| Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks C Finn, P Abbeel, S Levine International Conference on Machine Learning (ICML), 2017 | 15039 | 2017 |
| Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor T Haarnoja, A Zhou, P Abbeel, S Levine International conference on machine learning, 1861-1870, 2018 | 10792 | 2018 |
| Trust region policy optimization J Schulman, S Levine, P Moritz, MI Jordan, P Abbeel International Conference on Machine Learning (ICML 2015), 2015 | 9479 | 2015 |
| High-dimensional continuous control using generalized advantage estimation J Schulman, P Moritz, S Levine, M Jordan, P Abbeel International Conference on Learning Representations (ICLR 2016), 2015 | 4411 | 2015 |
| End-to-end training of deep visuomotor policies S Levine, C Finn, T Darrell, P Abbeel Journal of Machine Learning Research 17 (39), 1-40, 2016 | 4325 | 2016 |
| Soft actor-critic algorithms and applications T Haarnoja, A Zhou, K Hartikainen, G Tucker, S Ha, J Tan, V Kumar, ... arXiv preprint arXiv:1812.05905, 2018 | 3379 | 2018 |
| Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection S Levine, P Pastor, A Krizhevsky, J Ibarz, D Quillen The International Journal of Robotics Research 37 (4-5), 421-436, 2018 | 2930 | 2018 |
| Offline reinforcement learning: Tutorial, review, and perspectives on open problems S Levine, A Kumar, G Tucker, J Fu arXiv preprint arXiv:2005.01643, 2020 | 2288 | 2020 |
| Conservative q-learning for offline reinforcement learning A Kumar, A Zhou, G Tucker, S Levine Advances in neural information processing systems 33, 1179-1191, 2020 | 2184 | 2020 |
| Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates S Gu, E Holly, T Lillicrap, S Levine 2017 IEEE international conference on robotics and automation (ICRA), 3389-3396, 2017 | 2061 | 2017 |
| Palm-e: An embodied multimodal language model D Driess, F Xia, MSM Sajjadi, C Lynch, A Chowdhery, A Wahid, ... | 1802 | 2023 |
| Scalable deep reinforcement learning for vision-based robotic manipulation D Kalashnikov, A Irpan, P Pastor, J Ibarz, A Herzog, E Jang, D Quillen, ... Conference on robot learning, 651-673, 2018 | 1793 | 2018 |
| Deep reinforcement learning in a handful of trials using probabilistic dynamics models K Chua, R Calandra, R McAllister, S Levine Advances in neural information processing systems 31, 2018 | 1647 | 2018 |
| Reinforcement learning with deep energy-based policies T Haarnoja, H Tang, P Abbeel, S Levine International conference on machine learning, 1352-1361, 2017 | 1642 | 2017 |
| Wilds: A benchmark of in-the-wild distribution shifts PW Koh, S Sagawa, H Marklund, SM Xie, M Zhang, A Balsubramani, ... International conference on machine learning, 5637-5664, 2021 | 1620 | 2021 |
| Do as i can, not as i say: Grounding language in robotic affordances M Ahn, A Brohan, N Brown, Y Chebotar, O Cortes, B David, C Finn, C Fu, ... arXiv preprint arXiv:2204.01691, 2022 | 1607 | 2022 |
| Guided Policy Search S Levine, V Koltun International Conference on Machine Learning (ICML 2013), 2013 | 1422 | 2013 |
| D4rl: Datasets for deep data-driven reinforcement learning J Fu, A Kumar, O Nachum, G Tucker, S Levine arXiv preprint arXiv:2004.07219, 2020 | 1365 | 2020 |
| Continuous deep q-learning with model-based acceleration S Gu, T Lillicrap, I Sutskever, S Levine International conference on machine learning, 2829-2838, 2016 | 1362 | 2016 |
| Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning A Nagabandi, G Kahn, RS Fearing, S Levine 2018 IEEE international conference on robotics and automation (ICRA), 7559-7566, 2018 | 1339 | 2018 |
Chelsea FinnStanford University, Physical Intelligence確認したメール アドレス: cs.stanford.edu
