13 citations · 58 across the 14 of their papers we have counts for
8 papers · 1 filter
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…
Quality with Just Enough Diversity in Evolutionary Policy Search
Paul Templier, Luca Grillotti, Emmanuel Rachelson +2
Evolution Strategies (ES) are effective gradient-free optimization methods that can be competitive with gradient-based approaches for policy search. ES only rely on the total episo…
Benchmark tasks for Quality-Diversity applied to Uncertain domains
Manon Flageat, Luca Grillotti, Antoine Cully
While standard approaches to optimisation focus on producing a single high-performing solution, Quality-Diversity (QD) algorithms allow large diverse collections of such solutions…
Don't Bet on Luck Alone: Enhancing Behavioral Reproducibility of Quality-Diversity Solutions in Uncertain Domains
Luca Grillotti, Manon Flageat, Bryan Lim +1
Quality-Diversity (QD) algorithms are designed to generate collections of high-performing solutions while maximizing their diversity in a given descriptor space. However, in the pr…
Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning
Manon Flageat, Bryan Lim, Luca Grillotti +3
We present a Quality-Diversity benchmark suite for Deep Neuroevolution in Reinforcement Learning domains for robot control. The suite includes the definition of tasks, environments…
Relevance-guided Unsupervised Discovery of Abilities with Quality-Diversity Algorithms
Luca Grillotti, Antoine Cully
Quality-Diversity algorithms provide efficient mechanisms to generate large collections of diverse and high-performing solutions, which have shown to be instrumental for solving do…
Accelerated Quality-Diversity through Massive Parallelism
Bryan Lim, Maxime Allard, Luca Grillotti +1
Quality-Diversity (QD) optimization algorithms are a well-known approach to generate large collections of diverse and high-quality solutions. However, derived from evolutionary com…
Unsupervised Behaviour Discovery with Quality-Diversity Optimisation
Luca Grillotti, Antoine Cully
Quality-Diversity algorithms refer to a class of evolutionary algorithms designed to find a collection of diverse and high-performing solutions to a given problem. In robotics, suc…