Distributional Multi-Objective Decision Making

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.

Authors

• Willem Röpke
• Conor F. Hayes
• Patrick Mannion University of Galway
• Enda Howley
• Ann Nowé
• Diederik M. Roijers

Keywords

• Machine Learning: ML: Reinforcement learning
• Uncertainty in AI: UAI: Other
• Uncertainty in AI: UAI: Sequential decision making