A practical guide to multi-objective reinforcement learning and planning

By Conor F. Hayes, Roxana Ruadulescu, Eugenio Bargiacchi, Johan Kallstrom, Matthew Macfarlane, Mathieu Reymond, T. Verstraeten, Luisa M. Zintgraf, Richard Dazeley, Fredrik Heintz and 8 more. Autonomous Agents and Multi-Agent Systems, 2021

587 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/d835e503c3b46b7967629d8f24887f4a14ecf225