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FELTEN Florian

ORCID
0000-0002-2874-3645
Main Referenced Co-authors
TALBI, El-Ghazali  (6)
DANOY, Grégoire  (5)
Nowé, Ann (2)
Alegre, Lucas N. (1)
Alegre, Lucas Nunes (1)
Main Referenced Keywords
Multi-objective (3); Reinforcement Learning (3); Artificial Intelligence (1); Benchmarking (1); Environments (1);
Main Referenced Unit & Research Centers
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > PCOG - Parallel Computing & Optimization Group (2)
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Parallel Computing & Optimization Group (PCOG) (1)
ULHPC - University of Luxembourg: High Performance Computing (1)
Main Referenced Disciplines
Computer science (7)

Publications (total 7)

The most downloaded
146 downloads
FELTEN, F. (2024). Multi-Objective Reinforcement Learning [Doctoral thesis, Unilu - Université du Luxembourg]. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/61488 https://hdl.handle.net/10993/61488

The most cited

6 citations (Scopus®)

FELTEN, F.* , Alegre, L. N.* , Nowé, A., L. C. Bazzan, A., TALBI, E.-G., DANOY, G., & C. da Silva, B. (2024). A Toolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement Learning. In A Toolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement Learning. United States: Curran Associates. https://hdl.handle.net/10993/59624

FELTEN, F. (2024). Multi-Objective Reinforcement Learning [Doctoral thesis, Unilu - Université du Luxembourg]. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/61488

FELTEN, F., TALBI, E.-G., & DANOY, G. (26 February 2024). Multi-Objective Reinforcement Learning Based on Decomposition: A Taxonomy and Framework. Journal of Artificial Intelligence Research, 79, 679-723. doi:10.1613/jair.1.15702
Peer Reviewed verified by ORBi

FELTEN, F.* , Alegre, L. N.* , Nowé, A., L. C. Bazzan, A., TALBI, E.-G., DANOY, G., & C. da Silva, B. (2024). A Toolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement Learning. In A Toolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement Learning. United States: Curran Associates.
Peer reviewed
* These authors have contributed equally to this work.

FELTEN, F., GAREEV, D., TALBI, E.-G., & DANOY, G. (October 2023). Hyperparameter Optimization for Multi-Objective Reinforcement Learning [Paper presentation]. Multi-Objective Decision Making Workshop (MoDeM), Krakow, Poland. doi:10.48550/arXiv.2310.16487
Peer reviewed

Alegre, L. N., FELTEN, F., TALBI, E.-G., DANOY, G., Nowé, A., Bazzan, A., & da Silva, B. (November 2022). MO-Gym: A Library of Multi-Objective Reinforcement Learning Environments [Paper presentation]. BNAIC/BeNeLearn 2022, Mechelen, Belgium.

FELTEN, F., TALBI, E.-G., & DANOY, G. (2022). MORL/D: Multi-Objective Reinforcement Learning based on Decomposition. In International Conference in Optimization and Learning (OLA2022). doi:10.5220/0010989100003116
Peer reviewed

FELTEN, F., Danoy, G., TALBI, E.-G., & BOUVRY, P. (2022). Metaheuristics-based Exploration Strategies for Multi-Objective Reinforcement Learning. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence (pp. 662--673). Online Streaming, Unknown/unspecified: SCITEPRESS - Science and Technology Publications. doi:10.5220/0010989100003116
Peer reviewed

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