Effective diversity in population based reinforcement learning J Parker-Holder, A Pacchiano, KM Choromanski, SJ Roberts Advances in Neural Information Processing Systems 33, 18050-18062, 2020 | 153 | 2020 |
Evolving curricula with regret-based environment design J Parker-Holder, M Jiang, M Dennis, M Samvelyan, J Foerster, ... International Conference on Machine Learning, 17473-17498, 2022 | 93 | 2022 |
Replay-guided adversarial environment design M Jiang, M Dennis, J Parker-Holder, J Foerster, E Grefenstette, ... Advances in Neural Information Processing Systems 34, 1884-1897, 2021 | 82 | 2021 |
Human-timescale adaptation in an open-ended task space AA Team, J Bauer, K Baumli, S Baveja, F Behbahani, A Bhoopchand, ... ICML 2023 (Oral), 2023 | 78* | 2023 |
Automated reinforcement learning (autorl): A survey and open problems J Parker-Holder, R Rajan, X Song, A Biedenkapp, Y Miao, T Eimer, ... Journal of Artificial Intelligence Research 74, 517-568, 2022 | 78 | 2022 |
Minihack the planet: A sandbox for open-ended reinforcement learning research M Samvelyan, R Kirk, V Kurin, J Parker-Holder, M Jiang, E Hambro, ... arXiv preprint arXiv:2109.13202, 2021 | 73 | 2021 |
Provably efficient online hyperparameter optimization with population-based bandits J Parker-Holder, V Nguyen, SJ Roberts Advances in neural information processing systems 33, 17200-17211, 2020 | 68 | 2020 |
Revisiting Design Choices in Offline Model-Based Reinforcement Learning C Lu, PJ Ball, J Parker-Holder, MA Osborne, SJ Roberts arXiv preprint arXiv:2110.04135, 2021 | 57 | 2021 |
Ready policy one: World building through active learning P Ball, J Parker-Holder, A Pacchiano, K Choromanski, S Roberts International Conference on Machine Learning, 591-601, 2020 | 53 | 2020 |
From complexity to simplicity: Adaptive es-active subspaces for blackbox optimization KM Choromanski, A Pacchiano, J Parker-Holder, Y Tang, V Sindhwani Advances in Neural Information Processing Systems 32, 2019 | 50 | 2019 |
Tactical optimism and pessimism for deep reinforcement learning T Moskovitz, J Parker-Holder, A Pacchiano, M Arbel, M Jordan Advances in Neural Information Processing Systems 34, 12849-12863, 2021 | 49 | 2021 |
Same state, different task: Continual reinforcement learning without interference S Kessler, J Parker-Holder, P Ball, S Zohren, SJ Roberts Proceedings of the AAAI Conference on Artificial Intelligence 36 (7), 7143-7151, 2022 | 44* | 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, 2020 | 42 | 2020 |
Learning to score behaviors for guided policy optimization A Pacchiano, J Parker-Holder, Y Tang, K Choromanski, A Choromanska, ... International Conference on Machine Learning, 7445-7454, 2020 | 40 | 2020 |
Towards tractable optimism in model-based reinforcement learning A Pacchiano, P Ball, J Parker-Holder, K Choromanski, S Roberts Uncertainty in Artificial Intelligence, 1413-1423, 2021 | 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 arXiv preprint arXiv:2206.04779, 2022 | 36 | 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, 619-629, 2021 | 34 | 2021 |
Synthetic Experience Replay C Lu, PJ Ball, YW Teh, J Parker-Holder NeurIPS 2023, 2023 | 30 | 2023 |
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, 753-765, 2020 | 26 | 2020 |
From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers K Choromanski, H Lin, H Chen, T Zhang, A Sehanobish, V Likhosherstov, ... International Conference on Machine Learning, 3962-3983, 2022 | 20 | 2022 |