Practical Evaluation and Optimization of Contextual Bandit Algorithms

Abstract : We study and empirically optimize contextual bandit learning, exploration, and problem encodings across 500+ datasets, creating a reference for practitioners and discovering or reinforcing a number of natural open problems for researchers. Across these experiments we show that minimizing the amount of exploration is a key design goal for practical performance. Remarkably, many problems can be solved purely via the implicit exploration imposed by the diversity of contexts. For practitioners, we introduce a number of practical improvements to common exploration algorithms including Bootstrap Thompson sampling, Online Cover, and $\epsilon$-greedy. We also detail a new form of reduction to regression for learning from exploration data. Overall, this is a thorough study and review of contextual bandit methodology.
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Pré-publication, Document de travail
2018
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https://hal.inria.fr/hal-01708310
Contributeur : Alberto Bietti <>
Soumis le : mardi 13 février 2018 - 15:08:17
Dernière modification le : mercredi 11 avril 2018 - 01:59:15

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  • HAL Id : hal-01708310, version 1
  • ARXIV : 1802.04064

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Alberto Bietti, Alekh Agarwal, John Langford. Practical Evaluation and Optimization of Contextual Bandit Algorithms. 2018. 〈hal-01708310〉

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