40 citations · 58 across the 9 of their papers we have counts for
12 papers
Behavioral Foundation Models for Quality Diversity
Nazim Bendib, Nicolas Perrin-Gilbert, Olivier Sigaud
Behavioral Foundation Models (BFMs) are an emerging paradigm in reinforcement learning, playing a role analogous to large language models in natural language processing: they have…
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…
Learning to explore when mistakes are not allowed
Charly Pecqueux-Guézénec, Stéphane Doncieux, Nicolas Perrin-Gilbert
Goal-Conditioned Reinforcement Learning (GCRL) provides a versatile framework for developing unified controllers capable of handling wide ranges of tasks, exploring environments, a…
Single-Reset Divide & Conquer Imitation Learning
Alexandre Chenu, Olivier Serris, Olivier Sigaud +1
Demonstrations are commonly used to speed up the learning process of Deep Reinforcement Learning algorithms. To cope with the difficulty of accessing multiple demonstrations, some…
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,…
The Quality-Diversity Transformer: Generating Behavior-Conditioned Trajectories with Decision Transformers
Valentin Macé, Raphaël Boige, Felix Chalumeau +3
In the context of neuroevolution, Quality-Diversity algorithms have proven effective in generating repertoires of diverse and efficient policies by relying on the definition of a b…
Assessing Quality-Diversity Neuro-Evolution Algorithms Performance in Hard Exploration Problems
Felix Chalumeau, Thomas Pierrot, Valentin Macé +4
A fascinating aspect of nature lies in its ability to produce a collection of organisms that are all high-performing in their niche. Quality-Diversity (QD) methods are evolutionary…
Leveraging Sequentiality in Reinforcement Learning from a Single Demonstration
Alexandre Chenu, Olivier Serris, Olivier Sigaud +1
Deep Reinforcement Learning has been successfully applied to learn robotic control. However, the corresponding algorithms struggle when applied to problems where the agent is only…
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…
Exploratory State Representation Learning
Astrid Merckling, Nicolas Perrin-Gilbert, Alex Coninx +1
Not having access to compact and meaningful representations is known to significantly increase the complexity of reinforcement learning (RL). For this reason, it can be useful to p…
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…