  In the Markov decision process model, policies are usually evaluated by
expected cumulative rewards. As this decision criterion is not always suitable,
we propose in this paper an algorithm for computing a policy optimal for the
quantile criterion. Both finite and infinite horizons are considered. Finally
we experimentally evaluate our approach on random MDPs and on a data center
control problem.
