Théo Vincent

Research Interests
(Deep) Reinforcement Learning
Affiliations
1. TU Darmstadt, Intelligent Autonomous Systems, Computer Science Department
2. German Research Center for AI (DFKI), Research Department: SAIROL
Contact
Room 119, Floor 2
Landwehrstraße 50A, 64293 Darmstadt
theo.vincent@robot-learning.de [main]
theo.vincent@dfki.de
Théo Vincent joined the Intelligent Autonomous Systems Group as a Ph.D. student in December 2022. He is part of the group SAIROL, a DFKI unit, and works under the supervision of Jan Peters. He is currently working on sample-efficient deep reinforcement learning methods. Théo published in top ML conferences, developing advanced algorithms for hyperparameter tuning (AdaQN), for discovering sparse neural network structures (EauDeQN), and for boosting the performance of classical RL algorithms (iS-QN, i-QN, and PBO).
During his studies, Théo focused on computer vision and reinforcement learning. He graduated from MVA, a double master's degree program in collaboration with ENS ParisSaclay and Ponts ParisTech. He did his master's thesis at IAS on value-based methods in offline settings supervised by Carlo D’Eramo.
Before his Ph.D., Théo worked in a Parisian lab called Saint-Venant lab, supervised by Rémi Carmigniani to track professional swimmers on video clips using Deep Learning. He then joined Signality, a Swedish start-up led by Mikael Rousson, to investigate the problem of finding a homography linking a football pitch to a camera. He also worked in the group of Chirag Patel at Harvard Medical School to understand how our organs are aging.
Talks
08/2025 – at Mila , Montréal 🇨🇦 |
hosted by Glen Berseth |
07/2025 – at IWIALS , Hirschegg 🇦🇹 |
hosted by Jan Peters |
07/2025 – at Cohere Labs , Online 🌎 |
available on YouTube |
11/2024 – at Uni Mannheim , Mannheim 🇩🇪 |
hosted by Leif Döring |
09/2024 – at Naver Labs , Grenoble 🇫🇷 |
hosted by Jean-Michel Renders |
05/2024 – at INSAIT, Sofia 🇧🇬 |
hosted by Danda Paudel |
04/2023 – at Lite RL, Würzburg 🇩🇪 |
hosted by Carlo D'Eramo |
Reviewing
IROS (2023), RLC (2024, 2025), ICLR (2025), JMLR (2025)
AutoRL@ICML (Outstanding reviewer)
Teaching
🎮 Reinforcement Learning, Summer 2024 & Summer 2025 |
📊 Statistical Machine Learning, Summer 2023 & Summer 2024 |
Supervision
- Habib Maraqten: integrated project (2025)
- Kevin Gerhardt: bachelor thesis (2025)
- Tim Faust: master thesis (2025)
- Yogesh Tripathi: master thesis, research assistant, integrated projects (x2) (2024- 2025)
- Fabian Wahren: master thesis (2023)
Publications
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- Vincent, T.; Tripathi, Y.; Faust, T.; Oren, Y.; Peters, J.; D'Eramo, C. (2025). Bridging the Performance Gap Between Target-Free and Target-Based Reinforcement Learning With Iterated Q-Learning, European Workshop on Reinforcement Learning (EWRL).
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- Vincent, T.; Faust, T.; Tripathi, Y.; Peters, J.; D'Eramo, C. (2025). Eau De Q-Network: Adaptive Distillation of Neural Networks in Deep Reinforcement Learning, Reinforcement Learning Conference (RLC).
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- Vincent, T.; Palenicek, D.; Belousov, B.; Peters, J.; D'Eramo, C. (2025). Iterated Q-Network: Beyond One-Step Bellman Updates in Deep Reinforcement Learning, Transactions on Machine Learning Research (TMLR).
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- Vincent, T.; Wahren, F.; Peters, J.; Belousov, B.; D'Eramo, C. (2025). Adaptive Q-Network: On-the-fly Target Selection for Deep Reinforcement Learning, International Conference on Learning Representations (ICLR).
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- Vincent, T.; Metelli, A.; Belousov, B.; Peters, J.; Restelli, M.; D'Eramo, C. (2024). Parameterized Projected Bellman Operator, Proceedings of the National Conference on Artificial Intelligence (AAAI).