on its expected sample complexity and batch complexity.

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Track: Fast Track: published work

Publication Link: [e-mail]

Submission Number: 35