About Me
I am a Computer Science student from ENS Rennes specializing in Reinforcement Learning (RL) and sequential decision-making under uncertainty. I'm currently completing my final Master's internship at LIP6 (Sorbonne Université) on hardware-accelerated Multi-Objective (and potentially Multi-Agent) RL benchmarking with JAX. My goal is to pursue a PhD focused on RL theory, deep RL, and RL under different settings, with the possibility to apply it to robotics or industry, starting Fall 2026.
Current Research Focus
- NNs & POMDP: Developing theory-grounded methods to solve POMDPs with Neural Networks to achieve stronger theoretical guarantees at scale. Recently accepted at RDPIA 2026. I will present this work at the conference and I will put a link to the paper here once it's available.
- MB-MARL: Designing a Multi-Agent RL solution using an actor-critic approach to learn a decentralized world-model using a centralized one. While having the ability to handle varying number of agents.