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Clément Romac

Research Scientist / Ph.D. Student
Hugging Face / Inria Bordeaux (France)
clement[dot]romac[at]gmail[dot]com


About Me

Hi, I’m Clément Romac. After graduating in Computer Science engineering from Ingesup with a Data Science specialisation, while part-time working on Automated Machine Learning in a startup called Weenove, I obtained a MSc. in theoretical Computer Science at the University of Bordeaux (France). Shortly after, I joined the FLOWERS team at Inria Bordeaux as a Research Engineer working on Automatic Curriculum Learning for Deep RL and Automated scientific discovery in complex systems (e.g. self-organizing).

I’m now a Research Scientist at Hugging Face and a second-year PhD student jointly supervised by Pierre-Yves Oudeyer (FLOWERS, Inria) and Thomas Wolf (Hugging Face) studying how autonomous Deep RL agents can leverage Large Language Models. I am also co-animating the Machine Learning Meetup group of Bordeaux.

Research Interests

News

Publications

  1. ICML
    Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning
    Thomas Carta, Clément Romac, Thomas Wolf, Sylvain Lamprier, Olivier Sigaud, Pierre-Yves Oudeyer
    International Conference on Machine Learning (ICML), 2023.
    PDF Code Project Page Poster

  2. ICML
    TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL
    Clément Romac, Rémy Portelas, Katja Hofmann, Pierre-Yves Oudeyer
    International Conference on Machine Learning (ICML), 2021.
    PDF Code Project Page Poster

  3. Meta Automatic Curriculum Learning
    Rémy Portelas, Clément Romac, Katja Hofmann, Pierre-Yves Oudeyer
    Preprint, 2020
    PDF Code Project Page

  4. Deep Recurrent Q-Learning vs Deep Q-Learning on a simple Partially Observable Markov Decision Process with Minecraft
    Clément Romac, Vincent Beraud
    Preprint, 2019
    PDF Code

Projects

  1. ChatGPT explained in 5 minutes
    A series of short videos explaining Large Language Models.
    Project Page

  2. Lamorel
    Lamorel is a Python library designed for RL practitioners eager to use Large Language Models (LLMs).
    Code

  3. Automated Discovery Tool
    A software for assisted and automated discovery of patterns in the exploration of complex systems.
    Code

  4. Interactive Deep RL Demo
    In-browser interactive demo of Deep Reinforcement Learning agents' adaptation to unknown 2D tasks.
    Project Page Code

Other stuff


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