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About this paper
Alex Barbier-Chebbah, Christian L. Vestergaard, Jean-Baptiste Masson
AIM approximates information maximization in two-arm Bernoulli and Gaussian bandits by analytically estimating which pull most reduces uncertainty about the maximum reward, avoiding numerical integration of the exact objective. Simulations report logarithmic mean-regret growth and long-time performance matching Thompson sampling. Results are limited to tested settings; the asymptotic argument assumes the best arm gets most pulls and is not entirely rigorous.
Summary by alphaXiv.
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