A practical guide to multi-objective reinforcement learning and planning
- Hayes, Conor, Rădulescu, Roxana, Bargiacchi, Eugenio, Källström, Johan, Macfarlane, Matthew, Reymond, Mathieu, Verstraeten, Timothy, Zintgraf, Luisa, Dazeley, Richard, Heintz, Fredrik, Howley, Enda, Irissappane, Athirai, Mannion, Patrick, Nowé, Ann, Ramos, Gabriel, Restelli, Marcello, Vamplew, Peter, Roijers, Diederik
- Authors: Hayes, Conor , Rădulescu, Roxana , Bargiacchi, Eugenio , Källström, Johan , Macfarlane, Matthew , Reymond, Mathieu , Verstraeten, Timothy , Zintgraf, Luisa , Dazeley, Richard , Heintz, Fredrik , Howley, Enda , Irissappane, Athirai , Mannion, Patrick , Nowé, Ann , Ramos, Gabriel , Restelli, Marcello , Vamplew, Peter , Roijers, Diederik
- Date: 2022
- Type: Text , Journal article
- Relation: Autonomous Agents and Multi-Agent Systems Vol. 36, no. 1 (2022), p.
- Full Text:
- Reviewed:
- Description: 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).
- Authors: Hayes, Conor , Rădulescu, Roxana , Bargiacchi, Eugenio , Källström, Johan , Macfarlane, Matthew , Reymond, Mathieu , Verstraeten, Timothy , Zintgraf, Luisa , Dazeley, Richard , Heintz, Fredrik , Howley, Enda , Irissappane, Athirai , Mannion, Patrick , Nowé, Ann , Ramos, Gabriel , Restelli, Marcello , Vamplew, Peter , Roijers, Diederik
- Date: 2022
- Type: Text , Journal article
- Relation: Autonomous Agents and Multi-Agent Systems Vol. 36, no. 1 (2022), p.
- Full Text:
- Reviewed:
- Description: 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).
A brief guide to multi-objective reinforcement learning and planning JAAMAS track
- Hayes, Conor, Bargiacchi, Eugenio, Källström, Johan, Macfarlane, Matthew, Reymond, Mathieu, Verstraeten, Timothy, Zintgraf, Luisa, Dazeley, Richard, Heintz, Fredrik, Howley, Enda, Irissappane, Athirai, Mannion, Patrick, Nowé, Ann, Ramos, Gabriel, Restelli, Marcello, Vamplew, Peter, Roijers, Diederik
- Authors: Hayes, Conor , Bargiacchi, Eugenio , Källström, Johan , Macfarlane, Matthew , Reymond, Mathieu , Verstraeten, Timothy , Zintgraf, Luisa , Dazeley, Richard , Heintz, Fredrik , Howley, Enda , Irissappane, Athirai , Mannion, Patrick , Nowé, Ann , Ramos, Gabriel , Restelli, Marcello , Vamplew, Peter , Roijers, Diederik
- Date: 2023
- Type: Text , Conference paper
- 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
- Full Text:
- Reviewed:
- Description: 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.
- Authors: Hayes, Conor , Bargiacchi, Eugenio , Källström, Johan , Macfarlane, Matthew , Reymond, Mathieu , Verstraeten, Timothy , Zintgraf, Luisa , Dazeley, Richard , Heintz, Fredrik , Howley, Enda , Irissappane, Athirai , Mannion, Patrick , Nowé, Ann , Ramos, Gabriel , Restelli, Marcello , Vamplew, Peter , Roijers, Diederik
- Date: 2023
- Type: Text , Conference paper
- 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
- Full Text:
- Reviewed:
- Description: 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.
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