Incorporating Human Priors into Deep Reinforcement Learning for Robotic Control. - Manon Flageat; Kai Arulkumaran; Anil A. Bharath (ESANN, 2020)

Fast and stable MAP-Elites in noisy domains using deep grids. - Manon Flageat; Antoine Cully (ALIFE, 2020)

Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning. - Manon Flageat; Bryan Lim; Luca Grillotti; Maxime Allard; Simón C. Smith; Antoine Cully (CoRR, 2022)

Efficient Exploration using Model-Based Quality-Diversity with Gradients. - Bryan Lim; Manon Flageat; Antoine Cully (CoRR, 2022)

MAP-Elites with Descriptor-Conditioned Gradients and Archive Distillation into a Single Policy. - Maxence Faldor; Félix Chalumeau; Manon Flageat; Antoine Cully (GECCO, 2023)

Benchmark Tasks for Quality-Diversity Applied to Uncertain Domains. - Manon Flageat; Luca Grillotti; Antoine Cully (GECCO Companion, 2023)

Don't Bet on Luck Alone: Enhancing Behavioral Reproducibility of Quality-Diversity Solutions in Uncertain Domains. - Luca Grillotti; Manon Flageat; Bryan Lim; Antoine Cully (GECCO, 2023)

Understanding the Synergies between Quality-Diversity and Deep Reinforcement Learning. - Bryan Lim; Manon Flageat; Antoine Cully (GECCO, 2023)

Multiple Hands Make Light Work: Enhancing Quality and Diversity using MAP-Elites with Multiple Parallel Evolution Strategies. - Manon Flageat; Bryan Lim; Antoine Cully (CoRR, 2023)

QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration. - Félix Chalumeau; Bryan Lim; Raphaël Boige; Maxime Allard; Luca Grillotti; Manon Flageat; Valentin Macé; Arthur Flajolet; Thomas Pierrot; Antoine Cully (CoRR, 2023)

Mix-ME: Quality-Diversity for Multi-Agent Learning. - Garðar Ingvarsson; Mikayel Samvelyan; Bryan Lim; Manon Flageat; Antoine Cully; Tim Rocktäschel (CoRR, 2023)

Empirical analysis of PGA-MAP-Elites for Neuroevolution in Uncertain Domains. - Manon Flageat; Félix Chalumeau; Antoine Cully (ACM Trans. Evol. Learn. Optim., 2023)

Beyond Expected Return: Accounting for Policy Reproducibility When Evaluating Reinforcement Learning Algorithms. - Manon Flageat; Bryan Lim; Antoine Cully (AAAI, 2024)

Enhancing MAP-Elites with Multiple Parallel Evolution Strategies. - Manon Flageat; Bryan Lim; Antoine Cully (GECCO, 2024)

Evolutionary Reinforcement Learning. - Manon Flageat; Bryan Lim; Antoine Cully (GECCO Companion, 2024)

Large Language Models as In-context AI Generators for Quality-Diversity. - Bryan Lim; Manon Flageat; Antoine Cully (CoRR, 2024)

Exploring the Performance-Reproducibility Trade-off in Quality-Diversity. - Manon Flageat; Hannah Janmohamed; Bryan Lim; Antoine Cully (CoRR, 2024)

QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration. - Félix Chalumeau; Bryan Lim; Raphaël Boige; Maxime Allard; Luca Grillotti; Manon Flageat; Valentin Macé; Guillaume Richard; Arthur Flajolet; Thomas Pierrot; Antoine Cully (J. Mach. Learn. Res., 2024)

Uncertain Quality-Diversity: Evaluation Methodology and New Methods for Quality-Diversity in Uncertain Domains. - Manon Flageat; Antoine Cully (IEEE Trans. Evol. Comput., 2024)

Extract-QD Framework: A Generic Approach for Quality-Diversity in Noisy, Stochastic or Uncertain Domains. - Manon Flageat; Johann Huber; François Hélénon; Stéphane Doncieux; Antoine Cully (GECCO, 2025)

Synergizing Quality-Diversity with Descriptor-Conditioned Reinforcement Learning. - Maxence Faldor; Félix Chalumeau; Manon Flageat; Antoine Cully (ACM Trans. Evol. Learn. Optim., 2025)