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