5 papers
Preference-Conditioned Gradient Variations for Multi-Objective Quality-Diversity
Hannah Janmohamed, Maxence Faldor, Thomas Pierrot +1
In a variety of domains, from robotics to finance, Quality-Diversity algorithms have been used to generate collections of both diverse and high-performing solutions. Multi-Objectiv…
Time to Play: Simulating Early-Life Animal Dynamics Enhances Robotics Locomotion Discovery
Paul Templier, Hannah Janmohamed, David Labonte +1
Developmental changes in body morphology profoundly shape locomotion in animals, yet artificial agents and robots are typically trained under static physical parameters. Inspired b…
Multi-Objective Quality-Diversity in Unstructured and Unbounded Spaces
Hannah Janmohamed, Antoine Cully
Quality-Diversity algorithms are powerful tools for discovering diverse, high-performing solutions. Recently, Multi-Objective Quality-Diversity (MOQD) extends QD to problems with s…
Exploring the Performance-Reproducibility Trade-off in Quality-Diversity
Manon Flageat, Hannah Janmohamed, Bryan Lim +1
Quality-Diversity (QD) algorithms have exhibited promising results across many domains and applications. However, uncertainty in fitness and behaviour estimations of solutions rema…
Dominated Novelty Search: Rethinking Local Competition in Quality-Diversity
Ryan Bahlous-Boldi, Maxence Faldor, Luca Grillotti +4
Quality-Diversity is a family of evolutionary algorithms that generate diverse, high-performing solutions through local competition principles inspired by natural evolution. While…