| Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks C Finn, P Abbeel, S Levine International Conference on Machine Learning (ICML), 2017 | 10235 | 2017 |
| Trust region policy optimization J Schulman, S Levine, P Moritz, MI Jordan, P Abbeel International Conference on Machine Learning (ICML 2015), 2015 | 6922 | 2015 |
| Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor T Haarnoja, A Zhou, P Abbeel, S Levine International conference on machine learning, 1861-1870, 2018 | 6145 | 2018 |
| End-to-end training of deep visuomotor policies S Levine, C Finn, T Darrell, P Abbeel The Journal of Machine Learning Research 17 (1), 1334-1373, 2016 | 3541 | 2016 |
| High-dimensional continuous control using generalized advantage estimation J Schulman, P Moritz, S Levine, M Jordan, P Abbeel International Conference on Learning Representations (ICLR 2016), 2015 | 2906 | 2015 |
| Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection S Levine, P Pastor, A Krizhevsky, J Ibarz, D Quillen The International Journal of Robotics Research 37 (4-5), 421-436, 2018 | 2373 | 2018 |
| Soft actor-critic algorithms and applications T Haarnoja, A Zhou, K Hartikainen, G Tucker, S Ha, J Tan, V Kumar, ... arXiv preprint arXiv:1812.05905, 2018 | 1845 | 2018 |
| 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 | 1653 | 2017 |
| Guided Policy Search S Levine, V Koltun International Conference on Machine Learning (ICML 2013), 2013 | 1229 | 2013 |
| Reinforcement learning with deep energy-based policies T Haarnoja, H Tang, P Abbeel, S Levine International conference on machine learning, 1352-1361, 2017 | 1178 | 2017 |
| Offline reinforcement learning: Tutorial, review, and perspectives on open problems S Levine, A Kumar, G Tucker, J Fu arXiv preprint arXiv:2005.01643, 2020 | 1173 | 2020 |
| Continuous deep q-learning with model-based acceleration S Gu, T Lillicrap, I Sutskever, S Levine International conference on machine learning, 2829-2838, 2016 | 1135 | 2016 |
| Deep reinforcement learning in a handful of trials using probabilistic dynamics models K Chua, R Calandra, R McAllister, S Levine Advances in neural information processing systems 31, 2018 | 1108 | 2018 |
| Unsupervised learning for physical interaction through video prediction C Finn, I Goodfellow, S Levine Advances in neural information processing systems 29, 2016 | 1097 | 2016 |
| Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning A Nagabandi, G Kahn, RS Fearing, S Levine 2018 IEEE international conference on robotics and automation (ICRA), 7559-7566, 2018 | 1020 | 2018 |
| Conservative q-learning for offline reinforcement learning A Kumar, A Zhou, G Tucker, S Levine Advances in Neural Information Processing Systems 33, 1179-1191, 2020 | 998 | 2020 |
| Guided cost learning: Deep inverse optimal control via policy optimization C Finn, S Levine, P Abbeel International conference on machine learning, 49-58, 2016 | 968 | 2016 |
| Recurrent network models for human dynamics K Fragkiadaki, S Levine, P Felsen, J Malik Proceedings of the IEEE international conference on computer vision, 4346-4354, 2015 | 959 | 2015 |
| Diversity is all you need: Learning skills without a reward function B Eysenbach, A Gupta, J Ibarz, S Levine arXiv preprint arXiv:1802.06070, 2018 | 876 | 2018 |
| Deepmimic: Example-guided deep reinforcement learning of physics-based character skills XB Peng, P Abbeel, S Levine, M Van de Panne ACM Transactions On Graphics (TOG) 37 (4), 1-14, 2018 | 874 | 2018 |
Chelsea FinnStanford University, GoogleBestätigte E-Mail-Adresse bei cs.stanford.edu
