| 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 | 6264 | 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 | 2401 | 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 | 1403 | 2016 |
| Evaluating protein transfer learning with TAPE R Rao, N Bhattacharya, N Thomas, Y Duan, P Chen, J Canny, P Abbeel, ... Advances in neural information processing systems 32, 2019 | 1247 | 2019 |
| Adversarial attacks on neural network policies S Huang, N Papernot, I Goodfellow, Y Duan, P Abbeel arXiv preprint arXiv:1702.02284, 2017 | 1224 | 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 | 1168 | 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 29, 2016 | 1106 | 2016 |
| One-shot imitation learning Y Duan, M Andrychowicz, B Stadie, OAI Jonathan Ho, J Schneider, ... Advances in neural information processing systems 30, 2017 | 915 | 2017 |
| Variational lossy autoencoder X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ... arXiv preprint arXiv:1611.02731, 2016 | 894 | 2016 |
| #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 | 837 | 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 | 808* | 2016 |
| Model-ensemble trust-region policy optimization T Kurutach, I Clavera, Y Duan, A Tamar, P Abbeel arXiv preprint arXiv:1802.10592, 2018 | 624 | 2018 |
| 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 | 599 | 2019 |
| Stochastic neural networks for hierarchical reinforcement learning C Florensa, Y Duan, P Abbeel arXiv preprint arXiv:1704.03012, 2017 | 467 | 2017 |
| Deep unsupervised cardinality estimation Z Yang, E Liang, A Kamsetty, C Wu, Y Duan, X Chen, P Abbeel, ... arXiv preprint arXiv:1905.04278, 2019 | 313 | 2019 |
| NeuroCard: one cardinality estimator for all tables Z Yang, A Kamsetty, S Luan, E Liang, Y Duan, X Chen, I Stoica arXiv preprint arXiv:2006.08109, 2020 | 272 | 2020 |
| Domain randomization and generative models for robotic grasping J Tobin, L Biewald, R Duan, M Andrychowicz, A Handa, V Kumar, ... 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2018 | 204 | 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 | 201 | 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 | 197* | 2018 |
| Attacking machine learning with adversarial examples I Goodfellow, N Papernot, S Huang, Y Duan, P Abbeel, J Clark OpenAI Blog 24 (1), 2017 | 96 | 2017 |
Pieter AbbeelUC Berkeley | Amazon FARVerified email at cs.berkeley.edu
