Cong Lu
Cited by
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Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces
X Wan, V Nguyen, H Ha, B Ru, C Lu, MA Osborne
International Conference on Machine Learning (ICML) 38, 10663-10674, 2021
Exploration in approximate hyper-state space for meta reinforcement learning
LM Zintgraf, L Feng, C Lu, M Igl, K Hartikainen, K Hofmann, S Whiteson
International Conference on Machine Learning, 12991-13001, 2020
Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment
PJ Ball*, C Lu*, J Parker-Holder, S Roberts
International Conference on Machine Learning (ICML) 38, 2021
On pathologies in KL-regularized reinforcement learning from expert demonstrations
TGJ Rudner*, C Lu*, M Osborne, Y Gal, Y Teh
Advances in Neural Information Processing Systems 34, 2021
Revisiting Design Choices in Offline Model Based Reinforcement Learning
C Lu*, P Ball*, J Parker-Holder, M Osborne, S Roberts
ICLR 2022 (Spotlight), 2022
VariBAD: Variational Bayes-Adaptive Deep RL via Meta-Learning
L Zintgraf, S Schulze, C Lu, L Feng, M Igl, K Shiarlis, Y Gal, K Hofmann, ...
Journal of Machine Learning Research 22 (289), 1-39, 2021
Challenges and opportunities in offline reinforcement learning from visual observations
C Lu, PJ Ball, TGJ Rudner, J Parker-Holder, MA Osborne, YW Teh
Workshop on Learning from Diverse, Offline Data @ RSS2022 (Outstanding Paper), 2022
Bayesian Generational Population-Based Training
X Wan, C Lu, J Parker-Holder, PJ Ball, V Nguyen, B Ru, MA Osborne
AutoML 2022, 2022
Go-Explore Complex 3D Game Environments for Automated Reachability Testing
C Lu, R Georgescu, J Verwey
AIIDE Workshop on Experimental AI in Games 2022, 2022
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