Sequential decision-making problems with multiple objectives arise naturally
in practice and pose unique challenges for research in decision-theoretic
planning and learning, which has largely focused on single-objective settings.
This article surveys algorithms designed for sequential decision-making
problems with multiple objectives. Though there is a growing body of literature
on this subject, little of it makes explicit under what circumstances special
methods are needed to solve multi-objective problems. Therefore, we identify
three distinct scenarios in which converting such a problem to a
single-objective one is impossible, infeasible, or undesirable. Furthermore, we
propose a taxonomy that classifies multi-objective methods according to the
applicable scenario, the nature of the scalarization function (which projects
multi-objective values to scalar ones), and the type of policies considered. We
show how these factors determine the nature of an optimal solution, which can
be a single policy, a convex hull, or a Pareto front. Using this taxonomy, we
survey the literature on multi-objective methods for planning and learning.
Finally, we discuss key applications of such methods and outline opportunities
for future work.

本文调查了设计用于带有多个目标函数的顺序决策问题的算法。通过分类法，将对多目标方法的文献研究按适用的设定情况、标量化函数的属性和考虑的策略类型进行分类。总之，文章总结了关键应用和未来研究机会。