Distributional Multi-Objective Decision Making
This provides a new approach for decision support in real-world problems with conflicting objectives, though it appears incremental by building on existing multi-objective methods.
The paper tackles the problem of decision support in multi-objective scenarios by introducing a distributional dominance criterion to identify optimal policies, showing that it captures policies ignored by traditional Pareto fronts and proving it includes all policies maximizing expected utility for risk-averse decision makers.
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.