  For effective decision support in scenarios with conflicting objectives, sets
of potentially optimal solutions can be presented to the decision maker. We
explore both what policies these sets should contain and how such sets can be
computed efficiently. With this in mind, we take a distributional approach and
introduce a novel dominance criterion relating return distributions of policies
directly. Based on this criterion, we present the distributional undominated
set and show that it contains optimal policies otherwise ignored by the Pareto
front. In addition, we propose the convex distributional undominated set and
prove that it comprises all policies that maximise expected utility for
multivariate risk-averse decision makers. We propose a novel algorithm to learn
the distributional undominated set and further contribute pruning operators to
reduce the set to the convex distributional undominated set. Through
experiments, we demonstrate the feasibility and effectiveness of these methods,
making this a valuable new approach for decision support in real-world
problems.
