Multi-armed bandit problems are challenging sequential decision problems that have been widely studied as they constitute a mathematical framework that abstracts many different decision problems in fields such as machine learning, logistics, industrial optimization, management of clinical trials, etc. In this paper we address a non stationary environment with expected rewards that are dynamically evolving, considering a particular type of drift, that we call resets, in which the arm qualities are re-initialized from time to time. We compare different arm selection strategies with simulations, focusing on a Bayesian method based on Thompson sampling (a simple, yet effective, technique for trading off between exploration and exploitation).