So far the flow of knowledge has primarily been from conventional single-objective RL (SORL) into MORL, with algorithmic Research in multi-objective reinforcement learning(MORL) has introduced innovations from SORL being adapted to the context of multiple the utility-based paradigm, which makes use of both environmental objectives [2, 6, 22, 34]. This paper runs counter to that trend, rewards and a function that defines the utility derived as we will argue that the utility-based paradigm which has been bytheuser from thoserewards. Inthis paperweextend this paradigm widely adopted in MORL [5, 13, 21], has both relevance and benefits to the context of single-objective reinforcement learning(RL), to SORL. We present a general framework for utility-based RL and outline multiple potential benefits including the ability to perform (UBRL), which unifies the SORL and MORL frameworks, and discuss multi-policy learning across tasks relating to uncertain objectives, benefits and potential applications of this for single-objective risk-aware RL, discounting, and safe RL. We also examine problems - in particular focusing on the novel potential UBRL offers the algorithmic implications of adopting a utility-based approach.

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.