Qualitative Multi-Armed Bandits: A Quantile-Based Approach

Szörényi, Balázs; Busa-Fekete, Róbert; Weng, Paul und Hüllermeier, Eyke (July 2015): Qualitative Multi-Armed Bandits: A Quantile-Based Approach. 32nd International Conference on Machine Learning, Lille, France, July 6 - 11, 2015. In: Proceedings of the 32nd International Conference on Machine Learning, Vol. 37 pp. 1660-1668

Abstract

We formalize and study the multi-armed bandit (MAB) problem in a generalized stochastic setting, in which rewards are not assumed to be numerical. Instead, rewards are measured on a qualitative scale that allows for comparison but invalidates arithmetic operations such as averaging. Correspondingly, instead of characterizing an arm in terms of the mean of the underlying distribution, we opt for using a quantile of that distribution as a representative value. We address the problem of quantile-based online learning both for the case of a finite (pure exploration) and infinite time horizon (cumulative regret minimization). For both cases, we propose suitable algorithms and analyze their properties. These properties are also illustrated by means of first experimental studies.

• Item Type: Conference or Workshop Item (Paper)
• Faculties: Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning
• Subjects: 000 Computer science, information and general works > 004 Data processing computer science
• Language: English