Bryan Lim

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 47

Top Topics

Reinforcement Learning

Quadruped Robot

Top Venues

GECCO Companion

Publications

Manon Flageat, Bryan Lim, Antoine Cully
Beyond Expected Return: Accounting for Policy Reproducibility When Evaluating Reinforcement Learning Algorithms. AAAI (2024)
Bryan Lim, Manon Flageat, Antoine Cully
Large Language Models as In-context AI Generators for Quality-Diversity. CoRR (2024)
Manon Flageat, Bryan Lim, Antoine Cully
Evolutionary Reinforcement Learning. GECCO Companion (2024)
Manon Flageat, Bryan Lim, Antoine Cully
Enhancing MAP-Elites with Multiple Parallel Evolution Strategies. GECCO (2024)
Manon Flageat, Bryan Lim, Antoine Cully
Multiple Hands Make Light Work: Enhancing Quality and Diversity using MAP-Elites with Multiple Parallel Evolution Strategies. CoRR (2023)
Bryan Lim, Manon Flageat, Antoine Cully
Understanding the Synergies between Quality-Diversity and Deep Reinforcement Learning. GECCO (2023)
Simón C. Smith, Bryan Lim, Hannah Janmohamed, Antoine Cully
Quality-Diversity Optimisation on a Physical Robot Through Dynamics-Aware and Reset-Free Learning. GECCO Companion (2023)
Simón C. Smith, Bryan Lim, Hannah Janmohamed, Antoine Cully
Quality-Diversity Optimisation on a Physical Robot Through Dynamics-Aware and Reset-Free Learning. CoRR (2023)
Bryan Lim, Maxime Allard, Luca Grillotti, Antoine Cully
Accelerated Quality-Diversity through Massive Parallelism. Trans. Mach. Learn. Res. 2023 (2023)
Luca Grillotti, Manon Flageat, Bryan Lim, Antoine Cully
Don't Bet on Luck Alone: Enhancing Behavioral Reproducibility of Quality-Diversity Solutions in Uncertain Domains. GECCO (2023)
Maxime Allard, Simón C. Smith, Konstantinos I. Chatzilygeroudis, Bryan Lim, Antoine Cully
Online Damage Recovery for Physical Robots with Hierarchical Quality-Diversity. ACM Trans. Evol. Learn. Optim. 3 (2) (2023)
Luca Grillotti, Manon Flageat, Bryan Lim, Antoine Cully
Don't Bet on Luck Alone: Enhancing Behavioral Reproducibility of Quality-Diversity Solutions in Uncertain Domains. CoRR (2023)
Manon Flageat, Bryan Lim, Antoine Cully
Beyond Expected Return: Accounting for Policy Reproducibility when Evaluating Reinforcement Learning Algorithms. CoRR (2023)
Shikha Surana, Bryan Lim, Antoine Cully
Efficient Learning of Locomotion Skills through the Discovery of Diverse Environmental Trajectory Generator Priors. ICRA (2023)
Garðar Ingvarsson, Mikayel Samvelyan, Bryan Lim, Manon Flageat, Antoine Cully, Tim Rocktäschel
Mix-ME: Quality-Diversity for Multi-Agent Learning. CoRR (2023)
Bryan Lim, Manon Flageat, Antoine Cully
Understanding the Synergies between Quality-Diversity and Deep Reinforcement Learning. CoRR (2023)
Félix Chalumeau, Bryan Lim, Raphaël Boige, Maxime Allard, Luca Grillotti, Manon Flageat, Valentin Macé, Arthur Flajolet, Thomas Pierrot, Antoine Cully
QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration. CoRR (2023)
Félix Chalumeau, Raphaël Boige, Bryan Lim, Valentin Macé, Maxime Allard, Arthur Flajolet, Antoine Cully, Thomas Pierrot
Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill Discovery. ICLR (2023)
Shikha Surana, Bryan Lim, Antoine Cully
Efficient Learning of Locomotion Skills through the Discovery of Diverse Environmental Trajectory Generator Priors. CoRR (2022)
Manon Flageat, Bryan Lim, Luca Grillotti, Maxime Allard, Simón C. Smith, Antoine Cully
Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning. CoRR (2022)
Bryan Lim, Luca Grillotti, Lorenzo Bernasconi, Antoine Cully
Dynamics-Aware Quality-Diversity for Efficient Learning of Skill Repertoires. ICRA (2022)
