A practical guide to multi-objective reinforcement learning and planning

Real-world 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 simple linear combination. Such approaches may oversimplify underlying problem hence produce suboptimal results. This paper serves as guide to application multi-objective methods difficult problems, is aimed at researchers who already familiar with single-objective wish adopt perspective on their research, well practitioners encounter decision problems practice. It identifies factors influence nature desired solution, illustrates by example how these design systems for complex problems.