| Full text | |
| Author(s): Show less - |
Hayes, Conor F.
;
Radulescu, Roxana
;
Bargiacchi, Eugenio
;
Kallstrom, Johan
;
Macfarlane, Matthew
;
Reymond, Mathieu
;
Verstraeten, Timothy
;
Zintgraf, Luisa M.
;
Dazeley, Richard
;
Heintz, Fredrik
;
Howley, Enda
;
Irissappane, Athirai A.
;
Mannion, Patrick
;
Nowe, Ann
;
Ramos, Gabriel
;
Restelli, Marcello
;
Vamplew, Peter
;
Roijers, Diederik M.
Total Authors: 18
|
| Document type: | Journal article |
| Source: | AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS; v. 36, n. 1, p. 59-pg., 2022-04-01. |
| Abstract | |
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. (AU) | |
| FAPESP's process: | 20/05165-1 - Communication and machine learning in urban mobility: a multiagent and multiobjective approach |
| Grantee: | Ana Lúcia Cetertich Bazzan |
| Support Opportunities: | Regular Research Grants |