Théo Vincent

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.

Publications

1. •     Bib
     Vincent, T.; Wahren, F.; Peters, J.; Belousov, B.; D'Eramo, C.; (2024). Adaptive Q-Network: On-the-fly Target Selection for Deep Reinforcement Learning, European Workshop on Reinforcement Learning (EWRL).
   •     Bib
     Vincent, T.; Wahren, F.; Peters, J.; Belousov, B.; D'Eramo, C.; (2024). Adaptive Q-Network: On-the-fly Target Selection for Deep Reinforcement Learning, ICML Workshop on Automated Reinforcement Learning.
2. •     Bib
     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).
   •     Bib
     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.
3. •     Bib
     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).