| Offline reinforcement learning: Tutorial, review, and perspectives on open problems S Levine, A Kumar, G Tucker, J Fu arXiv preprint arXiv:2005.01643, 2020 | 1738 | 2020 |
| Stabilizing off-policy q-learning via bootstrapping error reduction A Kumar, J Fu, M Soh, G Tucker, S Levine Advances in Neural Information Processing Systems, 11761-11771, 2019 | 992 | 2019 |
| D4rl: Datasets for deep data-driven reinforcement learning J Fu, A Kumar, O Nachum, G Tucker, S Levine arXiv preprint arXiv:2004.07219, 2020 | 986 | 2020 |
| Learning Robust Rewards with Adversarial Inverse Reinforcement Learning J Fu, K Luo, S Levine International Conference on Learning Representations (ICLR), 2017 | 961 | 2017 |
| When to trust your model: Model-based policy optimization M Janner, J Fu, M Zhang, S Levine Advances in Neural Information Processing Systems, 12498-12509, 2019 | 917 | 2019 |
| Offline reinforcement learning: Tutorial, review S Levine, A Kumar, G Tucker, J Fu and Perspectives on Open Problems 5, 2020 | 190 | 2020 |
| One-shot learning of manipulation skills with online dynamics adaptation and neural network priors J Fu, S Levine, P Abbeel IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS …, 2016 | 184* | 2016 |
| Ex2: Exploration with exemplar models for deep reinforcement learning J Fu, J Co-Reyes, S Levine Advances in Neural Information Processing Systems, 2577-2587, 2017 | 180 | 2017 |
| Diagnosing Bottlenecks in Deep Q-learning Algorithms J Fu, A Kumar, M Soh, S Levine International Conference on Machine Learning (ICML), 2019 | 151 | 2019 |
| Learning to reach goals via iterated supervised learning D Ghosh, A Gupta, A Reddy, J Fu, C Devin, B Eysenbach, S Levine arXiv preprint arXiv:1912.06088, 2019 | 150 | 2019 |
| Variational inverse control with events: A general framework for data-driven reward definition J Fu, A Singh, D Ghosh, L Yang, S Levine Advances in Neural Information Processing Systems, 8538-8547, 2018 | 135 | 2018 |
| From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following J Fu, A Korattikara, S Levine, S Guadarrama International Conference on Learning Representations (ICLR), 2019 | 130 | 2019 |
| Generalizing Skills with Semi-Supervised Reinforcement Learning C Finn, T Yu, J Fu, P Abbeel, S Levine International Conference on Learning Representations (ICLR), 2017 | 82 | 2017 |
| Benchmarks for deep off-policy evaluation J Fu, M Norouzi, O Nachum, G Tucker, Z Wang, A Novikov, M Yang, ... arXiv preprint arXiv:2103.16596, 2021 | 78 | 2021 |
| Offline model-based optimization via normalized maximum likelihood estimation J Fu, S Levine arXiv preprint arXiv:2102.07970, 2021 | 52 | 2021 |
| Hierarchical model-based imitation learning for planning in autonomous driving E Bronstein, M Palatucci, D Notz, B White, A Kuefler, Y Lu, S Paul, ... 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2022 | 41 | 2022 |
| Imitation is not enough: Robustifying imitation with reinforcement learning for challenging driving scenarios Y Lu, J Fu, G Tucker, X Pan, E Bronstein, R Roelofs, B Sapp, B White, ... 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2023 | 40 | 2023 |
| Chai: A chatbot ai for task-oriented dialogue with offline reinforcement learning S Verma, J Fu, M Yang, S Levine arXiv preprint arXiv:2204.08426, 2022 | 34 | 2022 |
| Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research C Gulino, J Fu, W Luo, G Tucker, E Bronstein, Y Lu, J Harb, X Pan, ... Advances in Neural Information Processing Systems 36, 2024 | 29 | 2024 |
| Guided policy search code implementation, 2016 C Finn, M Zhang, J Fu, X Tan, Z McCarthy, E Scharff, S Levine Software available from rll. berkeley. edu/gps, 2016 | 28 | 2016 |
Sergey LevineUC Berkeley, Physical Intelligence在 eecs.berkeley.edu 的电子邮件经过验证
