  We propose Deep Optimistic Linear Support Learning (DOL) to solve
high-dimensional multi-objective decision problems where the relative
importances of the objectives are not known a priori. Using features from the
high-dimensional inputs, DOL computes the convex coverage set containing all
potential optimal solutions of the convex combinations of the objectives. To
our knowledge, this is the first time that deep reinforcement learning has
succeeded in learning multi-objective policies. In addition, we provide a
testbed with two experiments to be used as a benchmark for deep multi-objective
reinforcement learning.
