A practical guide to multi-objective reinforcement learning and planning

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 objective...

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Bibliographic Details
Published in:	Autonomous agents and multi-agent systems Vol. 36; no. 1
Main Authors:	Hayes, Conor F., Rădulescu, Roxana, Bargiacchi, Eugenio, Källström, Johan, Macfarlane, Matthew, Reymond, Mathieu, Verstraeten, Timothy, Zintgraf, Luisa M., Dazeley, Richard, Heintz, Fredrik, Howley, Enda, Irissappane, Athirai A., Mannion, Patrick, Nowé, Ann, Ramos, Gabriel, Restelli, Marcello, Vamplew, Peter, Roijers, Diederik M.
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.04.2022 Springer Nature B.V
Subjects:	Artificial Intelligence Computer Science Computer Systems Organization and Communication Networks Decision making Decision theory Learning Multi-objective decision making Multi-Objective Decision Making (MODeM) Multi-objective multi-agent systems Multi-objective planning Multi-objective reinforcement learning Software Engineering/Programming and Operating Systems Task complexity User Interfaces and Human Computer Interaction Multi-objective multi-agent systems Multi-objective reinforcement learning Multi-objective decision making Multi-objective planning
ISSN:	1387-2532, 1573-7454, 1573-7454
Online Access:	Get full text
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