| Model-agnostic meta-learning for fast adaptation of deep networks C Finn, P Abbeel, S Levine International conference on machine learning, 1126-1135, 2017 | 6711 | 2017 |
| Trust region policy optimization J Schulman, S Levine, P Abbeel, M Jordan, P Moritz International conference on machine learning, 1889-1897, 2015 | 5169 | 2015 |
| 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 | 3811 | 2016 |
| 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 | 3585 | 2018 |
| Apprenticeship learning via inverse reinforcement learning P Abbeel, AY Ng Proceedings of the twenty-first international conference on Machine learning, 1, 2004 | 3288 | 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 | 2988 | 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 | 2346 | 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 | 2064 | 2015 |
| Introduction to statistical relational learning D Koller, N Friedman, S Džeroski, C Sutton, A McCallum, A Pfeffer, ... MIT press, 2007 | 1895 | 2007 |
| 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 | 1886 | 2017 |
| Hindsight experience replay M Andrychowicz, F Wolski, A Ray, J Schneider, R Fong, P Welinder, ... Advances in neural information processing systems 30, 2017 | 1618 | 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 | 1514 | 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 | 1026 | 2018 |
| A simple neural attentive meta-learner N Mishra, M Rohaninejad, X Chen, P Abbeel arXiv preprint arXiv:1707.03141, 2017 | 1009 | 2017 |
| Discriminative probabilistic models for relational data B Taskar, P Abbeel, D Koller arXiv preprint arXiv:1301.0604, 2012 | 918 | 2012 |
| 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 | 888 | 2018 |
| 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 | 874 | 2015 |
| Reinforcement learning with deep energy-based policies T Haarnoja, H Tang, P Abbeel, S Levine International conference on machine learning, 1352-1361, 2017 | 860 | 2017 |
| An application of reinforcement learning to aerobatic helicopter flight P Abbeel, A Coates, M Quigley, A Ng Advances in neural information processing systems 19, 2006 | 806 | 2006 |
| Guided cost learning: Deep inverse optimal control via policy optimization C Finn, S Levine, P Abbeel International conference on machine learning, 49-58, 2016 | 784 | 2016 |
Sergey LevineUC Berkeley, GoogleVerified email at eecs.berkeley.edu
