Jack Parker-Holder

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Aldo PacchianoMicrosoft ResearchVerified email at microsoft.com
Krzysztof ChoromanskiGoogle Brain Robotics New York & Columbia UniversityVerified email at columbia.edu
Stephen RobertsProfessor of Engineering Science (Machine Learning, Information Engineering), University of OxfordVerified email at robots.ox.ac.uk
Philip J. BallPhD Student, University of OxfordVerified email at robots.ox.ac.uk
Yunhao TangResearch Scientist, DeepMindVerified email at columbia.edu
Tim RocktäschelResearch Scientist at Google DeepMind, Associate Professor at UCLVerified email at cs.ucl.ac.uk
Edward GrefenstetteHead of Machine Learning, Cohere; Honorary Professor, UCLVerified email at cohere.com
Jakob FoersterAssociate Professor, University of OxfordVerified email at eng.ox.ac.uk
Minqi JiangUniversity College London & FAIR, Meta AIVerified email at ucl.ac.uk
Mikayel SamvelyanMeta AI & UCLVerified email at meta.com
Xingyou (Richard) SongSenior Research Scientist, Google DeepMind (Brain Team)Verified email at google.com
Vu NguyenAmazonVerified email at robots.ox.ac.uk
Michael DennisPhD Candidate, UC BerkeleyVerified email at cs.berkeley.edu
Cong LuUniversity of OxfordVerified email at stats.ox.ac.uk
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Ted MoskovitzGatsby Unit, UCLVerified email at gatsby.ucl.ac.uk
Michael ArbelInria - Univ. Grenoble AlpesVerified email at inria.fr
Aleksandra FaustGoogle Brain, Mountain View, CAVerified email at google.com

Jack Parker-Holder

Research Scientist at DeepMind

Verified email at google.com - Homepage

Reinforcement Learning Quality Diversity Open-Endedness World Models


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Effective Diversity in Population Based Reinforcement Learning J Parker-Holder, A Pacchiano, K Choromanski, S Roberts NeurIPS 2020 (Spotlight), 2020	109	2020
Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits J Parker-Holder, V Nguyen, S Roberts NeurIPS 2020, 2020	49	2020
MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research M Samvelyan, R Kirk, V Kurin, J Parker-Holder, M Jiang, E Hambro, ... NeurIPS 2021 (Datasets and Benchmarks), 2021	42	2021
From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization KM Choromanski, A Pacchiano, J Parker-Holder, Y Tang, ... Advances in Neural Information Processing Systems, 10299-10309, 2019	42	2019
Ready Policy One: World Building Through Active Learning P Ball, J Parker-Holder, A Pacchiano, K Choromanski, S Roberts ICML 2020, 2020	40	2020
Evolving Curricula with Regret-Based Environment Design J Parker-Holder, M Jiang, M Dennis, M Samvelyan, J Foerster, ... ICML 2022, 2022	36	2022
Provably Robust Blackbox Optimization for Reinforcement Learning K Choromanski, A Pacchiano, J Parker-Holder*, Y Tang, D Jain, Y Yang, ... Conference on Robot Learning, 683-696, 2019	33*	2019
Automated Reinforcement Learning (AutoRL): A Survey and Open Problems J Parker-Holder, R Rajan, X Song*, A Biedenkapp, Y Miao, T Eimer, ... JAIR, 2022	31	2022
Replay-Guided Adversarial Environment Design M Jiang, M Dennis, J Parker-Holder, J Foerster, E Grefenstette, ... NeurIPS 2021, 2021	30	2021
Towards Tractable Optimism in Model-Based Reinforcement Learning A Pacchiano, P Ball, J Parker-Holder*, K Choromanski, S Roberts UAI 2021, 2020	28*	2020
Learning to Score Behaviors for Guided Policy Optimization A Pacchiano, J Parker-Holder, Y Tang*, K Choromanski, ... International Conference on Machine Learning, 7445-7454, 2020	27	2020
Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment PJ Ball, C Lu, J Parker-Holder, S Roberts ICML 2021, 2021	26	2021
Tactical Optimism and Pessimism for Deep Reinforcement Learning T Moskovitz, J Parker-Holder, A Pacchiano, M Arbel, MI Jordan NeurIPS 2021, 2021	24*	2021
Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian J Parker-Holder*, L Metz, C Resnick, H Hu, A Lerer, A Letcher, ... Advances in Neural Information Processing Systems 33, 2020	22	2020
Same State, Different Task: Continual Reinforcement Learning without Interference S Kessler, J Parker-Holder, P Ball, S Zohren, SJ Roberts AAAI 2022 (Oral), 2022	15	2022
Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations C Lu, PJ Ball, TGJ Rudner, J Parker-Holder, MA Osborne, YW Teh RSS L-DOD Workshop (Best Paper Award), 2022	12	2022
Revisiting Design Choices in Model-Based Offline Reinforcement Learning C Lu, PJ Ball, J Parker-Holder, MA Osborne, SJ Roberts ICLR 2022 (Spotlight), 2022	12*	2022
Human-Timescale Adaptation in an Open-Ended Task Space AA Team, J Bauer, K Baumli, S Baveja, F Behbahani, A Bhoopchand, ... ICML 2023, 2023	10*	2023
ES-ENAS: Blackbox Optimization over Hybrid Spaces via Combinatorial and Continuous Evolution X Song, KM Choromanski, J Parker-Holder, Y Tang, D Peng, D Jain, ...	9*	2021
Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL J Parker-Holder, V Nguyen, S Desai, S Roberts NeurIPS 2021, 2021	9	2021

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