| Diversity is all you need: Learning skills without a reward function B Eysenbach, A Gupta, J Ibarz, S Levine International Conference on Learning Representations, 2019 | 800 | 2019 |
| Search on the replay buffer: Bridging planning and reinforcement learning B Eysenbach, R Salakhutdinov, S Levine Advances in Neural Information Processing Systems, 15246-15257, 2019 | 200 | 2019 |
| Efficient exploration via state marginal matching L Lee, B Eysenbach, E Parisotto, E Xing, S Levine, R Salakhutdinov arXiv preprint arXiv:1906.05274, 2019 | 177 | 2019 |
| Self-consistent trajectory autoencoder: Hierarchical reinforcement learning with trajectory embeddings JD Co-Reyes, YX Liu, A Gupta, B Eysenbach, P Abbeel, S Levine International Conference on Machine Learning, 2018 | 138 | 2018 |
| Clustervision: Visual supervision of unsupervised clustering BC Kwon, B Eysenbach, J Verma, K Ng, C De Filippi, WF Stewart, A Perer IEEE transactions on visualization and computer graphics 24 (1), 142-151, 2017 | 134 | 2017 |
| Leave No Trace: Learning to reset for safe and autonomous reinforcement learning B Eysenbach, S Gu, J Ibarz, S Levine International Conference on Learning Representations, 2018 | 123 | 2018 |
| Unsupervised meta-learning for reinforcement learning A Gupta, B Eysenbach, C Finn, S Levine arXiv preprint arXiv:1806.04640, 2018 | 120 | 2018 |
| Learning to Reach Goals via Iterated Supervised Learning D Ghosh, A Gupta, A Reddy, J Fu, C Devin, B Eysenbach, S Levine International Conference on Learning Representations, 2021 | 103* | 2021 |
| Learning to be safe: Deep rl with a safety critic K Srinivasan, B Eysenbach, S Ha, J Tan, C Finn arXiv preprint arXiv:2010.14603, 2020 | 91 | 2020 |
| Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement B Eysenbach, X Geng, S Levine, R Salakhutdinov Advances in Neural Information Processing Systems 33, 2020 | 77 | 2020 |
| Maximum Entropy RL (Provably) Solves Some Robust RL Problems B Eysenbach, S Levine International Conference on Learning Representations, 2022 | 76 | 2022 |
| Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills Y Chebotar, K Hausman, Y Lu, T Xiao, D Kalashnikov, J Varley, A Irpan, ... International Conference on Machine Learning, 2021 | 76 | 2021 |
| Unsupervised curricula for visual meta-reinforcement learning A Jabri, K Hsu, A Gupta, B Eysenbach, S Levine, C Finn Advances in Neural Information Processing Systems, 2019 | 60 | 2019 |
| RvS: What is Essential for Offline RL via Supervised Learning? S Emmons, B Eysenbach, I Kostrikov, S Levine arXiv preprint arXiv:2112.10751, 2021 | 54 | 2021 |
| If MaxEnt RL is the Answer, What is the Question? B Eysenbach, S Levine arXiv preprint arXiv:1910.01913, 2019 | 47 | 2019 |
| Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers B Eysenbach, S Chaudhari, S Asawa, S Levine, R Salakhutdinov International Conference on Learning Representations, 2020 | 46 | 2020 |
| C-Learning: Learning to Achieve Goals via Recursive Classification B Eysenbach, R Salakhutdinov, S Levine International Conference on Learning Representations, 2021 | 44 | 2021 |
| Ving: Learning open-world navigation with visual goals D Shah, B Eysenbach, G Kahn, N Rhinehart, S Levine 2021 IEEE International Conference on Robotics and Automation (ICRA), 13215 …, 2021 | 36 | 2021 |
| Model-Based Visual Planning with Self-Supervised Functional Distances S Tian, S Nair, F Ebert, S Dasari, B Eysenbach, C Finn, S Levine International Conference on Learning Representations, 2021 | 35 | 2021 |
| f-IRL: Inverse Reinforcement Learning via State Marginal Matching T Ni, H Sikchi, Y Wang, T Gupta, L Lee, B Eysenbach Conference on Robot Learning, 2020 | 35 | 2020 |
Sergey LevineUC Berkeley, GoogleVerified email at eecs.berkeley.edu
