I am a Machine Learning Research Scientist at Hugging
Face.
I work on problems involving Embodied Learning, by developing novel Deep Reinforcement
Learning approaches and custom simulation environments.
My PhD is in Deep Reinforcement Learning approaches to planning and navigation in
robotics, which I studied at INSA Lyon, as
part of the INRIA CHROMA
team.
My work involves state of the art convolutional, recurrent and
transformer based network architectures applied to typical
RL optimization with on-policy (A2C, PPO, APPO) and off-policy (SAC, Q-learning, R2D2)
algorithms. I also often consider auxillary optimization objectives to improve agent
performance, such as classification, regression and semantic segmentation.
Agent behaviors learned with the Godot RL Agents Library
Image-goal based navigation in a 3D scan of our laboratory. The map is not provided to the agent.
Example of a Deep Reinforcement Learning Agent trained to collect a sequence of objects with the Advantage Actor
Critic Algorithm. The map is not provided to the agent.