Lim, Bryan

11 publications

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

MLOSS 2024 QDax: A Library for Quality-Diversity and Population-Based Algorithms with Hardware Acceleration Felix Chalumeau, Bryan Lim, Raphaël Boige, Maxime Allard, Luca Grillotti, Manon Flageat, Valentin Macé, Guillaume Richard, Arthur Flajolet, Thomas Pierrot, Antoine Cully

TMLR 2023 Accelerated Quality-Diversity Through Massive Parallelism Bryan Lim, Maxime Allard, Luca Grillotti, Antoine Cully

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

ICLR 2023 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

ICLRW 2022 Accelerated Quality-Diversity for Robotics Through Massive Parallelism Bryan Lim, Maxime Allard, Luca Grillotti, Antoine Cully

NeurIPSW 2022 Efficient Exploration Using Model-Based Quality-Diversity with Gradients Bryan Lim, Manon Flageat, Antoine Cully

ICLRW 2022 Learning to Walk Autonomously via Reset-Free Quality-Diversity Bryan Lim, Alexander Reichenbach, Antoine Cully

CoRL 2020 Tactile Object Pose Estimation from the First Touch with Geometric Contact Rendering Maria Bauza Villalonga, Alberto Rodriguez, Bryan Lim, Eric Valls, Theo Sechopoulos

MLHC 2018 Disease-Atlas: Navigating Disease Trajectories Using Deep Learning Bryan Lim, Mihaela van der Schaar

NeurIPS 2018 Forecasting Treatment Responses over Time Using Recurrent Marginal Structural Networks Bryan Lim