| Model-agnostic meta-learning for fast adaptation of deep networks C Finn, P Abbeel, S Levine International conference on machine learning, 1126-1135, 2017 | 9186 | 2017 |
| Trust region policy optimization J Schulman, S Levine, P Abbeel, M Jordan, P Moritz International conference on machine learning, 1889-1897, 2015 | 6400 | 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 | 5323 | 2018 |
| Infogan: Interpretable representation learning by information maximizing generative adversarial nets X Chen, Y Duan, R Houthooft, J Schulman, I Sutskever, P Abbeel Advances in neural information processing systems 29, 2016 | 4491 | 2016 |
| Apprenticeship learning via inverse reinforcement learning P Abbeel, AY Ng Proceedings of the twenty-first international conference on Machine learning, 1, 2004 | 3668 | 2004 |
| 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 | 3391 | 2016 |
| Multi-agent actor-critic for mixed cooperative-competitive environments R Lowe, YI Wu, A Tamar, J Harb, OAI Pieter Abbeel, I Mordatch Advances in neural information processing systems 30, 2017 | 3322 | 2017 |
| High-dimensional continuous control using generalized advantage estimation J Schulman, P Moritz, S Levine, M Jordan, P Abbeel arXiv preprint arXiv:1506.02438, 2015 | 2639 | 2015 |
| Domain randomization for transferring deep neural networks from simulation to the real world J Tobin, R Fong, A Ray, J Schneider, W Zaremba, P Abbeel 2017 IEEE/RSJ international conference on intelligent robots and systems …, 2017 | 2438 | 2017 |
| Denoising diffusion probabilistic models J Ho, A Jain, P Abbeel Advances in Neural Information Processing Systems 33, 6840-6851, 2020 | 2253 | 2020 |
| Hindsight experience replay M Andrychowicz, F Wolski, A Ray, J Schneider, R Fong, P Welinder, ... Advances in neural information processing systems 30, 2017 | 2096 | 2017 |
| Benchmarking deep reinforcement learning for continuous control Y Duan, X Chen, R Houthooft, J Schulman, P Abbeel International conference on machine learning, 1329-1338, 2016 | 1735 | 2016 |
| 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 | 1612 | 2018 |
| A simple neural attentive meta-learner N Mishra, M Rohaninejad, X Chen, P Abbeel arXiv preprint arXiv:1707.03141, 2017 | 1264 | 2017 |
| Sim-to-real transfer of robotic control with dynamics randomization XB Peng, M Andrychowicz, W Zaremba, P Abbeel 2018 IEEE international conference on robotics and automation (ICRA), 3803-3810, 2018 | 1112 | 2018 |
| Reinforcement learning with deep energy-based policies T Haarnoja, H Tang, P Abbeel, S Levine International conference on machine learning, 1352-1361, 2017 | 1086 | 2017 |
| Constrained policy optimization J Achiam, D Held, A Tamar, P Abbeel International conference on machine learning, 22-31, 2017 | 999 | 2017 |
| A survey of research on cloud robotics and automation B Kehoe, S Patil, P Abbeel, K Goldberg IEEE Transactions on automation science and engineering 12 (2), 398-409, 2015 | 941 | 2015 |
| Discriminative probabilistic models for relational data B Taskar, P Abbeel, D Koller arXiv preprint arXiv:1301.0604, 2012 | 929 | 2012 |
| Guided cost learning: Deep inverse optimal control via policy optimization C Finn, S Levine, P Abbeel International conference on machine learning, 49-58, 2016 | 911 | 2016 |
Sergey LevineUC Berkeley, GoogleVerified email at eecs.berkeley.edu
