| Asynchronous methods for deep reinforcement learning V Mnih, A Puigdomenech Badia, M Mirza, A Graves, T Lillicrap, T Harley, ... International conference on machine learning, 1928-1937, 2016 | 11700 | 2016 |
| Hybrid computing using a neural network with dynamic external memory A Graves, G Wayne, M Reynolds, T Harley, I Danihelka, ... Nature 538 (7626), 471-476, 2016 | 1944 | 2016 |
| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 1579 | 2023 |
| Imagination-augmented agents for deep reinforcement learning S Racanière, T Weber, D Reichert, L Buesing, A Guez, ... Advances in neural information processing systems 30, 2017 | 721* | 2017 |
| Agent57: Outperforming the atari human benchmark AP Badia, B Piot, S Kapturowski, P Sprechmann, A Vitvitskyi, ZD Guo, ... International conference on machine learning, 507-517, 2020 | 680 | 2020 |
| Neural episodic control A Pritzel, B Uria, S Srinivasan, AP Badia, O Vinyals, D Hassabis, ... International conference on machine learning, 2827-2836, 2017 | 423 | 2017 |
| 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 | 357 | 2020 |
| Memory-based parameter adaptation P Sprechmann, SM Jayakumar, JW Rae, A Pritzel, AP Badia, B Uria, ... arXiv preprint arXiv:1802.10542, 2018 | 114 | 2018 |
| The CLRS algorithmic reasoning benchmark P Veličković, AP Badia, D Budden, R Pascanu, A Banino, M Dashevskiy, ... International Conference on Machine Learning, 22084-22102, 2022 | 84 | 2022 |
| Generalization of reinforcement learners with working and episodic memory M Fortunato, M Tan, R Faulkner, S Hansen, A Puigdomènech Badia, ... Advances in neural information processing systems 32, 2019 | 74 | 2019 |
| Asynchronous methods for deep reinforcement learning. arXiv 2016 V Mnih, AP Badia, M Mirza, A Graves, TP Lillicrap, T Harley, D Silver, ... arXiv preprint arXiv:1602.01783, 1783 | 61 | 1783 |
| Retrieval-augmented reinforcement learning A Goyal, A Friesen, A Banino, T Weber, NR Ke, AP Badia, A Guez, ... International Conference on Machine Learning, 7740-7765, 2022 | 48 | 2022 |
| Memo: A deep network for flexible combination of episodic memories A Banino, AP Badia, R Köster, MJ Chadwick, V Zambaldi, D Hassabis, ... arXiv preprint arXiv:2001.10913, 2020 | 37 | 2020 |
| Beyond fine-tuning: Transferring behavior in reinforcement learning V Campos, P Sprechmann, S Hansen, A Barreto, S Kapturowski, ... arXiv preprint arXiv:2102.13515, 2021 | 30 | 2021 |
| Human-level Atari 200x faster S Kapturowski, V Campos, R Jiang, N Rakićević, H van Hasselt, ... arXiv preprint arXiv:2209.07550, 2022 | 28 | 2022 |
| Asynchronous deep reinforcement learning V Mnih, AP Badia, AB Graves, TJA Harley, D Silver, K Kavukcuoglu US Patent 10,936,946, 2021 | 23 | 2021 |
| Coverage as a principle for discovering transferable behavior in reinforcement learning V Campos, P Sprechmann, SS Hansen, A Barreto, C Blundell, A Vitvitskyi, ... | 9 | 2021 |
| Neural episodic control B Uria-Martínez, A Pritzel, C Blundell, AP Badia US Patent 10,664,753, 2020 | 5 | 2020 |
| Agent57: Outperforming the Atari Human Benchmark. arXiv e-prints, page AP Badia, B Piot, S Kapturowski, P Sprechmann, A Vitvitskyi, D Guo, ... arXiv preprint arXiv:2003.13350, 2020 | 5 | 2020 |
| Machine learning systems with memory based parameter adaptation for learning fast and slower P Sprechmann, S Jayakumar, JW Rae, A Pritzel, AP Badia, O Vinyals, ... US Patent App. 16/759,561, 2020 | 4 | 2020 |
