  Research in multi-objective reinforcement learning (MORL) has introduced the
utility-based paradigm, which makes use of both environmental rewards and a
function that defines the utility derived by the user from those rewards. In
this paper we extend this paradigm to the context of single-objective
reinforcement learning (RL), and outline multiple potential benefits including
the ability to perform multi-policy learning across tasks relating to uncertain
objectives, risk-aware RL, discounting, and safe RL. We also examine the
algorithmic implications of adopting a utility-based approach.
