| Continuous control with deep reinforcement learning TP Lillicrap, JJ Hunt, A Pritzel, N Heess, T Erez, Y Tassa, D Silver, ... arXiv preprint arXiv:1509.02971, 2015 | 5002 | 2015 |
| Simple and scalable predictive uncertainty estimation using deep ensembles B Lakshminarayanan, A Pritzel, C Blundell arXiv preprint arXiv:1612.01474, 2016 | 1192 | 2016 |
| Deep exploration via bootstrapped DQN I Osband, C Blundell, A Pritzel, B Van Roy arXiv preprint arXiv:1602.04621, 2016 | 631 | 2016 |
| Pathnet: Evolution channels gradient descent in super neural networks C Fernando, D Banarse, C Blundell, Y Zwols, D Ha, AA Rusu, A Pritzel, ... arXiv preprint arXiv:1701.08734, 2017 | 363 | 2017 |
| Vector-based navigation using grid-like representations in artificial agents A Banino, C Barry, B Uria, C Blundell, T Lillicrap, P Mirowski, A Pritzel, ... Nature 557 (7705), 429-433, 2018 | 299 | 2018 |
| Darla: Improving zero-shot transfer in reinforcement learning I Higgins, A Pal, A Rusu, L Matthey, C Burgess, A Pritzel, M Botvinick, ... International Conference on Machine Learning, 1480-1490, 2017 | 203 | 2017 |
| Neural episodic control A Pritzel, B Uria, S Srinivasan, AP Badia, O Vinyals, D Hassabis, ... International Conference on Machine Learning, 2827-2836, 2017 | 161 | 2017 |
| Model-free episodic control C Blundell, B Uria, A Pritzel, Y Li, A Ruderman, JZ Leibo, J Rae, ... arXiv preprint arXiv:1606.04460, 2016 | 158* | 2016 |
| Scrambling in the black hole portrait G Dvali, D Flassig, C Gomez, A Pritzel, N Wintergerst Physical Review D 88 (12), 124041, 2013 | 80 | 2013 |
| Black holes and quantumness on macroscopic scales D Flassig, A Pritzel, N Wintergerst Physical Review D 87 (8), 084007, 2013 | 62 | 2013 |
| Continuous control with deep reinforcement learning. arXiv 2015 TP Lillicrap, JJ Hunt, A Pritzel, N Heess, T Erez, Y Tassa, D Silver, ... arXiv preprint arXiv:1509.02971, 1935 | 58 | 1935 |
| Memory-based parameter adaptation P Sprechmann, SM Jayakumar, JW Rae, A Pritzel, AP Badia, B Uria, ... arXiv preprint arXiv:1802.10542, 2018 | 50 | 2018 |
| On ghosts in theories of self-interacting massive spin-2 particles S Folkerts, A Pritzel, N Wintergerst arXiv preprint arXiv:1107.3157, 2011 | 46 | 2011 |
| Meta-learning by the baldwin effect C Fernando, J Sygnowski, S Osindero, J Wang, T Schaul, D Teplyashin, ... Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2018 | 28 | 2018 |
| Topological model for domain walls in (super-) Yang-Mills theories M Dierigl, A Pritzel Physical Review D 90 (10), 105008, 2014 | 27 | 2014 |
| Never give up: Learning directed exploration strategies AP Badia, P Sprechmann, A Vitvitskyi, D Guo, B Piot, S Kapturowski, ... arXiv preprint arXiv:2002.06038, 2020 | 24* | 2020 |
| Fast deep reinforcement learning using online adjustments from the past S Hansen, P Sprechmann, A Pritzel, A Barreto, C Blundell arXiv preprint arXiv:1810.08163, 2018 | 17 | 2018 |
| Large-N ground state of the Lieb-Liniger model and Yang-Mills theory on a two-sphere D Flassig, A Franca, A Pritzel Physical Review A 93 (1), 013627, 2016 | 16 | 2016 |
| Generative temporal models with spatial memory for partially observed environments M Fraccaro, D Rezende, Y Zwols, A Pritzel, SMA Eslami, F Viola International Conference on Machine Learning, 1549-1558, 2018 | 15 | 2018 |
| Continuous control with deep reinforcement learning Y Bengio, TP Lillicrap, JJ Hunt, A Pritzel, N Heess, T Erez, D Wierstra Found. Trends® Mach. Learn 2, 1-127, 2009 | 11 | 2009 |
Timothy P. LillicrapSenior Research Scientist, Google DeepMindVerified email at google.com
