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A practical guide to multi-objective reinforcement learning and planning
Authors
Eugenio Bargiacchi
Richard Dazeley
+16 more
Conor Hayes
Frederick Heintz
Enda Howley
Athirai Irissappane
Johan Källström
Matthew Macfarlane
Patrick Mannion
Ann Nowé
Gabriel Ramos
Marcello Restelli
Mathieu Reymond
Diederik Roijers
Roxana Rădulescu
Peter Vamplew
Timothy Verstraeten
Luisa Zintgraf
Publication date
1 January 2022
Publisher
'Springer Science and Business Media LLC'
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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)
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Last time updated on 24/02/2023