- Title
- A practical guide to multi-objective reinforcement learning and planning
- Creator
- Hayes, Conor; Rădulescu, Roxana; Bargiacchi, Eugenio; Källström, Johan; Macfarlane, Matthew; Reymond, Mathieu; Verstraeten, Timothy; Zintgraf, Luisa; Dazeley, Richard; Heintz, Frederick; Howley, Enda; Irissappane, Athirai; Mannion, Patrick; Nowé, Ann; Ramos, Gabriel; Restelli, Marcello; Vamplew, Peter; Roijers, Diederik
- Date
- 2022
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/188509
- Identifier
- vital:17268
- Identifier
-
https://doi.org/10.1007/s10458-022-09552-y
- Identifier
- ISSN:1387-2532 (ISSN)
- Abstract
- Real-world sequential 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. © 2022, The Author(s).
- Publisher
- Springer
- Relation
- Autonomous Agents and Multi-Agent Systems Vol. 36, no. 1 (2022), p.
- 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 © The Author(s) 2022
- Rights
- Open Access
- Subject
- 4602 Artificial intelligence; Multi-objective decision making; Multi-objective multi-agent systems; Multi-objective planning; Multi-objective reinforcement learning
- Full Text
- Reviewed
- Funder
- This research was supported by funding from the Fonds voor Wetenschappelijk Onderzoek (FWO) through the grant of Eugenio Bargiacchi (#1SA2820N), and by funding from the Flemish Government under the “Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen” for Diederik M. Roijers and Ann Nowé. Roxana R
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