  Over the past few years, the multi-armed bandit model has become increasingly
popular in the machine learning community, partly because of applications
including online content optimization. This paper reviews two different
sequential learning tasks that have been considered in the bandit literature ;
they can be formulated as (sequentially) learning which distribution has the
highest mean among a set of distributions, with some constraints on the
learning process. For both of them (regret minimization and best arm
identification) we present recent, asymptotically optimal algorithms. We
compare the behaviors of the sampling rule of each algorithm as well as the
complexity terms associated to each problem.
