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 currently working on off-policy Reinforcement Learning methods. He is part of the group SAIROL, a DFKI unit, and works under the supervision of Boris Belousov and Jan Peters.
During his studies, Théo focused on Deep Learning, (3D) 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 supersived 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.
Supervision
- Master Thesis, Fabian Wahren (with Boris Belousov); Adapt your network: Investigating neural network’s architecture in Q-learning methods.
- HiWi, Yogesh Tripathi; SlimRL: a simple minimal library for off-policy Reinforcement Learning.
Teaching
Statistical Machine Learning (Summer - 2023)
Reinforcement Learning (Summer - 2024)
Reviewing
IROS, RLC
Publications
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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).
- Vincent, T.; Metelli, A.; Peters, J.; Restelli, M.; D'Eramo, C. (2023). Parameterized projected Bellman operator, ICML Workshop on New Frontiers in Learning, Control, and Dynamical Systems.
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- Vincent, T.; Belousov, B.; D'Eramo, C.; Peters, J. (2023). Iterated Deep Q-Network: Efficient Learning of Bellman Iterations for Deep Reinforcement Learning, European Workshop on Reinforcement Learning (EWRL).