Research output per year
Research output per year
Personal profile
Personal information
Since 2020, I am an assistant professor at the VU Amsterdam. Previously, I did a post-doc in Montreal at McGill University/Mila. I obtained a PhD in machine learning at the University of Liege in 2017.
Research
My field of research is focused on deep learning and reinforcement learning. One main goal is to create agents that can learn how to perform sequential decision-making tasks while generalizing from limited data. My research focuses in particular on the role of representation learning and how it is possible to integrate model-free learning (direct inference from deep learning) with model-based learning (e.g. planning and "reasoning").
Teaching
I'm currently teaching "Projects on Reinforcement Learning" as well as co-teaching "Dynamic Programming and Reinforcement Learning". I am also the program director of the MSc AI at the VU Amsterdam since 2024 (cohorts of 150+ students).
Ancillary activities
No ancillary activities
Ancillary activities are updated daily
Keywords
- Q Science (General)
- deep learning
- machine learning
- reinforcement learning
- representation learning
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Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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The relationship between intrinsic capacity and functional ability – identifying key environmental features to support healthy ageing
Govaerts, J., Yang, Z., Visser, M., Huisman, M., Francois-Lavet, V., van Schoor, N. M. & Schaap, L. A., Jul 2026, In: Experimental Gerontology. 220, 113171.Research output: Contribution to Journal › Article › Academic › peer-review
Open Access -
Reinforcement Learning from Cross-domain Videos with Video Prediction Model
Yang, Z., Zu, X., Kooi, J. E., Delliaux, T., Liu, H., Yu, S., Luck, K. S. & François-Lavet, V., 2 Jun 2026.Research output: Working paper / Preprint › Preprint › Academic
File -
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning
Arcudi, A., Sartor, D., Sinigaglia, A., François-Lavet, V. & Susto, G. A., 1 Sept 2025, In: IFAC-PapersOnLine. 59, 26, p. 25-30 6 p.Research output: Contribution to Journal › Article › Academic › peer-review
Open Access -
Analyzing Latent Entropy in Deep Q-Learning
Kooi, J. E., Hoogendoorn, M. & Francois-Lavet, V., 2025, 2025 IEEE Symposium on Computational Intelligence in Image, Signal Processing and Synthetic Media (CISM): [Proceedings]. Institute of Electrical and Electronics Engineers Inc., p. 1-7 7 p.Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review
Open AccessFile206 Downloads (Pure) -
Learning Task-Aware Abstract Representations for Meta-Reinforcement Learning
van Remmerden, L., Yang, Z., Yu, S., Hoogendoorn, M. & François-Lavet, V., 2025, In: Transactions on Machine Learning Research. 2025, 12, p. 1-41 41 p.Research output: Contribution to Journal › Article › Academic › peer-review
Open Access
Courses
Projects
- 1 Active
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IMPROve: Intrinsic Capacity Maintenance for Promoting Healthy Aging
Schaap, L. (Project Researcher), van Schoor, N. M. (Project Researcher), Visser, M. (Project Researcher), Vollenbroek-Hutten, M. M. R. (Project Researcher), Wang, Y. (Project Researcher) & Francois Lavet, V. (Project Researcher)
1/06/23 → 31/05/28
Project: Research