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The Multi-armed bandit offer the advantage to learn and exploit the alreadylearnt knowledge at the same time. This capability allows this approach to beapplied in different domains, going from clinical trials where the goal isinvestigating the effects of different experimental treatments while minimizingpatient losses, to adaptive routing where the goal is to minimize the delays ina network. This article provides a review of the recent results on applyingbandit to real-life scenario and summarize the state of the art for each ofthese fields. Different techniques has been proposed to solve this problemsetting, like epsilon-greedy, Upper confident bound (UCB) and Thompson Sampling(TS). We are showing here how this algorithms were adapted to solve thedifferent problems of exploration exploitation.

Etat de l'art sur l'application des bandits multi-bras