40 citations · 58 across the 9 of their papers we have counts for
5 papers · 1 filter
Improving Zero-Shot Offline RL via Behavioral Task Sampling
Nazim Bendib, Nicolas Perrin-Gilbert, Olivier Sigaud
Offline zero-shot reinforcement learning (RL) aims to learn agents that optimize unseen reward functions without additional environment interaction. The standard approach to this p…
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,…
Divide & Conquer Imitation Learning
Alexandre Chenu, Nicolas Perrin-Gilbert, Olivier Sigaud
When cast into the Deep Reinforcement Learning framework, many robotics tasks require solving a long horizon and sparse reward problem, where learning algorithms struggle. In such…
Selection-Expansion: A Unifying Framework for Motion-Planning and Diversity Search Algorithms
Alexandre Chenu, Nicolas Perrin-Gilbert, Stéphane Doncieux +1
Reinforcement learning agents need a reward signal to learn successful policies. When this signal is sparse or the corresponding gradient is deceptive, such agents need a dedicated…
Diversity Policy Gradient for Sample Efficient Quality-Diversity Optimization
Thomas Pierrot, Valentin Macé, Félix Chalumeau +6
A fascinating aspect of nature lies in its ability to produce a large and diverse collection of organisms that are all high-performing in their niche. By contrast, most AI algorith…