Jack Parker-Holder

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 73

Top Topics

Reinforcement Learning

Top Venues

Publications

Mikayel Samvelyan, Davide Paglieri, Minqi Jiang, Jack Parker-Holder, Tim Rocktäschel
Multi-Agent Diagnostics for Robustness via Illuminated Diversity. CoRR (2024)
Jakob Bauer, Kate Baumli, Feryal M. P. Behbahani, Avishkar Bhoopchand, Nathalie Bradley-Schmieg, Michael Chang, Natalie Clay, Adrian Collister, Vibhavari Dasagi, Lucy Gonzalez, Karol Gregor, Edward Hughes, Sheleem Kashem, Maria Loks-Thompson, Hannah Openshaw, Jack Parker-Holder, Shreya Pathak, Nicolas Perez Nieves, Nemanja Rakicevic, Tim Rocktäschel, Yannick Schroecker, Satinder Singh, Jakub Sygnowski, Karl Tuyls, Sarah York, Alexander Zacherl, Lei M. Zhang
Human-Timescale Adaptation in an Open-Ended Task Space. ICML (2023)
Matthew Thomas Jackson, Minqi Jiang, Jack Parker-Holder, Risto Vuorio, Chris Lu, Gregory Farquhar, Shimon Whiteson, Jakob N. Foerster
Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design. NeurIPS (2023)
Adaptive Agent Team, Jakob Bauer, Kate Baumli, Satinder Baveja, Feryal M. P. Behbahani, Avishkar Bhoopchand, Nathalie Bradley-Schmieg, Michael Chang, Natalie Clay, Adrian Collister, Vibhavari Dasagi, Lucy Gonzalez, Karol Gregor, Edward Hughes, Sheleem Kashem, Maria Loks-Thompson, Hannah Openshaw, Jack Parker-Holder, Shreya Pathak, Nicolas Perez Nieves, Nemanja Rakicevic, Tim Rocktäschel, Yannick Schroecker, Jakub Sygnowski, Karl Tuyls, Sarah York, Alexander Zacherl, Lei Zhang
Human-Timescale Adaptation in an Open-Ended Task Space. CoRR (2023)
Cong Lu, Philip J. Ball, Yee Whye Teh, Jack Parker-Holder
Synthetic Experience Replay. NeurIPS (2023)
Mikayel Samvelyan, Akbir Khan, Michael Dennis, Minqi Jiang, Jack Parker-Holder, Jakob N. Foerster, Roberta Raileanu, Tim Rocktäschel
MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning. CoRR (2023)
Cong Lu, Philip J. Ball, Jack Parker-Holder
Synthetic Experience Replay. CoRR (2023)
Matthew Thomas Jackson, Minqi Jiang, Jack Parker-Holder, Risto Vuorio, Chris Lu, Gregory Farquhar, Shimon Whiteson, Jakob Nicolaus Foerster
Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design. CoRR (2023)
Mikayel Samvelyan, Akbir Khan, Michael Dennis, Minqi Jiang, Jack Parker-Holder, Jakob Nicolaus Foerster, Roberta Raileanu, Tim Rocktäschel
MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning. ICLR (2023)
Ishita Mediratta, Minqi Jiang, Jack Parker-Holder, Michael Dennis, Eugene Vinitsky, Tim Rocktäschel
Stabilizing Unsupervised Environment Design with a Learned Adversary. CoLLAs (2023)
Kate Baumli, Satinder Baveja, Feryal M. P. Behbahani, Harris Chan, Gheorghe Comanici, Sebastian Flennerhag, Maxime Gazeau, Kristian Holsheimer, Dan Horgan, Michael Laskin, Clare Lyle, Hussain Masoom, Kay McKinney, Volodymyr Mnih, Alexander Neitz, Fabio Pardo, Jack Parker-Holder, John Quan, Tim Rocktäschel, Himanshu Sahni, Tom Schaul, Yannick Schroecker, Stephen Spencer, Richie Steigerwald, Luyu Wang, Lei Zhang
Vision-Language Models as a Source of Rewards. CoRR (2023)
Samuel Kessler, Mateusz Ostaszewski, Michal Pawel Bortkiewicz, Mateusz Zarski, Maciej Wolczyk, Jack Parker-Holder, Stephen J. Roberts, Piotr Milos
The Effectiveness of World Models for Continual Reinforcement Learning. CoLLAs (2023)
Ishita Mediratta, Minqi Jiang, Jack Parker-Holder, Michael Dennis, Eugene Vinitsky, Tim Rocktäschel
Stabilizing Unsupervised Environment Design with a Learned Adversary. CoRR (2023)
Yingchen Xu, Jack Parker-Holder, Aldo Pacchiano, Philip J. Ball, Oleh Rybkin, Stephen Roberts, Tim Rocktäschel, Edward Grefenstette
Learning General World Models in a Handful of Reward-Free Deployments. NeurIPS (2022)
