My LinkedIn is probably more complete than this page.
Felten, F., Danoy, G., Talbi, E. and Bouvry, P. (2022). Metaheuristics-based Exploration Strategies for Multi-Objective Reinforcement Learning. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-547-0; ISSN 2184-433X, pages 662-673. DOI: 10.5220/0010989100003116.
F. Felten, E.-G. Talbi, and G. Danoy, (2022). MORL/D: Multi-Objective Reinforcement Learning based on Decomposition. In Proceedings of International Conference on Optimization and Learning (OLA2022)
L. N. Alegre, F. Felten, E.-G. Talbi, G. Danoy, A. Nowé, and A. L. C. Bazzan, “MO-Gym: A Library of Multi-Objective Reinforcement Learning Environments,” Proceedings of the 34th Benelux Conference on Artificial Intelligence BNAIC/Benelearn 2022
F. Felten, D. Gareev, E.-G. Talbi, and G. Danoy, (2023). Hyperparameter Optimization for Multi-Objective Reinforcement Learning. arXiv, Oct. 25, 2023. doi: 10.48550/arXiv.2310.16487.
Florian Felten, Lucas Nunes Alegre, Ann Nowe, Ana L. C. Bazzan, El Ghazali Talbi, Grégoire Danoy, and Bruno Castro da Silva, ‘A Toolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement Learning’, in Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2023).
Florian Felten, El Ghazali Talbi, and Grégoire Danoy, ‘Multi-Objective Reinforcement Learning Based on Decomposition: A Taxonomy and Framework’, in Journal of Artificial Intelligence Research.
Conference talk at International Conference on Agents and Artificial Intelligence, Online
Vulgarization at My lab, Online
Conference talk at MODem workshop at ECAI, Krakow, Poland
News article at My lab, Esch-sur-Alzette, Luxembourg