1 code implementation • 7 Aug 2023 • Felix Chalumeau, Bryan Lim, Raphael Boige, Maxime Allard, Luca Grillotti, Manon Flageat, Valentin Macé, Arthur Flajolet, Thomas Pierrot, Antoine Cully
QDax is an open-source library with a streamlined and modular API for Quality-Diversity (QD) optimization algorithms in Jax.
no code implementations • 27 Mar 2023 • Valentin Macé, Raphaël Boige, Felix Chalumeau, Thomas Pierrot, Guillaume Richard, Nicolas Perrin-Gilbert
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 behavior space.
no code implementations • 24 Nov 2022 • Felix Chalumeau, Thomas Pierrot, Valentin Macé, Arthur Flajolet, Karim Beguir, Antoine Cully, Nicolas Perrin-Gilbert
Exploration is at the heart of several domains trying to solve control problems such as Reinforcement Learning and QD methods are promising candidates to overcome the challenges associated.
1 code implementation • 6 Oct 2022 • Felix Chalumeau, Raphael Boige, Bryan Lim, Valentin Macé, Maxime Allard, Arthur Flajolet, Antoine Cully, Thomas Pierrot
Recent work has shown that training a mixture of policies, as opposed to a single one, that are driven to explore different regions of the state-action space can address this shortcoming by generating a diverse set of behaviors, referred to as skills, that can be collectively used to great effect in adaptation tasks or for hierarchical planning.
no code implementations • 1 Jan 2021 • Thomas Pierrot, Valentin Macé, Jean-Baptiste Sevestre, Louis Monier, Alexandre Laterre, Nicolas Perrin, Karim Beguir, Olivier Sigaud
Very large action spaces constitute a critical challenge for deep Reinforcement Learning (RL) algorithms.
no code implementations • 1 Jan 2021 • Thomas Pierrot, Valentin Macé, Geoffrey Cideron, Nicolas Perrin, Karim Beguir, Olivier Sigaud
The QD part contributes structural biases by decoupling the search for diversity from the search for high return, resulting in efficient management of the exploration-exploitation trade-off.
1 code implementation • NeurIPS 2021 • Thomas Pierrot, Valentin Macé, Félix Chalumeau, Arthur Flajolet, Geoffrey Cideron, Karim Beguir, Antoine Cully, Olivier Sigaud, Nicolas Perrin-Gilbert
This paper proposes a novel algorithm, QDPG, which combines the strength of Policy Gradient algorithms and Quality Diversity approaches to produce a collection of diverse and high-performing neural policies in continuous control environments.
no code implementations • EMNLP (IWSLT) 2019 • Valentin Macé, Christophe Servan
In Machine Translation, considering the document as a whole can help to resolve ambiguities and inconsistencies.