Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning
<|reference_start|>Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning: 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.<|reference_end|>
@article{vamplew2024utility-based,
    title={Utility-Based Reinforcement Learning: Unifying Single-objective and
  Multi-objective Reinforcement Learning},
    author={Peter Vamplew, Cameron Foale, Conor F. Hayes, Patrick Mannion, Enda
  Howley, Richard Dazeley, Scott Johnson, Johan K"allstr"om, Gabriel Ramos,
  Roxana Ru{a}dulescu, Willem R"opke, Diederik M. Roijers},
    journal={arXiv preprint arXiv:2402.02665},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.02665},
    primaryClass={cs.LG}
}