Krzysztof Choromanski, Han Lin, Haoxian Chen, Tianyi Zhang, Arijit Sehanobish, Valerii Likhosherstov, Jack Parker-Holder, Tamás Sarlós, Adrian Weller, Thomas Weingarten
From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers. ICML (2022)
Jack Parker-Holder, Raghu Rajan, Xingyou Song, André Biedenkapp, Yingjie Miao, Theresa Eimer, Baohe Zhang, Vu Nguyen, Roberto Calandra, Aleksandra Faust, Frank Hutter, Marius Lindauer
Automated Reinforcement Learning (AutoRL): A Survey and Open Problems. CoRR (2022)
Xingchen Wan, Cong Lu, Jack Parker-Holder, Philip J. Ball, Vu Nguyen, Binxin Ru, Michael A. Osborne
Bayesian Generational Population-Based Training. AutoML (2022)
Xingchen Wan, Cong Lu, Jack Parker-Holder, Philip J. Ball, Vu Nguyen, Binxin Ru, Michael A. Osborne
Bayesian Generational Population-Based Training. CoRR (2022)
Jack Parker-Holder, Minqi Jiang, Michael Dennis, Mikayel Samvelyan, Jakob N. Foerster, Edward Grefenstette, Tim Rocktäschel
Evolving Curricula with Regret-Based Environment Design. ICML (2022)
Jaleh Zand, Jack Parker-Holder, Stephen J. Roberts
On-the-fly Strategy Adaptation for ad-hoc Agent Coordination. AAMAS (2022)
Samuel Kessler, Piotr Milos, Jack Parker-Holder, Stephen J. Roberts
The Surprising Effectiveness of Latent World Models for Continual Reinforcement Learning. CoRR (2022)
Jonathan Lorraine, Paul Vicol, Jack Parker-Holder, Tal Kachman, Luke Metz, Jakob N. Foerster
Lyapunov Exponents for Diversity in Differentiable Games. AAMAS (2022)
Michael Matthews, Mikayel Samvelyan, Jack Parker-Holder, Edward Grefenstette, Tim Rocktäschel
Hierarchical Kickstarting for Skill Transfer in Reinforcement Learning. CoRR (2022)
Jaleh Zand, Jack Parker-Holder, Stephen J. Roberts
On-the-fly Strategy Adaptation for ad-hoc Agent Coordination. CoRR (2022)
Minqi Jiang, Michael Dennis, Jack Parker-Holder, Andrei Lupu, Heinrich Küttler, Edward Grefenstette, Tim Rocktäschel, Jakob N. Foerster
Grounding Aleatoric Uncertainty for Unsupervised Environment Design. NeurIPS (2022)
Eric Hambro, Sharada P. Mohanty, Dmitrii Babaev, Minwoo Byeon, Dipam Chakraborty, Edward Grefenstette, Minqi Jiang, DaeJin Jo, Anssi Kanervisto, Jongmin Kim, Sungwoong Kim, Robert Kirk, Vitaly Kurin, Heinrich Küttler, Taehwon Kwon, Donghoon Lee, Vegard Mella, Nantas Nardelli, Ivan Nazarov, Nikita Ovsov, Jack Parker-Holder, Roberta Raileanu, Karolis Ramanauskas, Tim Rocktäschel, Danielle Rothermel, Mikayel Samvelyan, Dmitry Sorokin, Maciej Sypetkowski, Michal Sypetkowski
Insights From the NeurIPS 2021 NetHack Challenge. CoRR (2022)
Yingchen Xu, Jack Parker-Holder, Aldo Pacchiano, Philip J. Ball, Oleh Rybkin, Stephen J. Roberts, Tim Rocktäschel, Edward Grefenstette
Learning General World Models in a Handful of Reward-Free Deployments. CoRR (2022)
Jack Parker-Holder, Raghu Rajan, Xingyou Song, André Biedenkapp, Yingjie Miao, Theresa Eimer, Baohe Zhang, Vu Nguyen, Roberto Calandra, Aleksandra Faust, Frank Hutter, Marius Lindauer
Automated Reinforcement Learning (AutoRL): A Survey and Open Problems. J. Artif. Intell. Res. 74 (2022)
Samuel Kessler, Jack Parker-Holder, Philip J. Ball, Stefan Zohren, Stephen J. Roberts
Same State, Different Task: Continual Reinforcement Learning without Interference. AAAI (2022)
Jack Parker-Holder, Minqi Jiang, Michael Dennis, Mikayel Samvelyan, Jakob Foerster, Edward Grefenstette, Tim Rocktäschel
Evolving Curricula with Regret-Based Environment Design. CoRR (2022)
Cong Lu, Philip J. Ball, Jack Parker-Holder, Michael A. Osborne, Stephen J. Roberts
Revisiting Design Choices in Offline Model Based Reinforcement Learning. ICLR (2022)
Ted Moskovitz, Michael Arbel, Jack Parker-Holder, Aldo Pacchiano
Towards an Understanding of Default Policies in Multitask Policy Optimization. AISTATS (2022)
