6 citations · 6 across the 8 of their papers we have counts for
4 papers · 1 filter
On Double Descent in Reinforcement Learning with LSTD and Random Features
David Brellmann, Eloïse Berthier, David Filliat +1
Temporal Difference (TD) algorithms are widely used in Deep Reinforcement Learning (RL). Their performance is heavily influenced by the size of the neural network. While in supervi…
VIBR: Learning View-Invariant Value Functions for Robust Visual Control
Tom Dupuis, Jaonary Rabarisoa, Quoc-Cuong Pham +1
End-to-end reinforcement learning on images showed significant progress in the recent years. Data-based approach leverage data augmentation and domain randomization while represent…
Evaluating Robustness over High Level Driving Instruction for Autonomous Driving
Florence Carton, David Filliat, Jaonary Rabarisoa +1
In recent years, we have witnessed increasingly high performance in the field of autonomous end-to-end driving. In particular, more and more research is being done on driving in ur…
EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use case
Natalia Díaz-Rodríguez, Alberto Lamas, Jules Sanchez +7
The latest Deep Learning (DL) models for detection and classification have achieved an unprecedented performance over classical machine learning algorithms. However, DL models are…