Distributional multi-objective decision making

Willem Röpke, Conor F. Hayes, Patrick Mannion, Enda Howley, Ann Nowe, Diederik M. Roijers

Research output: Chapter in Book/Report/Conference proceedingConference paperResearch


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.
Original languageEnglish
Title of host publicationProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence
Subtitle of host publicationMain Track
PublisherInternational Joint Conferences on Artificial Intelligence
Number of pages9
Publication statusPublished - 2023


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