| 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 | 15761 | 2015 |
| Deterministic policy gradient algorithms D Silver, G Lever, N Heess, T Degris, D Wierstra, M Riedmiller ICML, 2014 | 4917 | 2014 |
| Recurrent models of visual attention V Mnih, N Heess, A Graves Advances in neural information processing systems, 2204-2212, 2014 | 4609 | 2014 |
| Relational inductive biases, deep learning, and graph networks PW Battaglia, JB Hamrick, V Bapst, A Sanchez-Gonzalez, V Zambaldi, ... arXiv preprint arXiv:1806.01261, 2018 | 3469 | 2018 |
| Emergence of locomotion behaviours in rich environments N Heess, S Sriram, J Lemmon, J Merel, G Wayne, Y Tassa, T Erez, ... arXiv preprint arXiv:1707.02286, 2017 | 1075 | 2017 |
| Feudal networks for hierarchical reinforcement learning AS Vezhnevets, S Osindero, T Schaul, N Heess, M Jaderberg, D Silver, ... Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017 | 1028 | 2017 |
| Sample efficient actor-critic with experience replay Z Wang, V Bapst, N Heess, V Mnih, R Munos, K Kavukcuoglu, ... arXiv preprint arXiv:1611.01224, 2016 | 965 | 2016 |
| Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards M Večerík, T Hester, J Scholz, F Wang, O Pietquin, B Piot, N Heess, ... arXiv preprint arXiv:1707.08817, 2017 | 758 | 2017 |
| A Generalist Agent S Reed, K Zolna, E Parisotto, SG Colmenarejo, A Novikov, G Barth-Maron, ... arXiv preprint arXiv:2205.06175, 2022 | 693 | 2022 |
| Imagination-augmented agents for deep reinforcement learning T Weber, S Racanière, DP Reichert, L Buesing, A Guez, DJ Rezende, ... arXiv preprint arXiv:1707.06203, 2017 | 682* | 2017 |
| Graph networks as learnable physics engines for inference and control A Sanchez-Gonzalez, N Heess, JT Springenberg, J Merel, M Riedmiller, ... arXiv preprint arXiv:1806.01242, 2018 | 680 | 2018 |
| Learning continuous control policies by stochastic value gradients N Heess, G Wayne, D Silver, T Lillicrap, T Erez, Y Tassa Advances in Neural Information Processing Systems, 2944-2952, 2015 | 641 | 2015 |
| Sim-to-real robot learning from pixels with progressive nets AA Rusu, M Vecerik, T Rothörl, N Heess, R Pascanu, R Hadsell arXiv preprint arXiv:1610.04286, 2016 | 620 | 2016 |
| Distributed distributional deterministic policy gradients G Barth-Maron, MW Hoffman, D Budden, W Dabney, D Horgan, A Muldal, ... arXiv preprint arXiv:1804.08617, 2018 | 608 | 2018 |
| Distral: Robust multitask reinforcement learning Y Teh, V Bapst, WM Czarnecki, J Quan, J Kirkpatrick, R Hadsell, N Heess, ... Advances in Neural Information Processing Systems, 4496-4506, 2017 | 594 | 2017 |
| Attend, infer, repeat: Fast scene understanding with generative models SMA Eslami, N Heess, T Weber, Y Tassa, D Szepesvari, GE Hinton Advances in Neural Information Processing Systems, 3225-3233, 2016 | 573 | 2016 |
| 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 | 535 | 1935 |
| Maximum a posteriori policy optimisation A Abdolmaleki, JT Springenberg, Y Tassa, R Munos, N Heess, ... arXiv preprint arXiv:1806.06920, 2018 | 490 | 2018 |
| Learning by playing-solving sparse reward tasks from scratch M Riedmiller, R Hafner, T Lampe, M Neunert, J Degrave, T Van de Wiele, ... arXiv preprint arXiv:1802.10567, 2018 | 461 | 2018 |
| Imagination-augmented agents for deep reinforcement learning S Racanière, T Weber, D Reichert, L Buesing, A Guez, DJ Rezende, ... Advances in neural information processing systems, 5690-5701, 2017 | 448 | 2017 |
