Publication Details
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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

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

Reference 
 
 
Link scopus