| 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 | 1447 | 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 | 649 | 2016 |
| 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 | 279 | 2014 |
| Vime: Variational information maximizing exploration R Houthooft, X Chen, Y Duan, J Schulman, F De Turck, P Abbeel Advances in Neural Information Processing Systems, 1109-1117, 2016 | 278* | 2016 |
| Variational lossy autoencoder X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ... arXiv preprint arXiv:1611.02731, 2016 | 257 | 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 | 251* | 2016 |
| One-shot imitation learning Y Duan, M Andrychowicz, B Stadie, OAIJ Ho, J Schneider, I Sutskever, ... Advances in neural information processing systems, 1087-1098, 2017 | 233 | 2017 |
| 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 | 223 | 2016 |
| Adversarial attacks on neural network policies S Huang, N Papernot, I Goodfellow, Y Duan, P Abbeel arXiv preprint arXiv:1702.02284, 2017 | 189 | 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 | 154 | 2016 |
| Stochastic neural networks for hierarchical reinforcement learning C Florensa, Y Duan, P Abbeel arXiv preprint arXiv:1704.03012, 2017 | 115 | 2017 |
| Model-ensemble trust-region policy optimization T Kurutach, I Clavera, Y Duan, A Tamar, P Abbeel arXiv preprint arXiv:1802.10592, 2018 | 56 | 2018 |
| 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 | 39 | 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 | 30* | 2018 |
| 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 | 26 | 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 | 24 | 2014 |
| Flow++: Improving flow-based generative models with variational dequantization and architecture design J Ho, X Chen, A Srinivas, Y Duan, P Abbeel arXiv preprint arXiv:1902.00275, 2019 | 18 | 2019 |
| Planning locally optimal, curvature-constrained trajectories in 3D using sequential convex optimization Y Duan, S Patil, J Schulman, K Goldberg, P Abbeel 2014 IEEE International Conference on Robotics and Automation (ICRA), 5889-5895, 2014 | 18 | 2014 |
| Meta learning for control Y Duan UC Berkeley, 2017 | 3 | 2017 |
| Evaluating Protein Transfer Learning with TAPE R Rao, N Bhattacharya, N Thomas, Y Duan, X Chen, J Canny, P Abbeel, ... arXiv preprint arXiv:1906.08230, 2019 | 1 | 2019 |
Pieter AbbeelUC Berkeley | Covariant.AIVerified email at cs.berkeley.edu