Bryan Lim, Maxime Allard, Luca Grillotti, Antoine Cully
QDax: on the benefits of massive parallelization for quality-diversity. GECCO Companion (2022)
Bryan Lim, Alexander Reichenbach, Antoine Cully
Learning to walk autonomously via reset-free quality-diversity. GECCO (2022)
Bryan Lim, Manon Flageat, Antoine Cully
Efficient Exploration using Model-Based Quality-Diversity with Gradients. CoRR (2022)
Maxime Allard, Simón C. Smith, Konstantinos I. Chatzilygeroudis, Bryan Lim, Antoine Cully
Online Damage Recovery for Physical Robots with Hierarchical Quality-Diversity. CoRR (2022)
Bryan Lim, Alexander Reichenbach, Antoine Cully
Learning to Walk Autonomously via Reset-Free Quality-Diversity. CoRR (2022)
Bryan Lim, Maxime Allard, Luca Grillotti, Antoine Cully
Accelerated Quality-Diversity for Robotics through Massive Parallelism. CoRR (2022)
Félix Chalumeau, Raphael Boige, Bryan Lim, Valentin Macé, Maxime Allard, Arthur Flajolet, Antoine Cully, Thomas Pierrot
Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill Discovery. CoRR (2022)
Bryan Lim, Luca Grillotti, Lorenzo Bernasconi, Antoine Cully
Dynamics-Aware Quality-Diversity for Efficient Learning of Skill Repertoires. CoRR (2021)
Daniel Poh, Bryan Lim, Stefan Zohren, Stephen J. Roberts
Enhancing Cross-Sectional Currency Strategies by Ranking Refinement with Transformer-based Architectures. CoRR (2021)
Zihao Zhang, Bryan Lim, Stefan Zohren
Deep Learning for Market by Order Data. CoRR (2021)
Donghyun Kim, D. Carballo, Jared Di Carlo, Benjamin Katz, Gerardo Bledt, Bryan Lim, Sangbae Kim
Vision Aided Dynamic Exploration of Unstructured Terrain with a Small-Scale Quadruped Robot. ICRA (2020)
Bryan Lim, Stefan Zohren, Stephen Roberts
Detecting Changes in Asset Co-Movement Using the Autoencoder Reconstruction Ratio. CoRR (2020)
Maria Bauzá Villalonga, Alberto Rodriguez, Bryan Lim, Eric Valls, Theo Sechopoulos
Tactile Object Pose Estimation from the First Touch with Geometric Contact Rendering. CoRL (2020)
Bryan Lim, Stefan Zohren, Stephen Roberts
Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction. IJCNN (2020)
Maria Bauzá, Eric Valls, Bryan Lim, Theo Sechopoulos, Alberto Rodriguez
Tactile Object Pose Estimation from the First Touch with Geometric Contact Rendering. CoRR (2020)
Thomas Dudzik, Matthew Chignoli, Gerardo Bledt, Bryan Lim, Adam Miller, Donghyun Kim, Sangbae Kim
Robust Autonomous Navigation of a Small-Scale Quadruped Robot in Real-World Environments. IROS (2020)
Daniel Poh, Bryan Lim, Stefan Zohren, Stephen J. Roberts
Building Cross-Sectional Systematic Strategies By Learning to Rank. CoRR (2020)
Bryan Lim, Stefan Zohren
Time Series Forecasting With Deep Learning: A Survey. CoRR (2020)
Bryan Lim, Sercan Ömer Arik, Nicolas Loeff, Tomas Pfister
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting. CoRR (2019)
Bryan Lim, Stefan Zohren, Stephen J. Roberts
Population-based Global Optimisation Methods for Learning Long-term Dependencies with RNNs. CoRR (2019)
Bryan Lim, Stefan Zohren, Stephen J. Roberts
Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction. CoRR (2019)
Bryan Lim, Stefan Zohren, Stephen J. Roberts
Enhancing Time Series Momentum Strategies Using Deep Neural Networks. CoRR (2019)
Bryan Lim, Mihaela van der Schaar
Forecasting Disease Trajectories in Alzheimer's Disease Using Deep Learning. CoRR (2018)
Bryan Lim
Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks. NeurIPS (2018)
Bryan Lim, Mihaela van der Schaar
Disease-Atlas: Navigating Disease Trajectories using Deep Learning. MLHC (2018)
Bryan Lim, Mihaela van der Schaar
Disease-Atlas: Navigating Disease Trajectories with Deep Learning. CoRR (2018)