| Offline reinforcement learning: Tutorial, review, and perspectives on open problems S Levine, A Kumar, G Tucker, J Fu arXiv preprint arXiv:2005.01643, 2020 | 963 | 2020 |
| Learning Robust Rewards with Adversarial Inverse Reinforcement Learning J Fu, K Luo, S Levine International Conference on Learning Representations (ICLR), 2017 | 696 | 2017 |
| 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 | 593 | 2019 |
| 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 | 592 | 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 | 484 | 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 | 167 | 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 | 153 | 2017 |
| Diagnosing Bottlenecks in Deep Q-learning Algorithms J Fu, A Kumar, M Soh, S Levine International Conference on Machine Learning (ICML), 2019 | 114 | 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 | 109 | 2018 |
| Offline Reinforcement Learning: Tutorial, Review S Levine, A Kumar, G Tucker, J Fu and Perspectives on Open Problems 5, 2020 | 103 | 2020 |
| 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 | 102 | 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 | 77 | 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 | 72 | 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 | 46 | 2021 |
| 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 | 25 | 2016 |
| Offline model-based optimization via normalized maximum likelihood estimation J Fu, S Levine arXiv preprint arXiv:2102.07970, 2021 | 24 | 2021 |
| Learning to reach goals without reinforcement learning D Ghosh, A Gupta, J Fu, A Reddy, C Devin, B Eysenbach, S Levine | 24 | 2019 |
| 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 | 14 | 2022 |
| Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios Y Lu, J Fu, G Tucker, X Pan, E Bronstein, B Roelofs, B Sapp, B White, ... arXiv preprint arXiv:2212.11419, 2022 | 7 | 2022 |
| 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 | 7 | 2022 |
Sergey LevineUC Berkeley, GoogleVerified email at eecs.berkeley.edu
