40 citations · 134 across the 40 of their papers we have counts for
3 papers · 1 filter
A Definition of Open-Ended Learning Problems for Goal-Conditioned Agents
Olivier Sigaud, Gianluca Baldassarre, Cedric Colas +5
A lot of recent machine learning research papers have ``open-ended learning'' in their title. But very few of them attempt to define what they mean when using the term. Even worse,…
An Open-Loop Baseline for Reinforcement Learning Locomotion Tasks
Antonin Raffin, Olivier Sigaud, Jens Kober +3
In search of a simple baseline for Deep Reinforcement Learning in locomotion tasks, we propose a model-free open-loop strategy. By leveraging prior knowledge and the elegance of si…
Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning
Thomas Carta, Clément Romac, Thomas Wolf +3
Recent works successfully leveraged Large Language Models' (LLM) abilities to capture abstract knowledge about world's physics to solve decision-making problems. Yet, the alignment…