- Title
- Utility-based reinforcement learning : unifying single-objective and multi-objective reinforcement learning
- Creator
- Vamplew, Peter; Foale, Cameron; Hayes, Conor; Mannion, Patrick; Howley, Enda; Dazeley, Richard; Johnson, Scott; Källström, Johan; Ramos, Gabriel; Rădulescu, Roxana; Röpke, Willem; Roijers, Diederik
- Date
- 2024
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/202781
- Identifier
- vital:19751
- Identifier
- ISBN:1548-8403 (ISSN)
- Abstract
- 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. © 2024 International Foundation for Autonomous Agents and Multiagent Systems.
- Publisher
- International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
- Relation
- 23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024, Auckland, 6-10 May 2024, Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS Vol. 2024-May, p. 2717-2721
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright © 2024 International Foundation for Autonomous Agents and Multiagent Systems
- Rights
- Open Access
- Subject
- Reinforcement learning; Utility
- Full Text
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