38 citations · 96 across the 20 of their papers we have counts for
8 papers · 1 filter
Mix-ME: Quality-Diversity for Multi-Agent Learning
Garðar Ingvarsson, Mikayel Samvelyan, Bryan Lim +3
In many real-world systems, such as adaptive robotics, achieving a single, optimised solution may be insufficient. Instead, a diverse set of high-performing solutions is often requ…
QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration
Felix Chalumeau, Bryan Lim, Raphael Boige +7
QDax is an open-source library with a streamlined and modular API for Quality-Diversity (QD) optimization algorithms in Jax. The library serves as a versatile tool for optimization…
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…
Uncertain Quality-Diversity: Evaluation methodology and new methods for Quality-Diversity in Uncertain Domains
Manon Flageat, Antoine Cully
Quality-Diversity optimisation (QD) has proven to yield promising results across a broad set of applications. However, QD approaches struggle in the presence of uncertainty in the…
Understanding the Synergies between Quality-Diversity and Deep Reinforcement Learning
Bryan Lim, Manon Flageat, Antoine Cully
The synergies between Quality-Diversity (QD) and Deep Reinforcement Learning (RL) have led to powerful hybrid QD-RL algorithms that have shown tremendous potential, and brings the…
MAP-Elites with Descriptor-Conditioned Gradients and Archive Distillation into a Single Policy
Maxence Faldor, Félix Chalumeau, Manon Flageat +1
Quality-Diversity algorithms, such as MAP-Elites, are a branch of Evolutionary Computation generating collections of diverse and high-performing solutions, that have been successfu…
Enhancing MAP-Elites with Multiple Parallel Evolution Strategies
Manon Flageat, Bryan Lim, Antoine Cully
With the development of fast and massively parallel evaluations in many domains, Quality-Diversity (QD) algorithms, that already proved promising in a large range of applications,…