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# Profitable Bandits

*Proceedings of The 10th Asian Conference on Machine Learning*, PMLR 95:694-709, 2018.

#### Abstract

Originally motivated by default risk management applications, this paper investigates a novel problem, referred to as the \emph{profitable bandit problem} here. At each step, an agent chooses a subset of the $K\geq 1$ possible actions. For each action chosen, she then respectively pays and receives the sum of a random number of costs and rewards. Her objective is to maximize her cumulated profit. We adapt and study three well-known strategies in this purpose, that were proved to be most efficient in other settings: \textsc{kl-UCB}, \textsc{Bayes-UCB} and \textsc{Thompson Sampling}. For each of them, we prove a finite time regret bound which, together with a lower bound we obtain as well, establishes asymptotic optimality in some cases. Our goal is also to \emph{compare} these three strategies from a theoretical and empirical perspective both at the same time. We give simple, self-contained proofs that emphasize their similarities, as well as their differences. While both Bayesian strategies are automatically adapted to the geometry of information, the numerical experiments carried out show a slight advantage for \textsc{Thompson Sampling} in practice.