| Categorical reparameterization with gumbel-softmax E Jang, S Gu, B Poole arXiv preprint arXiv:1611.01144, 2016 | 4915 | 2016 |
| Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates S Gu, E Holly, T Lillicrap, S Levine 2017 IEEE international conference on robotics and automation (ICRA), 3389-3396, 2017 | 1709 | 2017 |
| Continuous deep q-learning with model-based acceleration S Gu, T Lillicrap, I Sutskever, S Levine International conference on machine learning, 2829-2838, 2016 | 1153 | 2016 |
| Continuous deep q-learning with model-based acceleration S Gu, T Lillicrap, I Sutskever, S Levine International conference on machine learning, 2829-2838, 2016 | 1153 | 2016 |
| Large language models are zero-shot reasoners T Kojima, SS Gu, M Reid, Y Matsuo, Y Iwasawa Advances in neural information processing systems 35, 22199-22213, 2022 | 989 | 2022 |
| Towards deep neural network architectures robust to adversarial examples S Gu, L Rigazio arXiv preprint arXiv:1412.5068, 2014 | 943 | 2014 |
| Data-efficient hierarchical reinforcement learning O Nachum, SS Gu, H Lee, S Levine Advances in neural information processing systems 31, 2018 | 824 | 2018 |
| Scaling instruction-finetuned language models HW Chung, L Hou, S Longpre, B Zoph, Y Tay, W Fedus, Y Li, X Wang, ... arXiv preprint arXiv:2210.11416, 2022 | 803 | 2022 |
| Beyond the imitation game: Quantifying and extrapolating the capabilities of language models A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ... arXiv preprint arXiv:2206.04615, 2022 | 450 | 2022 |
| A minimalist approach to offline reinforcement learning S Fujimoto, SS Gu Advances in neural information processing systems 34, 20132-20145, 2021 | 396 | 2021 |
| Q-prop: Sample-efficient policy gradient with an off-policy critic S Gu, T Lillicrap, Z Ghahramani, RE Turner, S Levine arXiv preprint arXiv:1611.02247, 2016 | 374 | 2016 |
| Dynamics-aware unsupervised discovery of skills A Sharma, S Gu, S Levine, V Kumar, K Hausman arXiv preprint arXiv:1907.01657, 2019 | 352 | 2019 |
| Human-centric dialog training via offline reinforcement learning N Jaques, JH Shen, A Ghandeharioun, C Ferguson, A Lapedriza, ... arXiv preprint arXiv:2010.05848, 2020 | 298* | 2020 |
| Temporal difference models: Model-free deep rl for model-based control V Pong, S Gu, M Dalal, S Levine arXiv preprint arXiv:1802.09081, 2018 | 262 | 2018 |
| Sequence tutor: Conservative fine-tuning of sequence generation models with kl-control N Jaques, S Gu, D Bahdanau, JM Hernández-Lobato, RE Turner, D Eck International Conference on Machine Learning, 1645-1654, 2017 | 232* | 2017 |
| A divergence minimization perspective on imitation learning methods SKS Ghasemipour, R Zemel, S Gu Conference on Robot Learning, 1259-1277, 2020 | 231 | 2020 |
| Near-optimal representation learning for hierarchical reinforcement learning O Nachum, S Gu, H Lee, S Levine arXiv preprint arXiv:1810.01257, 2018 | 209 | 2018 |
| Language as an abstraction for hierarchical deep reinforcement learning Y Jiang, SS Gu, KP Murphy, C Finn Advances in Neural Information Processing Systems 32, 2019 | 186 | 2019 |
| Interpolated policy gradient: Merging on-policy and off-policy gradient estimation for deep reinforcement learning SS Gu, T Lillicrap, RE Turner, Z Ghahramani, B Schölkopf, S Levine Advances in neural information processing systems 30, 2017 | 177 | 2017 |
| Muprop: Unbiased backpropagation for stochastic neural networks S Gu, S Levine, I Sutskever, A Mnih arXiv preprint arXiv:1511.05176, 2015 | 157 | 2015 |
Sergey LevineUC Berkeley, GoogleVerified email at eecs.berkeley.edu
