1 citations · 4 across the 7 of their papers we have counts for
7 papers
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…
Large Language Models as In-context AI Generators for Quality-Diversity
Bryan Lim, Manon Flageat, Antoine Cully
Quality-Diversity (QD) approaches are a promising direction to develop open-ended processes as they can discover archives of high-quality solutions across diverse niches. While alr…
Beyond Expected Return: Accounting for Policy Reproducibility when Evaluating Reinforcement Learning Algorithms
Manon Flageat, Bryan Lim, Antoine Cully
Many applications in Reinforcement Learning (RL) usually have noise or stochasticity present in the environment. Beyond their impact on learning, these uncertainties lead the exact…
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…
Quality-Diversity Optimisation on a Physical Robot Through Dynamics-Aware and Reset-Free Learning
Simón C. Smith, Bryan Lim, Hannah Janmohamed +1
Learning algorithms, like Quality-Diversity (QD), can be used to acquire repertoires of diverse robotics skills. This learning is commonly done via computer simulation due to the l…
Online Damage Recovery for Physical Robots with Hierarchical Quality-Diversity
Maxime Allard, Simón C. Smith, Konstantinos Chatzilygeroudis +2
In real-world environments, robots need to be resilient to damages and robust to unforeseen scenarios. Quality-Diversity (QD) algorithms have been successfully used to make robots…
Efficient Learning of Locomotion Skills through the Discovery of Diverse Environmental Trajectory Generator Priors
Shikha Surana, Bryan Lim, Antoine Cully
Data-driven learning based methods have recently been particularly successful at learning robust locomotion controllers for a variety of unstructured terrains. Prior work has shown…