| Overcoming exploration in reinforcement learning with demonstrations A Nair, B McGrew, M Andrychowicz, W Zaremba, P Abbeel IEEE International Conference on Robotics and Automation, 2017 | 491 | 2017 |
| Learning to poke by poking: Experiential learning of intuitive physics P Agrawal, A Nair, P Abbeel, J Malik, S Levine Advances in Neural Information Processing Systems, 2016 | 468 | 2016 |
| Visual reinforcement learning with imagined goals A Nair, V Pong, M Dalal, S Bahl, S Lin, S Levine Advances in Neural Information Processing Systems, 2018 | 336 | 2018 |
| Combining self-supervised learning and imitation for vision-based rope manipulation A Nair, D Chen, P Agrawal, P Isola, P Abbeel, J Malik, S Levine IEEE International Conference on Robotics and Automation, 2017 | 208 | 2017 |
| Residual reinforcement learning for robot control T Johannink, S Bahl, A Nair, J Luo, A Kumar, M Loskyll, JA Ojea, ... IEEE Conference on Robotics and Automation, 2018 | 188 | 2018 |
| Skew-fit: State-covering self-supervised reinforcement learning VH Pong, M Dalal, S Lin, A Nair, S Bahl, S Levine International Conference on Machine Learning, 2019 | 141 | 2019 |
| AWAC: Accelerating Online Reinforcement Learning with Offline Datasets A Nair, A Gupta, M Dalal, S Levine | 118 | 2020 |
| Deep reinforcement learning for industrial insertion tasks with visual inputs and natural rewards G Schoettler, A Nair, J Luo, S Bahl, JA Ojea, E Solowjow, S Levine IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019 | 87 | 2019 |
| Contextual imagined goals for self-supervised robotic learning A Nair, S Bahl, A Khazatsky, V Pong, G Berseth, S Levine Conference on Robot Learning, 2019 | 49 | 2019 |
| Offline reinforcement learning with implicit q-learning I Kostrikov, A Nair, S Levine International Conference on Learning Representations, 2021, 2021 | 26 | 2021 |
| Meta-reinforcement learning for robotic industrial insertion tasks G Schoettler, A Nair, JA Ojea, S Levine, E Solowjow 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2020 | 24 | 2020 |
| What Can I Do Here? Learning New Skills by Imagining Visual Affordances A Khazatsky, A Nair, D Jing, S Levine IEEE Interational Conference on Robotics and Automation, 2021 | 9 | 2021 |
| Disco rl: Distribution-conditioned reinforcement learning for general-purpose policies S Nasiriany, VH Pong, A Nair, A Khazatsky, G Berseth, S Levine 2021 IEEE International Conference on Robotics and Automation (ICRA), 6635-6641, 2021 | 8 | 2021 |
| Offline meta-reinforcement learning with online self-supervision VH Pong, A Nair, L Smith, C Huang, S Levine arXiv preprint arXiv:2107.03974, 2021 | 6 | 2021 |
| Planning to Practice: Efficient Online Fine-Tuning by Composing Goals in Latent Space K Fang, P Yin, A Nair, S Levine arXiv preprint arXiv:2205.08129, 2022 | | 2022 |
| Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning P Hansen-Estruch, A Zhang, A Nair, P Yin, S Levine arXiv preprint arXiv:2204.13060, 2022 | | 2022 |
| EE 126 Probability and Random Processes: Course Syllabus K Chandrasekher, A Nair University of California, Berkeley, 2016 | | 2016 |
| See What Your Robot Sees M Li, A Nair Global Conference on Educational Robotics, 2012 | | 2012 |
| DisCo RL: Distribution-Conditioned Reinforcement Learning for General-Purpose Policies Download PDF A Khazatsky, A Nair, S Levine, G Berseth, VH Pong, S Nasiriany | | |
Sergey LevineUC Berkeley, GoogleZweryfikowany adres z eecs.berkeley.edu
