期刊
AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS
卷 36, 期 1, 页码 -出版社
SPRINGER
DOI: 10.1007/s10458-022-09552-y
关键词
Multi-objective decision making; Multi-objective reinforcement learning; Multi-objective planning; Multi-objective multi-agent systems
资金
- Fonds voor Wetenschappelijk Onderzoek (FWO) [1SA2820N]
- Flemish Government
- FWO [iBOF/21/027]
- National University of Ireland Galway Hardiman Scholarship
- FAPERGS [19/2551-0001277-2]
- FAPESP [2020/05165-1]
- Swedish Governmental Agency for Innovation Systems [NFFP7/2017-04885]
- Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation
- LIFT - Dutch Research Council (NWO) [019.011]
- 2017 Microsoft Research PhD Scholarship Program
- 2020 Microsoft Research EMEA PhD Award
This paper serves as a guide for applying multi-objective methods to difficult decision-making problems, addressing the issue of oversimplification in current research and providing insights for researchers and practitioners familiar with single-objective reinforcement learning and planning methods.
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.
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