arXiv Open Access 2017

A minimax and asymptotically optimal algorithm for stochastic bandits

Pierre Ménard Aurélien Garivier

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Abstrak

We propose the kl-UCB ++ algorithm for regret minimization in stochastic bandit models with exponential families of distributions. We prove that it is simultaneously asymptotically optimal (in the sense of Lai and Robbins' lower bound) and minimax optimal. This is the first algorithm proved to enjoy these two properties at the same time. This work thus merges two different lines of research with simple and clear proofs.

Topik & Kata Kunci

stat.ML cs.LG math.ST

Penulis (2)

Pierre Ménard

Aurélien Garivier

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Ménard, P., Garivier, A. (2017). A minimax and asymptotically optimal algorithm for stochastic bandits. https://arxiv.org/abs/1702.07211

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Informasi Jurnal

Tahun Terbit: 2017
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