| Trust region policy optimization J Schulman, S Levine, P Moritz, MI Jordan, P Abbeel International Conference on Machine Learning (ICML 2015), 2015 | 2550 | 2015 |
| Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks C Finn, P Abbeel, S Levine International Conference on Machine Learning (ICML), 2017 | 2056 | 2017 |
| 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 | 1909 | 2016 |
| 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 | 1154 | 2018 |
| 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 | 939 | 2015 |
| Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor T Haarnoja, A Zhou, P Abbeel, S Levine arXiv preprint arXiv:1801.01290, 2018 | 781 | 2018 |
| Guided Policy Search S Levine, V Koltun International Conference on Machine Learning (ICML 2013), 2013 | 695 | 2013 |
| 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 | 644 | 2017 |
| Unsupervised learning for physical interaction through video prediction C Finn, I Goodfellow, S Levine Advances in neural information processing systems, 64-72, 2016 | 595 | 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 | 580 | 2016 |
| Guided cost learning: Deep inverse optimal control via policy optimization C Finn, S Levine, P Abbeel International conference on machine learning, 49-58, 2016 | 408 | 2016 |
| Reinforcement learning with deep energy-based policies T Haarnoja, H Tang, P Abbeel, S Levine arXiv preprint arXiv:1702.08165, 2017 | 399 | 2017 |
| 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 | 380 | 2015 |
| Value iteration networks A Tamar, Y Wu, G Thomas, S Levine, P Abbeel Advances in Neural Information Processing Systems, 2154-2162, 2016 | 360 | 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 | 350 | 2018 |
| Learning neural network policies with guided policy search under unknown dynamics S Levine, P Abbeel Advances in Neural Information Processing Systems, 1071-1079, 2014 | 350 | 2014 |
| Deep visual foresight for planning robot motion C Finn, S Levine 2017 IEEE International Conference on Robotics and Automation (ICRA), 2786-2793, 2017 | 345 | 2017 |
| CAD2RL: Real Single-Image Flight without a Single Real Image F Sadeghi, S Levine arXiv preprint arXiv:1611.04201, 2016 | 333 | 2016 |
| Deep Spatial Autoencoders for Visuomotor Learning C Finn, XY Tan, Y Duan, T Darrell, S Levine, P Abbeel International Conference on Robotics and Automation (ICRA 2016), 2015 | 328* | 2015 |
| Cognitive mapping and planning for visual navigation S Gupta, J Davidson, S Levine, R Sukthankar, J Malik Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 315 | 2017 |
Pieter AbbeelUC Berkeley | Covariant.AIVerified email at cs.berkeley.edu