Cong Lu, Philip J. Ball, Tim G. J. Rudner, Jack Parker-Holder, Michael A. Osborne, Yee Whye Teh
Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations. CoRR (2022)
Michael Matthews, Mikayel Samvelyan, Jack Parker-Holder, Edward Grefenstette, Tim Rocktäschel
Hierarchical Kickstarting for Skill Transfer in Reinforcement Learning. CoLLAs (2022)
Minqi Jiang, Michael Dennis, Jack Parker-Holder, Andrei Lupu, Heinrich Küttler, Edward Grefenstette, Tim Rocktäschel, Jakob N. Foerster
Grounding Aleatoric Uncertainty in Unsupervised Environment Design. CoRR (2022)
Samuel Kessler, Jack Parker-Holder, Philip J. Ball, Stefan Zohren, Stephen J. Roberts
Same State, Different Task: Continual Reinforcement Learning without Interference. CoRR (2021)
Xingyou Song, Krzysztof Choromanski, Jack Parker-Holder, Yunhao Tang, Daiyi Peng, Deepali Jain, Wenbo Gao, Aldo Pacchiano, Tamás Sarlós, Yuxiang Yang
ES-ENAS: Combining Evolution Strategies with Neural Architecture Search at No Extra Cost for Reinforcement Learning. CoRR (2021)
Minqi Jiang, Michael Dennis, Jack Parker-Holder, Jakob Foerster, Edward Grefenstette, Tim Rocktäschel
Replay-Guided Adversarial Environment Design. CoRR (2021)
Philip J. Ball, Cong Lu, Jack Parker-Holder, Stephen J. Roberts
Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment. ICML (2021)
Mikayel Samvelyan, Robert Kirk, Vitaly Kurin, Jack Parker-Holder, Minqi Jiang, Eric Hambro, Fabio Petroni, Heinrich Küttler, Edward Grefenstette, Tim Rocktäschel
MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research. NeurIPS Datasets and Benchmarks (2021)
Krzysztof Choromanski, Han Lin, Haoxian Chen, Jack Parker-Holder
Graph Kernel Attention Transformers. CoRR (2021)
Ted Moskovitz, Michael Arbel, Jack Parker-Holder, Aldo Pacchiano
Towards an Understanding of Default Policies in Multitask Policy Optimization. CoRR (2021)
Cong Lu, Philip J. Ball, Jack Parker-Holder, Michael A. Osborne, Stephen J. Roberts
Revisiting Design Choices in Model-Based Offline Reinforcement Learning. CoRR (2021)
Ted Moskovitz, Jack Parker-Holder, Aldo Pacchiano, Michael Arbel, Michael I. Jordan
Tactical Optimism and Pessimism for Deep Reinforcement Learning. NeurIPS (2021)
Mikayel Samvelyan, Robert Kirk, Vitaly Kurin, Jack Parker-Holder, Minqi Jiang, Eric Hambro, Fabio Petroni, Heinrich Küttler, Edward Grefenstette, Tim Rocktäschel
MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research. CoRR (2021)
Krzysztof Choromanski, Deepali Jain, Jack Parker-Holder, Xingyou Song, Valerii Likhosherstov, Anirban Santara, Aldo Pacchiano, Yunhao Tang, Adrian Weller
Unlocking Pixels for Reinforcement Learning via Implicit Attention. CoRR (2021)
Philip J. Ball, Cong Lu, Jack Parker-Holder, Stephen J. Roberts
Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment. CoRR (2021)
Aldo Pacchiano, Philip Ball, Jack Parker-Holder, Krzysztof Choromanski, Stephen Roberts
Towards tractable optimism in model-based reinforcement learning. UAI (2021)
Ted Moskovitz, Jack Parker-Holder, Aldo Pacchiano, Michael Arbel
Deep Reinforcement Learning with Dynamic Optimism. CoRR (2021)
Jack Parker-Holder, Vu Nguyen, Shaan Desai, Stephen J. Roberts
Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL. CoRR (2021)
Jonathan Lorraine, Paul Vicol, Jack Parker-Holder, Tal Kachman, Luke Metz, Jakob Foerster
Lyapunov Exponents for Diversity in Differentiable Games. CoRR (2021)
Minqi Jiang, Michael Dennis, Jack Parker-Holder, Jakob N. Foerster, Edward Grefenstette, Tim Rocktäschel
Replay-Guided Adversarial Environment Design. NeurIPS (2021)
Jack Parker-Holder, Vu Nguyen, Shaan Desai, Stephen J. Roberts
Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL. NeurIPS (2021)
Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang
Practical Nonisotropic Monte Carlo Sampling in High Dimensions via Determinantal Point Processes. AISTATS (2020)
Aldo Pacchiano, Philip J. Ball, Jack Parker-Holder, Krzysztof Choromanski, Stephen Roberts
On Optimism in Model-Based Reinforcement Learning. CoRR (2020)
Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen Roberts
Effective Diversity in Population-Based Reinforcement Learning. CoRR (2020)
Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Krzysztof Choromanski, Anna Choromanska, Michael I. Jordan
Learning to Score Behaviors for Guided Policy Optimization. ICML (2020)
Jack Parker-Holder, Vu Nguyen, Stephen J. Roberts
Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits. NeurIPS (2020)
Jack Parker-Holder, Aldo Pacchiano, Krzysztof Marcin Choromanski, Stephen J. Roberts
Effective Diversity in Population Based Reinforcement Learning. NeurIPS (2020)
Krzysztof Choromanski, David Cheikhi, Jared Davis, Valerii Likhosherstov, Achille Nazaret, Achraf Bahamou, Xingyou Song, Mrugank Akarte, Jack Parker-Holder, Jacob Bergquist, Yuan Gao, Aldo Pacchiano, Tamás Sarlós, Adrian Weller, Vikas Sindhwani
Stochastic Flows and Geometric Optimization on the Orthogonal Group. ICML (2020)
Jack Parker-Holder, Vu Nguyen, Stephen Roberts
One-Shot Bayes Opt with Probabilistic Population Based Training. CoRR (2020)
Jack Parker-Holder, Luke Metz, Cinjon Resnick, Hengyuan Hu, Adam Lerer, Alistair Letcher, Alexander Peysakhovich, Aldo Pacchiano, Jakob N. Foerster
Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian. NeurIPS (2020)
Jack Parker-Holder, Luke Metz, Cinjon Resnick, Hengyuan Hu, Adam Lerer, Alistair Letcher, Alex Peysakhovich, Aldo Pacchiano, Jakob N. Foerster
Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian. CoRR (2020)
Philip J. Ball, Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen J. Roberts
Ready Policy One: World Building Through Active Learning. ICML (2020)
Krzysztof Choromanski, David Cheikhi, Jared Davis, Valerii Likhosherstov, Achille Nazaret, Achraf Bahamou, Xingyou Song, Mrugank Akarte, Jack Parker-Holder, Jacob Bergquist, Yuan Gao, Aldo Pacchiano, Tamás Sarlós, Adrian Weller, Vikas Sindhwani
Stochastic Flows and Geometric Optimization on the Orthogonal Group. CoRR (2020)
Philip J. Ball, Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen Roberts
Ready Policy One: World Building Through Active Learning. CoRR (2020)
Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Jasmine Hsu, Atil Iscen, Deepali Jain, Vikas Sindhwani
When random search is not enough: Sample-Efficient and Noise-Robust Blackbox Optimization of RL Policies. CoRR (2019)
Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang
Adaptive Sample-Efficient Blackbox Optimization via ES-active Subspaces. CoRR (2019)
Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Deepali Jain, Yuxiang Yang, Atil Iscen, Jasmine Hsu, Vikas Sindhwani
Provably Robust Blackbox Optimization for Reinforcement Learning. CoRL (2019)
Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Anna Choromanska, Krzysztof Choromanski, Michael I. Jordan
Wasserstein Reinforcement Learning. CoRR (2019)
Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Vikas Sindhwani
From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization. NeurIPS (2019)
Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang
Structured Monte Carlo Sampling for Nonisotropic Distributions via Determinantal Point Processes. CoRR (2019)
Xingyou Song, Krzysztof Choromanski, Jack Parker-Holder, Yunhao Tang, Wenbo Gao, Aldo Pacchiano, Tamás Sarlós, Deepali Jain, Yuxiang Yang
Reinforcement Learning with Chromatic Networks. CoRR (2019)