| Data-Efficient Hierarchical Reinforcement Learning O Nachum, S Gu, H Lee, S Levine Advances in Neural Information Processing Systems, 2018 | 824 | 2018 |
| D4rl: Datasets for deep data-driven reinforcement learning J Fu, A Kumar, O Nachum, G Tucker, S Levine arXiv preprint arXiv:2004.07219, 2020 | 706 | 2020 |
| Behavior regularized offline reinforcement learning Y Wu, G Tucker, O Nachum arXiv preprint arXiv:1911.11361, 2019 | 538 | 2019 |
| A Lyapunov-based Approach to Safe Reinforcement Learning Y Chow, O Nachum, E Duenez-Guzman, M Ghavamzadeh Advances in Neural Information Processing Systems, 2018 | 480 | 2018 |
| Bridging the gap between value and policy based reinforcement learning O Nachum, M Norouzi, K Xu, D Schuurmans Advances in neural information processing systems 30, 2017 | 452 | 2017 |
| Learning to remember rare events Ł Kaiser, O Nachum, A Roy, S Bengio International Conference for Learning Representations, 2017 | 391 | 2017 |
| Morphnet: Fast & simple resource-constrained structure learning of deep networks A Gordon, E Eban, O Nachum, B Chen, H Wu, TJ Yang, E Choi Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 366 | 2018 |
| Dualdice: Behavior-agnostic estimation of discounted stationary distribution corrections O Nachum, Y Chow, B Dai, L Li Advances in neural information processing systems 32, 2019 | 279 | 2019 |
| Deepmdp: Learning continuous latent space models for representation learning C Gelada, S Kumar, J Buckman, O Nachum, MG Bellemare International Conference on Machine Learning, 2170-2179, 2019 | 262 | 2019 |
| Identifying and correcting label bias in machine learning H Jiang, O Nachum International Conference on Artificial Intelligence and Statistics, 702-712, 2020 | 249 | 2020 |
| Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods D Quillen, E Jang, O Nachum, C Finn, J Ibarz, S Levine IEEE International Conference on Robotics and Automation, 2018 | 234 | 2018 |
| Rt-1: Robotics transformer for real-world control at scale A Brohan, N Brown, J Carbajal, Y Chebotar, J Dabis, C Finn, ... arXiv preprint arXiv:2212.06817, 2022 | 226 | 2022 |
| Lyapunov-based safe policy optimization for continuous control Y Chow, O Nachum, A Faust, E Duenez-Guzman, M Ghavamzadeh arXiv preprint arXiv:1901.10031, 2019 | 213 | 2019 |
| Near-optimal representation learning for hierarchical reinforcement learning O Nachum, S Gu, H Lee, S Levine arXiv preprint arXiv:1810.01257, 2018 | 209 | 2018 |
| Offline reinforcement learning with fisher divergence critic regularization I Kostrikov, R Fergus, J Tompson, O Nachum International Conference on Machine Learning, 5774-5783, 2021 | 207 | 2021 |
| Algaedice: Policy gradient from arbitrary experience O Nachum, B Dai, I Kostrikov, Y Chow, L Li, D Schuurmans arXiv preprint arXiv:1912.02074, 2019 | 190 | 2019 |
| Imitation learning via off-policy distribution matching I Kostrikov, O Nachum, J Tompson arXiv preprint arXiv:1912.05032, 2019 | 150 | 2019 |
| Rl unplugged: A suite of benchmarks for offline reinforcement learning C Gulcehre, Z Wang, A Novikov, T Paine, S Gómez, K Zolna, R Agarwal, ... Advances in Neural Information Processing Systems 33, 7248-7259, 2020 | 136* | 2020 |
| Opal: Offline primitive discovery for accelerating offline reinforcement learning A Ajay, A Kumar, P Agrawal, S Levine, O Nachum arXiv preprint arXiv:2010.13611, 2020 | 132 | 2020 |
| Trust-pcl: An off-policy trust region method for continuous control O Nachum, M Norouzi, K Xu, D Schuurmans International Conference for Learning Representations, 2018 | 120 | 2018 |
Dale SchuurmansUniversity of Alberta, GoogleVerified email at cs.ualberta.ca
