Published in NeurIPS 2025, 2025
The first unified API for engineering design optimization and ML research.
Recommended citation: Florian Felten, Gabriel Apaza, Gerhard Bräunlich, Cashen Diniz, XULIANG DONG, Arthur Drake, Milad Habibi, Nathaniel James Hoffman, Matthew Keeler, Soheyl Massoudi, Francis VanGessel, Mark Fuge ‘EngiBench: A Framework for Data-Driven Engineering Design Research’, in NeurIPS 2025.
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Published in Université du Luxembourg [FSTM], 2024
My Thesis on Multi-Objective Reinforcement Learning
Recommended citation: Florian Felten, ‘Multi-Objective Reinforcement Learning’, in Unilu - Université du Luxembourg [FSTM], Luxembourg.
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Published in Journal of Artificial Intelligence Research (JAIR), 2024
MOO/D + RL = MORL/D
Recommended citation: 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.
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Published in Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2023
MO-Gymnasium + MORL-baselines + Public datasets of training results.
Recommended citation: 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).
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Published in Multi-Objective Decision Making Workshop (MODeM2023), 2023
Setting up the foundations for HPO 4 MORL.
Recommended citation: 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.
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Published in BNAIC/BeNeLearn 22, 2022
Open source library for Multi-Objective RL.
Recommended citation: 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
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Published in International Conference in Optimization and Learning (OLA2022), 2022
Applying decomposition techniques from MOO to MORL.
Recommended citation: 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)
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Published in Proceedings of the 14th International Conference on Agents and Artificial Intelligence, 2022
Nominated for best student paper award.
Recommended citation: 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.
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