| Keep doing what worked: Behavioral modelling priors for offline reinforcement learning NY Siegel, JT Springenberg, F Berkenkamp, A Abdolmaleki, M Neunert, ... arXiv preprint arXiv:2002.08396, 2020 | 241 | 2020 |
| Critic regularized regression Z Wang, A Novikov, K Zolna, JS Merel, JT Springenberg, SE Reed, ... Advances in Neural Information Processing Systems 33, 7768-7778, 2020 | 214 | 2020 |
| Figureseer: Parsing result-figures in research papers N Siegel, Z Horvitz, R Levin, S Divvala, A Farhadi Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016 | 167 | 2016 |
| Extracting scientific figures with distantly supervised neural networks N Siegel, N Lourie, R Power, W Ammar Proceedings of the 18th ACM/IEEE on joint conference on digital libraries …, 2018 | 126 | 2018 |
| From motor control to team play in simulated humanoid football S Liu, G Lever, Z Wang, J Merel, SM Eslami, D Hennes, WM Czarnecki, ... arXiv preprint arXiv:2105.12196, 2021 | 48 | 2021 |
| Imagined value gradients: Model-based policy optimization with tranferable latent dynamics models A Byravan, JT Springenberg, A Abdolmaleki, R Hafner, M Neunert, ... Conference on Robot Learning, 566-589, 2020 | 40 | 2020 |
| Data-efficient hindsight off-policy option learning M Wulfmeier, D Rao, R Hafner, T Lampe, A Abdolmaleki, T Hertweck, ... International Conference on Machine Learning, 11340-11350, 2021 | 33 | 2021 |
| From motor control to team play in simulated humanoid football S Liu, G Lever, Z Wang, J Merel, SMA Eslami, D Hennes, WM Czarnecki, ... Science Robotics 7 (69), eabo0235, 2022 | 31 | 2022 |
| Compositional transfer in hierarchical reinforcement learning M Wulfmeier, A Abdolmaleki, R Hafner, JT Springenberg, M Neunert, ... arXiv preprint arXiv:1906.11228, 2019 | 27 | 2019 |
| Regularized hierarchical policies for compositional transfer in robotics M Wulfmeier, A Abdolmaleki, R Hafner, JT Springenberg, M Neunert, ... arXiv preprint arXiv:1906.11228, 2019 | 26 | 2019 |
| Solving math word problems with process-and outcome-based feedback J Uesato, N Kushman, R Kumar, F Song, N Siegel, L Wang, A Creswell, ... arXiv preprint arXiv:2211.14275, 2022 | 22 | 2022 |
| Towards real robot learning in the wild: A case study in bipedal locomotion M Bloesch, J Humplik, V Patraucean, R Hafner, T Haarnoja, A Byravan, ... Conference on Robot Learning, 1502-1511, 2022 | 18 | 2022 |
| Imitate and repurpose: Learning reusable robot movement skills from human and animal behaviors S Bohez, S Tunyasuvunakool, P Brakel, F Sadeghi, L Hasenclever, ... arXiv preprint arXiv:2203.17138, 2022 | 12 | 2022 |
| Learning agile soccer skills for a bipedal robot with deep reinforcement learning T Haarnoja, B Moran, G Lever, SH Huang, D Tirumala, M Wulfmeier, ... arXiv preprint arXiv:2304.13653, 2023 | 6 | 2023 |
| Simple sensor intentions for exploration T Hertweck, M Riedmiller, M Bloesch, JT Springenberg, N Siegel, ... arXiv preprint arXiv:2005.07541, 2020 | 5 | 2020 |
| Solving math word problems with process-based and outcome-based feedback J Uesato, N Kushman, R Kumar, HF Song, NY Siegel, L Wang, A Creswell, ... | 2 | 2022 |
| Understanding charts in research papers: A learning approach N Siegel Technical report, 2015 | 2 | 2015 |
| " What, not how": Solving an under-actuated insertion task from scratch G Vezzani, M Neunert, M Wulfmeier, R Jeong, T Lampe, N Siegel, ... arXiv preprint arXiv:2010.15492, 2020 | | 2020 |
Nicolas HeessDeepMindVerified email at google.com
