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A practical guide t...
A practical guide to multi-objective reinforcement learning and planning
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- Hayes, Conor F. (författare)
- National University of Ireland Galway, Galway, Ireland
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- Rădulescu, Roxana (författare)
- AI Lab, Vrije Universiteit Brussel, Brussels, Belgium
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- Bargiacchi, Eugenio (författare)
- AI Lab, Vrije Universiteit Brussel, Brussels, Belgium
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- Källström, Johan, 1976- (författare)
- Linköpings universitet,Artificiell intelligens och integrerade datorsystem,Tekniska fakulteten
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- Macfarlane, Matthew (författare)
- AMLAB, University of Amsterdam, Amsterdam, The Netherlands
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- Reymond, Mathieu (författare)
- AI Lab, Vrije Universiteit Brussel, Brussels, Belgium
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- Verstraeten, Timothy (författare)
- AI Lab, Vrije Universiteit Brussel, Brussels, Belgium
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- Zintgraf, Luisa M. (författare)
- WhiRL, University of Oxford, Oxford, United Kingdom
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- Dazeley, Richard (författare)
- Deakin University, Geelong, Australia
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- Heintz, Fredrik, 1975- (författare)
- Linköpings universitet,Artificiell intelligens och integrerade datorsystem,Tekniska fakulteten
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- Howley, Enda (författare)
- National University of Ireland Galway, Galway, Ireland
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- Irissappane, Athirai A. (författare)
- University of Washington (Tacoma), Tacoma, USA
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- Mannion, Patrick (författare)
- National University of Ireland Galway, Galway, Ireland
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- Nowé, Ann (författare)
- AI Lab, Vrije Universiteit Brussel, Brussels, Belgium
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- Ramos, Gabriel (författare)
- Universidade do Vale do Rio dos Sinos, São Leopoldo, RS, Brazil
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- Restelli, Marcello (författare)
- Politecnico di Milano, Milan, Italy
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- Vamplew, Peter (författare)
- Federation University Australia, Ballarat, Australia
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- Roijers, Diederik M. (författare)
- HU University of Applied Sciences Utrecht, Utrecht, The Netherlands
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(creator_code:org_t)
- 2022-04-13
- 2022
- Engelska.
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Ingår i: Autonomous Agents and Multi-Agent Systems. - New York, NY, United States : Springer. - 1387-2532 .- 1573-7454. ; 36:1
- Relaterad länk:
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https://liu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Multi-objective decision making
- Multi-objective reinforcement learning
- Multi-objective planning
- Multi-objective multi-agent systems
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- ref (ämneskategori)
- art (ämneskategori)
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Hayes, Conor F.
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Rădulescu, Roxan ...
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Bargiacchi, Euge ...
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Källström, Johan ...
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Macfarlane, Matt ...
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Reymond, Mathieu
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visa fler...
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Verstraeten, Tim ...
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Zintgraf, Luisa ...
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Dazeley, Richard
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Heintz, Fredrik, ...
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Howley, Enda
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Irissappane, Ath ...
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Mannion, Patrick
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Nowé, Ann
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Ramos, Gabriel
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Restelli, Marcel ...
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Vamplew, Peter
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Roijers, Diederi ...
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visa färre...
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