Cong Lu

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Philip J. BallResearch Scientist at Google DeepMindVerified email at google.com
Jack Parker-HolderGoogle DeepMind, UCLVerified email at google.com
Michael A OsborneProfessor of Machine Learning, University of OxfordVerified email at robots.ox.ac.uk
Shimon WhitesonProfessor of Computer Science, University of Oxford / Senior Staff Research Scientist, WaymoVerified email at cs.ox.ac.uk
Maximilian IglWaymo ResearchVerified email at eng.ox.ac.uk
Luisa ZintgrafDeepMindVerified email at deepmind.com
Leo FengMila -- University of MontrealVerified email at mila.quebec
Stephen RobertsProfessor of Engineering Science (Machine Learning, Information Engineering), University of OxfordVerified email at robots.ox.ac.uk
Xingchen WanUniversity of Oxford; GoogleVerified email at google.com
Yee Whye TehProfessor of Statistical Machine Learning, Oxford, Research Scientist, DeepMindVerified email at stats.ox.ac.uk
Tim G. J. RudnerFaculty Fellow, New York UniversityVerified email at nyu.edu
Raluca GeorgescuMicrosoft ResearchVerified email at microsoft.com

Cong Lu

University of Oxford

Verified email at stats.ox.ac.uk - Homepage

Reinforcement Learning Offline RL World Models Bayesian Optimization Deep Learning


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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	40	2020
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	36	2021
Revisiting Design Choices in Offline Model Based Reinforcement Learning C Lu, PJ Ball, J Parker-Holder, M Osborne, S Roberts ICLR 2022 (Spotlight), 2022	32*	2022
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	29	2021
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, ... The Journal of Machine Learning Research 22 (1), 13198-13236, 2021	24	2021
Challenges and opportunities in offline reinforcement learning from visual observations C Lu, PJ Ball, TGJ Rudner, J Parker-Holder, MA Osborne, YW Teh Transactions on Machine Learning Research, 2022	20	2022
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	16	2021
Bayesian Generational Population-Based Training X Wan, C Lu, J Parker-Holder, PJ Ball, V Nguyen, B Ru, MA Osborne AutoML 2022, 2022	13	2022
Synthetic Experience Replay C Lu, PJ Ball, YW Teh, J Parker-Holder NeurIPS 2023, 2023	5	2023
Go-Explore Complex 3D Game Environments for Automated Reachability Testing C Lu, R Georgescu, J Verwey IEEE Transactions on Games, 2022	3	2022
Policy-Guided Diffusion M Jackson, M Matthews, C Lu, J Foerster, S Whiteson 6th Robot Learning Workshop @ NeurIPS 2023, 2023		2023
RAVL: Reach-Aware Value Learning for the Edge-of-Reach Problem in Offline Model-Based Reinforcement Learning A Sims, C Lu, YW Teh Second Agent Learning in Open-Endedness Workshop @ NeurIPS 2023, 2023		2023

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