1 code implementation • 15 Feb 2024 • Quentin Gallouédec, Edward Beeching, Clément Romac, Emmanuel Dellandréa
The search for a general model that can operate seamlessly across multiple domains remains a key goal in machine learning research.
3 code implementations • 6 Feb 2023 • Thomas Carta, Clément Romac, Thomas Wolf, Sylvain Lamprier, Olivier Sigaud, Pierre-Yves Oudeyer
Using an interactive textual environment designed to study higher-level forms of functional grounding, and a set of spatial and navigation tasks, we study several scientific questions: 1) Can LLMs boost sample efficiency for online learning of various RL tasks?
1 code implementation • 17 Mar 2021 • Clément Romac, Rémy Portelas, Katja Hofmann, Pierre-Yves Oudeyer
Training autonomous agents able to generalize to multiple tasks is a key target of Deep Reinforcement Learning (DRL) research.
no code implementations • 16 Nov 2020 • Rémy Portelas, Clément Romac, Katja Hofmann, Pierre-Yves Oudeyer
In such complex task spaces, it is essential to rely on some form of Automatic Curriculum Learning (ACL) to adapt the task sampling distribution to a given learning agent, instead of randomly sampling tasks, as many could end up being either trivial or unfeasible.
2 code implementations • 11 Mar 2019 • Clément Romac, Vincent Béraud
Deep Q-Learning has been successfully applied to a wide variety of tasks in the past several years.