We consider the problem of the identification of Blackwell optimal policies for deterministic finite Markov Decision Processes (d-MDPs). Specifically, we are interested in algorithms that learn reward distributions by querying samples over time, that stop almost surely and return a Blackwell optimal policy with high probability. We provide a characterization of the class of MDPs over which such algorithms exist together with an algorithm identifying Blackwell optimal policies with arbitrarly high probability.