| Gpt-4 technical report J Achiam, S Adler, S Agarwal, L Ahmad, I Akkaya, FL Aleman, D Almeida, ... arXiv preprint arXiv:2303.08774, 2023 | 6199 | 2023 |
| Visual reinforcement learning with imagined goals AV Nair, V Pong, M Dalal, S Bahl, S Lin, S Levine Advances in neural information processing systems 31, 2018 | 616 | 2018 |
| Uncertainty-aware reinforcement learning for collision avoidance G Kahn, A Villaflor, V Pong, P Abbeel, S Levine arXiv preprint arXiv:1702.01182, 2017 | 394 | 2017 |
| Skew-fit: State-covering self-supervised reinforcement learning VH Pong, M Dalal, S Lin, A Nair, S Bahl, S Levine arXiv preprint arXiv:1903.03698, 2019 | 301 | 2019 |
| Temporal difference models: Model-free deep rl for model-based control V Pong, S Gu, M Dalal, S Levine arXiv preprint arXiv:1802.09081, 2018 | 301 | 2018 |
| Composable deep reinforcement learning for robotic manipulation T Haarnoja, V Pong, A Zhou, M Dalal, P Abbeel, S Levine 2018 IEEE international conference on robotics and automation (ICRA), 6244-6251, 2018 | 296 | 2018 |
| Planning with goal-conditioned policies S Nasiriany, V Pong, S Lin, S Levine Advances in neural information processing systems 32, 2019 | 257 | 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, 530-539, 2020 | 93 | 2020 |
| Offline meta-reinforcement learning with online self-supervision VH Pong, AV Nair, LM Smith, C Huang, S Levine International Conference on Machine Learning, 17811-17829, 2022 | 77 | 2022 |
| Reactive high-level behavior synthesis for an atlas humanoid robot S Maniatopoulos, P Schillinger, V Pong, DC Conner, H Kress-Gazit 2016 IEEE international conference on robotics and automation (ICRA), 4192-4199, 2016 | 67 | 2016 |
| Introducing chatgpt J Schulman, B Zoph, C Kim, J Hilton, J Menick, J Weng, JFC Uribe, ... OpenAI Blog, 2022 | 45 | 2022 |
| Mural: Meta-learning uncertainty-aware rewards for outcome-driven reinforcement learning K Li, A Gupta, A Reddy, VH Pong, A Zhou, J Yu, S Levine International conference on machine learning, 6346-6356, 2021 | 40 | 2021 |
| Replab: A reproducible low-cost arm benchmark platform for robotic learning B Yang, J Zhang, V Pong, S Levine, D Jayaraman arXiv preprint arXiv:1905.07447, 2019 | 30 | 2019 |
| 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 | 20 | 2021 |
| Outcome-driven reinforcement learning via variational inference TGJ Rudner, V Pong, R McAllister, Y Gal, S Levine Advances in Neural Information Processing Systems 34, 13045-13058, 2021 | 17 | 2021 |
| Two evolving social network models SR Magura, VH Pong, D Sivakoff, R Durrett ALEA 12 (2), 699-715, 2015 | 2 | 2015 |
| Goal-Directed Exploration and Skill Reuse VH Pong University of California, Berkeley, 2021 | 1 | 2021 |
| Reinforcement learning with bayesian classifiers: Efficient skill learning from outcome examples K Li, A Gupta, VH Pong, A Reddy, A Zhou, J Yu, S Levine | 1 | 2020 |
| 最新无模型深度强化学习研究: 从零开始训练机器人 “玩乐高” T Haarnoja, V Pong, A Zhou, M Dalal, P Abbeel, S Levine 机器人产业, 2018 | | 2018 |
| TDM: From model-free to model-based deep reinforcement learning V Pong | | |
Sergey LevineUC Berkeley, Physical IntelligenceVerified email at eecs.berkeley.edu
