A practical guide to multi-objective reinforcement learning and planning - 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.; et al. (Autonomous agents and multi-agent systems, 2022)
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.