A Practical Guide to Multi-Objective Reinforcement Learning and Planning

• URL: http://arxiv.org/abs/2103.09568v1
• Date: Wed, 17 Mar 2021 11:07:28 GMT
• Title: A Practical Guide to Multi-Objective Reinforcement Learning and Planning
• Authors: Conor F. Hayes, Roxana R\u{a}dulescu, Eugenio Bargiacchi, Johan K\"allstr\"om, Matthew Macfarlane, Mathieu Reymond, Timothy Verstraeten, Luisa M. Zintgraf, Richard Dazeley, Fredrik Heintz, Enda Howley, Athirai A. Irissappane, Patrick Mannion, Ann Now\'e, Gabriel Ramos, Marcello Restelli, Peter Vamplew, Diederik M. Roijers
• Abstract summary: This paper serves as a guide to the application of multi-objective methods to difficult problems. It identifies the factors that may influence the nature of the desired solution. It illustrates by example how these influence the design of multi-objective decision-making systems.
• Score: 24.81310809455139
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-world 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.

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