| Deep reinforcement learning in large discrete action spaces G Dulac-Arnold, R Evans, H van Hasselt, P Sunehag, T Lillicrap, J Hunt, ... arXiv preprint arXiv:1512.07679, 2015 | 768 | 2015 |
| Neural scene representation and rendering SMA Eslami, D Jimenez Rezende, F Besse, F Viola, AS Morcos, ... Science 360 (6394), 1204-1210, 2018 | 736 | 2018 |
| Imagination-augmented agents for deep reinforcement learning T Weber, S Racaniere, DP Reichert, L Buesing, A Guez, DJ Rezende, ... arXiv preprint arXiv:1707.06203, 2017 | 731* | 2017 |
| Attend, infer, repeat: Fast scene understanding with generative models SM Eslami, N Heess, T Weber, Y Tassa, D Szepesvari, GE Hinton Advances in neural information processing systems 29, 2016 | 606 | 2016 |
| Gradient estimation using stochastic computation graphs J Schulman, N Heess, T Weber, P Abbeel Advances in neural information processing systems 28, 2015 | 464 | 2015 |
| Visual interaction networks: Learning a physics simulator from video N Watters, D Zoran, T Weber, P Battaglia, R Pascanu, A Tacchetti Advances in neural information processing systems 30, 2017 | 419 | 2017 |
| Relational recurrent neural networks A Santoro, R Faulkner, D Raposo, J Rae, M Chrzanowski, T Weber, ... Advances in neural information processing systems 31, 2018 | 274 | 2018 |
| Woulda, coulda, shoulda: Counterfactually-guided policy search L Buesing, T Weber, Y Zwols, S Racaniere, A Guez, JB Lespiau, N Heess arXiv preprint arXiv:1811.06272, 2018 | 162 | 2018 |
| Automated variational inference in probabilistic programming D Wingate, T Weber arXiv preprint arXiv:1301.1299, 2013 | 162 | 2013 |
| Temporal difference variational auto-encoder K Gregor, G Papamakarios, F Besse, L Buesing, T Weber arXiv preprint arXiv:1806.03107, 2018 | 153 | 2018 |
| Learning model-based planning from scratch R Pascanu, Y Li, O Vinyals, N Heess, L Buesing, S Racanière, D Reichert, ... arXiv preprint arXiv:1707.06170, 2017 | 124 | 2017 |
| Learning and querying fast generative models for reinforcement learning L Buesing, T Weber, S Racaniere, SM Eslami, D Rezende, DP Reichert, ... arXiv preprint arXiv:1802.03006, 2018 | 112 | 2018 |
| An investigation of model-free planning A Guez, M Mirza, K Gregor, R Kabra, S Racanière, T Weber, D Raposo, ... International Conference on Machine Learning, 2464-2473, 2019 | 99 | 2019 |
| Learning to search with mctsnets A Guez, T Weber, I Antonoglou, K Simonyan, O Vinyals, D Wierstra, ... International conference on machine learning, 1822-1831, 2018 | 97 | 2018 |
| On the role of planning in model-based deep reinforcement learning JB Hamrick, AL Friesen, F Behbahani, A Guez, F Viola, S Witherspoon, ... arXiv preprint arXiv:2011.04021, 2020 | 86 | 2020 |
| Muesli: Combining improvements in policy optimization M Hessel, I Danihelka, F Viola, A Guez, S Schmitt, L Sifre, T Weber, ... International conference on machine learning, 4214-4226, 2021 | 83 | 2021 |
| System linearization T Weber, B Vigoda, P Pratt, J Park, M McCormick US Patent App. 13/678,904, 2013 | 83 | 2013 |
| Counterfactual credit assignment in model-free reinforcement learning T Mesnard, T Weber, F Viola, S Thakoor, A Saade, A Harutyunyan, ... arXiv preprint arXiv:2011.09464, 2020 | 70 | 2020 |
| Combining q-learning and search with amortized value estimates JB Hamrick, V Bapst, A Sanchez-Gonzalez, T Pfaff, T Weber, L Buesing, ... arXiv preprint arXiv:1912.02807, 2019 | 61 | 2019 |
| Credit assignment techniques in stochastic computation graphs T Weber, N Heess, L Buesing, D Silver The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 54 | 2019 |
Lars BuesingGoogle DeepMind在 google.com 的电子邮件经过验证