| 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, 2172-2180, 2016 | 3154 | 2016 |
| 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 | 1280 | 2016 |
| Vime: Variational information maximizing exploration R Houthooft, X Chen, Y Duan, J Schulman, F De Turck, P Abbeel arXiv preprint arXiv:1605.09674, 2016 | 591* | 2016 |
| RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning Y Duan, J Schulman, X Chen, PL Bartlett, I Sutskever, P Abbeel arXiv preprint arXiv:1611.02779, 2016 | 573 | 2016 |
| Variational lossy autoencoder X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ... arXiv preprint arXiv:1611.02731, 2016 | 501 | 2016 |
| One-shot imitation learning Y Duan, M Andrychowicz, BC Stadie, J Ho, J Schneider, I Sutskever, ... arXiv preprint arXiv:1703.07326, 2017 | 493 | 2017 |
| Motion planning with sequential convex optimization and convex collision checking J Schulman, Y Duan, J Ho, A Lee, I Awwal, H Bradlow, J Pan, S Patil, ... The International Journal of Robotics Research 33 (9), 1251-1270, 2014 | 473 | 2014 |
| Adversarial attacks on neural network policies S Huang, N Papernot, I Goodfellow, Y Duan, P Abbeel arXiv preprint arXiv:1702.02284, 2017 | 461 | 2017 |
| #Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning H Tang, R Houthooft, D Foote, A Stooke, X Chen, Y Duan, J Schulman, ... arXiv preprint arXiv:1611.04717, 2016 | 449 | 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 | 447* | 2016 |
| Advances in neural information processing systems X Chen, Y Duan, R Houthooft, J Schulman, I Sutskever, P Abbeel Neural Information Processing Systems Fundation, Inc, 2015 | 397 | 2015 |
| Model-ensemble trust-region policy optimization T Kurutach, I Clavera, Y Duan, A Tamar, P Abbeel arXiv preprint arXiv:1802.10592, 2018 | 253 | 2018 |
| Stochastic neural networks for hierarchical reinforcement learning C Florensa, Y Duan, P Abbeel arXiv preprint arXiv:1704.03012, 2017 | 249 | 2017 |
| Evaluating protein transfer learning with TAPE R Rao, N Bhattacharya, N Thomas, Y Duan, X Chen, J Canny, P Abbeel, ... Advances in neural information processing systems 32, 9689, 2019 | 173 | 2019 |
| Flow++: Improving flow-based generative models with variational dequantization and architecture design J Ho, X Chen, A Srinivas, Y Duan, P Abbeel International Conference on Machine Learning, 2722-2730, 2019 | 168 | 2019 |
| Variance reduction for policy gradient with action-dependent factorized baselines C Wu, A Rajeswaran, Y Duan, V Kumar, AM Bayen, S Kakade, I Mordatch, ... arXiv preprint arXiv:1803.07246, 2018 | 100 | 2018 |
| The Importance of Sampling in Meta-Reinforcement Learning B Stadie, G Yang, R Houthooft, P Chen, Y Duan, Y Wu, P Abbeel, ... Advances in Neural Information Processing Systems, 9299-9309, 2018 | 80* | 2018 |
| Deep unsupervised cardinality estimation Z Yang, E Liang, A Kamsetty, C Wu, Y Duan, X Chen, P Abbeel, ... arXiv preprint arXiv:1905.04278, 2019 | 65 | 2019 |
| Sigma hulls for gaussian belief space planning for imprecise articulated robots amid obstacles A Lee, Y Duan, S Patil, J Schulman, Z McCarthy, J Van Den Berg, ... 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2013 | 37 | 2013 |
| Gaussian belief space planning with discontinuities in sensing domains S Patil, Y Duan, J Schulman, K Goldberg, P Abbeel 2014 IEEE International Conference on Robotics and Automation (ICRA), 6483-6490, 2014 | 31 | 2014 |
Pieter AbbeelUC Berkeley | Covariant.AIVerified email at cs.berkeley.edu
