CV

My LinkedIn is probably more complete than this page.

Education

Ph.D in Computer Science (2021–2024)
- I won several awards for my thesis on multi-objective RL.
- T.A. in Optimization for Computer Scientist: Master
M.S. in Computer Science, Université Catholique de Louvain, Major in AI, Big Data and Optimization, 2018
- Thesis subject: “Reducing train delays in a real-time context: a Constraint Programming approach using conditional time-interval variables”
- Grade: Magna Cum Laude
- Erasmus at KTH, Stockholm
B.S. in Computer Science, Université Catholique de Louvain, 2015

Work experience

2026–: Self employed / entrepreneur.
2024–2026: PostDoc @ ETH Zurich
2023–: Project Manager @ Farama Foundation
- I’m one of the original developers and maintainers of MO-Gymnasium.
- I also contribute to other projects of the foundation (PettingZoo, SuperSuit).
2021–2023: Lecturer @ Uni.lu
- Programming Fundamentals 3
- 2nd year CS course
- Functional Programming, Concurrent Programming, Introduction to distributed systems
2018–2021: Software engineer @ N-SIDE
- Duties included: Architecture, Development and deployment of the apps, Backlog management, Requirement analysis.
- Stack: Scala, Akka, Kubernetes, IBM CP Optimizer, Scala.js

Skills

Optimization
Machine Learning
Programming
- Functional
- Imperative & Object-Oriented
- Concurrency
- Distributed systems
Teaching
Chilling

Reviews

NeurIPS (Reviewer)
Neural Computing and Applications (Reviewer)
Multi-Objective Decision Making Workshop (MODeM) 2023–2025 (PC member)
Adaptive and Learning Agents Workshop at AAMAS (ALA) 2023 & 2024 (PC member)
International Conference on Optimization and Learning (OLA) 2022–2024 (PC member)

Publications

EngiBench: A Framework for Data-Driven Engineering Design Research

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.

Multi-Objective Reinforcement Learning

Florian Felten, ‘Multi-Objective Reinforcement Learning’, in Unilu - Université du Luxembourg [FSTM], Luxembourg.

Multi-Objective Reinforcement Learning Based on Decomposition: A Taxonomy and Framework

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.

A Toolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement Learning

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).

Hyperparameter Optimization for Multi-Objective Reinforcement Learning

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.

MO-Gym: A Library of Multi-Objective Reinforcement Learning Environments

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

MORL/D: Multi-Objective Reinforcement Learning based on Decomposition

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)

Metaheuristics-based Exploration Strategies for Multi-Objective Reinforcement Learning

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.