Feel-Good Thompson Sampling for Contextual Dueling Bandits

Understand

Contextual dueling bandits, where a learner compares two options based on context and receives feedback indicating which was preferred, extends classic dueling bandits by incorporating contextual information for decision-making and preference learning.

• Several algorithms based on the upper confidence bound (UCB) have been proposed for linear contextual dueling bandits.
• However, no algorithm based on posterior sampling has been developed in this setting, despite the empirical success observed in traditional contextual bandits.
• In this paper, we propose a Thompson sampling algorithm, named FGTS.CDB, for linear contextual dueling bandits.