A practical guide to multi-objective reinforcement learning and planning

• Published in: Autonomous agents and multi-agent systems Vol. 36; no. 1
• Main Authors: Hayes, Conor F., Rădulescu, Roxana, Bargiacchi, Eugenio, Källström, Johan, Macfarlane, Matthew, Reymond, Mathieu, Verstraeten, Timothy, Zintgraf, Luisa M., Dazeley, Richard, Heintz, Fredrik, Howley, Enda, Irissappane, Athirai A., Mannion, Patrick, Nowé, Ann, Ramos, Gabriel, Restelli, Marcello, Vamplew, Peter, Roijers, Diederik M.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2022; Springer Nature B.V
• Subjects: Artificial Intelligence; Computer Science; Computer Systems Organization and Communication Networks; Decision making; Decision theory; Learning; Multi-objective decision making; Multi-Objective Decision Making (MODeM); Multi-objective multi-agent systems; Multi-objective planning; Multi-objective reinforcement learning; Software Engineering/Programming and Operating Systems; Task complexity; User Interfaces and Human Computer Interaction; Multi-objective multi-agent systems; Multi-objective reinforcement learning; Multi-objective decision making; Multi-objective planning
• ISSN: 1387-2532; 1573-7454; 1573-7454
• DOI: 10.1007/s10458-022-09552-y

Real-world sequential decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcement learning and decision-theoretic planning either assumes only a single objective, or that multiple objectives can be adequately handled via a simple linear combination. Such approaches may oversimplify the underlying problem and hence produce suboptimal results. This paper serves as a guide to the application of multi-objective methods to difficult problems, and is aimed at researchers who are already familiar with single-objective reinforcement learning and planning methods who wish to adopt a multi-objective perspective on their research, as well as practitioners who encounter multi-objective decision problems in practice. It identifies the factors that may influence the nature of the desired solution, and illustrates by example how these influence the design of multi-objective decision-making systems for complex problems.