| Octo: An open-source generalist robot policy OM Team, D Ghosh, H Walke, K Pertsch, K Black, O Mees, S Dasari, ... arXiv preprint arXiv:2405.12213 2 (3), 7, 2024 | 257* | 2024 |
| 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 (ICLR 2021), arXiv …, 2021 | 230* | 2021 |
| 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 31, 2018 | 154 | 2018 |
| Learning Actionable Representations with Goal-Conditioned Policies D Ghosh, A Gupta, S Levine International Conference on Learning Representations (ICLR 2019), 2019 | 148 | 2019 |
| Why generalization in rl is difficult: Epistemic pomdps and implicit partial observability D Ghosh, J Rahme, A Kumar, A Zhang, RP Adams, S Levine Advances in neural information processing systems 34, 25502-25515, 2021 | 133 | 2021 |
| Divide-and-conquer reinforcement learning D Ghosh, A Singh, A Rajeswaran, V Kumar, S Levine International Conference on Learning Representations (ICLR 2018), 2018 | 128 | 2018 |
| Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning A Kumar, R Agarwal, D Ghosh, S Levine International Conference on Learning Representations (ICLR 2021), 2021 | 124 | 2021 |
| Representations for stable off-policy reinforcement learning D Ghosh, MG Bellemare International Conference on Machine Learning, 3556-3565, 2020 | 55 | 2020 |
| Hiql: Offline goal-conditioned rl with latent states as actions S Park, D Ghosh, B Eysenbach, S Levine Advances in Neural Information Processing Systems 36, 34866-34891, 2023 | 51 | 2023 |
| Offline rl policies should be trained to be adaptive D Ghosh, A Ajay, P Agrawal, S Levine International Conference on Machine Learning, 7513-7530, 2022 | 50 | 2022 |
| Reinforcement learning from passive data via latent intentions D Ghosh, CA Bhateja, S Levine International Conference on Machine Learning, 11321-11339, 2023 | 45 | 2023 |
| An operator view of policy gradient methods D Ghosh, M C Machado, N Le Roux Advances in Neural Information Processing Systems 33, 3397-3406, 2020 | 28 | 2020 |
| Robotic offline rl from internet videos via value-function pre-training C Bhateja, D Guo, D Ghosh, A Singh, M Tomar, Q Vuong, Y Chebotar, ... arXiv preprint arXiv:2309.13041, 2023 | 24* | 2023 |
| On catastrophic interference in atari 2600 games W Fedus, D Ghosh, JD Martin, MG Bellemare, Y Bengio, H Larochelle arXiv preprint arXiv:2002.12499, 2020 | 19 | 2020 |
| Distributionally adaptive meta reinforcement learning A Ajay, A Gupta, D Ghosh, S Levine, P Agrawal Advances in Neural Information Processing Systems 35, 25856-25869, 2022 | 17 | 2022 |
| Accelerating exploration with unlabeled prior data Q Li, J Zhang, D Ghosh, A Zhang, S Levine Advances in Neural Information Processing Systems 36, 67434-67458, 2023 | 9 | 2023 |
| What Do Learning Dynamics Reveal About Generalization in LLM Reasoning? K Kang, A Setlur, D Ghosh, J Steinhardt, C Tomlin, S Levine, A Kumar arXiv preprint arXiv:2411.07681, 2024 | 3 | 2024 |
| Video-Guided Skill Discovery M Tomar, D Ghosh, V Myers, A Dragan, ME Taylor, P Bachman, S Levine ICML 2023 Workshop The Many Facets of Preference-Based Learning, 2023 | 2 | 2023 |
| Reward Hacking vs. Reward Conquering: A Comparative Analysis in Layman’s Term D Ghosh | | |
| Generative Evolutionary MAML: Decoupling Latent Space and Learning D Ghosh | | |
Sergey LevineUC Berkeley, Physical Intelligenceایمیل تأیید شده در eecs.berkeley.edu