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 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.
翻译:尽管如此,在加强学习和决策理论规划方面,大多数研究要么只假设一个单一的目标,要么通过简单的线性组合充分处理多个目标,这些方法可能过于简单化根本问题,从而产生不理想的结果。本文件是将多目标方法应用于困难问题的指南,其对象是已经熟悉单一目标强化学习和规划方法、希望对其研究采取多目标观点的研究人员,以及在实践中遇到多目标决策问题的从业人员。它指出了可能影响所希望的解决办法的性质的因素,并举例说明这些因素如何影响复杂问题的多目标决策系统的设计。