- Title
- A brief guide to multi-objective reinforcement learning and planning JAAMAS track
- Creator
- Hayes, Conor; Bargiacchi, Eugenio; Källström, Johan; Macfarlane, Matthew; Reymond, Mathieu; Verstraeten, Timothy; Zintgraf, Luisa; Dazeley, Richard; Heintz, Frederik; Howley, Enda; Irissappane, Aathirai; Mannion, Patrick; Nowé, Ann; Ramos, Gabriel; Restelli, Marcello; Vamplew, Peter; Roijers, Diederik
- Date
- 2023
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/196563
- Identifier
- vital:18723
- Identifier
- ISBN:1548-8403 (ISSN)
- Abstract
- 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. © 2023 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.
- Publisher
- International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
- Relation
- 22nd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2023, London, 29 May to 2 June 2023, Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS Vol. 2023-May, p. 1988-1990
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2023 by International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). Permission to make digital or hard copies of portions of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyright for components of this work owned by others than IFAAMAS must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.
- Rights
- Open Access
- Subject
- Multi-objective; Planning; Reinforcement learning
- Full Text
- Reviewed
- Funder
- 2020 Microsoft Research EMEA Autonomous Systems and Software Program Onderzoeksprogramma Artificiële Intelligentie WASP
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