In many real-world problems, one needs to care about multiple objectives. These objectives can be contradicting and, depending on the decision maker, the different compromises will be ranked differently. In this preliminary work, we propose a novel algorithm: Pareto-DQN, that will estimate the Pareto front of complex environment, with a high-dimensional state-space. As a proof-of-concept, we successfully apply our algorithm to the Deep-Sea-Treasure environment, a well known Multi-objective reinforcement learning benchmark.