Research

Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill Discovery

Felix Chalumeau*, Raphael Boige*, Bryan Lim, Valentin Macé, Maxime Allard, Arthur Flajolet, Antoine Cully, Thomas Pierrot
ICLR, Notable-Top-25% (Spotlight), 2023

Paper

Accelerated Quality-Diversity for Robotics through Massive Parallelism

Bryan Lim, Maxime Allard, Luca Grillotti, Antoine Cully 

Published in Transactions on Machine Learning Research, 2023
The Genetic and Evolutionary Computation Conference (GECCO) – Poster, 2022
Agent Learning in Open-Endedness Workshop, ICLR (Spotlight Talk), 2022

Code : QDax | Paper

Online Damage Recovery for Physical Robots with Hierarchical Quality-Diversity

Maxime Allard, Simón C. Smith, Konstantinos Chatzilygeroudis, Bryan Lim, Antoine Cully
Pre-Print, 2022

Code | Paper

Hierarchical Quality-Diversity for Online Damage Recovery

Maxime Allard, Simón C. Smith, Konstantinos Chatzilygeroudis, Antoine Cully
The Genetic and Evolutionary Computation Conference (GECCO) – Full Paper, Oral Presentation & Won Best Paper Award, 2022

Code | Paper

Benchmarking Quality-Diversity Algorithms
on Neuroevolution for Reinforcement Learning

Manon Flageat*,Bryan Lim*, Luca Grillotti, Maxime Allard, Simón C. Smith, Antoine Cully 
Quality Diversity Algorithm Benchmarks Workshop, The Genetic and Evolutionary Computation Conference (GECCO) 2022

Paper

Other Research

  • Classification Of Sparsely Labeled Text Documents While Preserving Semantics
    JJ Thomas, AE Petrov, W Wang, M Allard – US Patent US 11455527 B2, 2022
  • Providing semantic completeness assessment with minimal domain-specific data
    JJ Thomas, M Allard, AE Petrov, VR Dandin, W Wang – US Patent US 11514246 B2, 2022
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