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A Brief Guide to Multi-Objective Reinforcement Learning and Planning
University of Galway.
Vrije Universiteit Brussel.
Vrije Universiteit Brussel.
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-4144-4893
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2023 (English)In: Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS) / [ed] A. Ricci, W. Yeoh, N. Agmon, B. An, 2023, p. 1988-1990Conference paper, Published paper (Refereed)
Abstract [en]

Real-world sequential decision-making tasks are usually complex, and require trade-offs between multiple–often conflicting–objectives. However, the majority of research in reinforcement learning (RL) and decision-theoretic planning assumes a single objective, or that multiple objectives can be handled via a predefined weighted sum over the objectives. Such approaches may oversimplify the underlying problem, and produce suboptimal results. This extended abstract outlines the limitations of using a semi-blind iterative process to solve multi-objective decision making problems. Our extended paper [4], serves as a guide for the application of explicitly multi-objective methods to difficult problems.

Place, publisher, year, edition, pages
2023. p. 1988-1990
Keywords [en]
Multi-Objective, Reinforcement Learning, Planning
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-194556ISBN: 978-1-4503-9432-1 (electronic)OAI: oai:DiVA.org:liu-194556DiVA, id: diva2:1764338
Conference
International Conference on Autonomous Agents and Multiagent Systems (AAMAS)
Funder
Vinnova, NFFP7/2017-04885Wallenberg AI, Autonomous Systems and Software Program (WASP)Available from: 2023-06-08 Created: 2023-06-08 Last updated: 2023-06-08

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Källström, JohanHeintz, Fredrik

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CiteExportLink to record
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Citation style
  • apa
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  • Other locale
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Output format
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  • text
  • asciidoc
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