Publication Details
Conor F. Hayes, Roxana Radulescu, Eugenio Bargiacchi, Bargiacchi, Eugenio, Johan Källström, Matthew Macfarlane, Mathieu Reymond, Verstraeten, Timothy, Luisa Zintgraf, Richard Dazeley, Fredrik Heintz, Enda Howley, Athirai A. Irissappane, Patrick Mannion, , Gabriel De Oliveira Ramos, Marcello Restelli, Peter Vamplew, Diederik M. Roijers

The 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023)

Contribution To Book Anthology


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 serves as a guide for the application of explicitly multi-objective methods to difficult problems.

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