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arXiv · 2305.06743

Implicitly normalized forecaster with clipping for linear and non-linear heavy-tailed multi-armed bandits

Yuriy Dorn·Nikita Kornilov·Nikolay Kutuzov·Alexander Nazin·Eduard Gorbunov·Alexander Gasnikov
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Abstract

The Implicitly Normalized Forecaster (INF) algorithm is considered to be an optimal solution for adversarial multi-armed bandit (MAB) problems. However, most of the existing complexity results for INF rely on restrictive assumptions, such as bounded rewards. Recently, a related algorithm was proposed that works for both adversarial and stochastic heavy-tailed MAB settings. However, this algorithm fails to fully exploit the available data. In this paper, we propose a new version of INF called the Implicitly Normalized Forecaster with clipping (INF-clip) for MAB problems with heavy-tailed reward distributions. We establish convergence results under mild assumptions on the rewards distribution and demonstrate that INF-clip is optimal for linear heavy-tailed stochastic MAB problems and works well for non-linear ones. Furthermore, we show that INF-clip outperforms the best-of-both-worlds algorithm in cases where it is difficult to distinguish between different arms.

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Yuriy Dorn, Nikita Kornilov, Nikolay Kutuzov, Alexander Nazin, Eduard Gorbunov, Alexander Gasnikov. 2023-05-11. Implicitly normalized forecaster with clipping for linear and non-linear heavy-tailed multi-armed bandits. https://arxiv.org/abs/2305.06743

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