2023
Flageat, M., & Cully, A. (2023). Uncertain Quality-Diversity: Evaluation methodology and new methods for Quality-Diversity in Uncertain Domains. IEEE Transactions on Evolutionary Computation.
Flageat, M., Chalumeau, F., & Cully, A. (2023). Empirical analysis of PGA-MAP-Elites for Neuroevolution in Uncertain Domains. ACM Transactions on Evolutionary Learning, 3(1), 1–32.
Flageat*, M., Lim*, B., & Cully, A. (2023). Understanding the Synergies between Quality-Diversity and Deep Reinforcement Learning. Proceedings of the Genetic and Evolutionary Computation Conference.
Flageat*, M., Grillotti*, L., Lim, B., & Cully, A. (2023). Don’t Bet on Luck Alone: Enhancing Behavioral Reproducibility of Quality-Diversity Solutions in Uncertain Domains. Proceedings of the Genetic and Evolutionary Computation Conference.
Faldor, M., Chalumeau, F., Flageat, M., & Cully, A. (2023). MAP-Elites with Descriptor-Conditioned Gradients and Archive Distillation into a Single Policy. Proceedings of the Genetic and Evolutionary Computation Conference.
Flageat*, M., Lim*, B., & Cully, A. (2023). Multiple Hands Make Light Work: Enhancing Quality and Diversity using MAP-Elites with Multiple Parallel Evolution Strategies. ArXiv Preprint.
2022
Flageat*, M., Lim*, B., & Cully, A. (2022). Efficient Exploration using Model-Based Quality-Diversity with Gradients. Deep Reinforcement Learning Workshop NeurIPS 2022.
Flageat*, M., Lim*, B., Grillotti, L., Allard, M., Smith, S. C., & Cully, A. (2022). Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning. QD Benchmark Workshop - Genetic and Evolutionary Computation Conference.
2020
Flageat, M., & Cully, A. (2020). Fast and stable MAP-Elites in noisy domains using deep grids. ALIFE 2020: The 2020 Conference on Artificial Life, 273–282.
Flageat, M., Arulkumaran, K., & Bharath, A. A. (2020). Incorporating Human Priors into Deep Reinforcement Learning for Robotic Control. ESANN, 229–234.