Setting the values of rewards in Markov decision processes (MDP) may be a difficult task. In this paper, we consider two ordinal decision models for MDPs where only an order is known over rewards. The first one, which has been proposed recently in MDPs [23], defines preferences with respect to a reference point. The second model, which can been viewed as the dual approach of the first one, is based on quantiles. Based on the first decision model, we give a new interpretation of rewards in standard MDPs, which sheds some interesting light on the preference system used in standard MDPs. The second model based on quantile optimization is a new approach in MDPs with ordinal rewards. Although quantile-based optimality is state-dependent, we prove that an optimal stationary deterministic policy exists for a given initial state. Finally, we propose solution methods based on linear programming for optimizing quantiles.