| A workflow for offline model-free robotic reinforcement learning A Kumar, A Singh, S Tian, C Finn, S Levine arXiv preprint arXiv:2109.10813, 2021 | 84 | 2021 |
| Action-quantized offline reinforcement learning for robotic skill learning J Luo, P Dong, J Wu, A Kumar, X Geng, S Levine Conference on Robot Learning, 1348-1361, 2023 | 11 | 2023 |
| Advantage-weighted regression: Simple and scalable off-policy reinforcement learning XB Peng, A Kumar, G Zhang, S Levine arXiv preprint arXiv:1910.00177, 2019 | 460 | 2019 |
| ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL Y Zhou, A Zanette, J Pan, S Levine, A Kumar arXiv preprint arXiv:2402.19446, 2024 | 6 | 2024 |
| Benchmarks for deep off-policy evaluation J Fu, M Norouzi, O Nachum, G Tucker, Z Wang, A Novikov, M Yang, ... arXiv preprint arXiv:2103.16596, 2021 | 79 | 2021 |
| Beyond uniform sampling: Offline reinforcement learning with imbalanced datasets ZW Hong, A Kumar, S Karnik, A Bhandwaldar, A Srivastava, J Pajarinen, ... Advances in Neural Information Processing Systems 36, 4985-5009, 2023 | 6 | 2023 |
| Cal-ql: Calibrated offline rl pre-training for efficient online fine-tuning M Nakamoto, S Zhai, A Singh, M Sobol Mark, Y Ma, C Finn, A Kumar, ... Advances in Neural Information Processing Systems 36, 2024 | 57 | 2024 |
| Calibration of Encoder Decoder Models for Neural Machine Translation A Kumar, S Sarawagi https://arxiv.org/abs/1903.00802, 2019 | 90 | 2019 |
| Challenges and tool implementation of hybrid rapidly-exploring random trees S Bak, S Bogomolov, TA Henzinger, A Kumar Numerical Software Verification: 10th International Workshop, NSV 2017 …, 2017 | 4 | 2017 |
| Challenges and Tool Implementation of Hybrid Rapidly-Exploring Random Trees (RRTs) S Bak, S Bogomolov, TA Henzinger, A Kumar | | |
| Cog: Connecting new skills to past experience with offline reinforcement learning A Singh, A Yu, J Yang, J Zhang, A Kumar, S Levine arXiv preprint arXiv:2010.14500, 2020 | 98 | 2020 |
| Combo: Conservative offline model-based policy optimization T Yu, A Kumar, R Rafailov, A Rajeswaran, S Levine, C Finn Advances in neural information processing systems 34, 28954-28967, 2021 | 356 | 2021 |
| Confidence-conditioned value functions for offline reinforcement learning J Hong, A Kumar, S Levine arXiv preprint arXiv:2212.04607, 2022 | 18 | 2022 |
| Conservative data sharing for multi-task offline reinforcement learning T Yu, A Kumar, Y Chebotar, K Hausman, S Levine, C Finn Advances in Neural Information Processing Systems 34, 11501-11516, 2021 | 75 | 2021 |
| Conservative objective models for effective offline model-based optimization B Trabucco, A Kumar, X Geng, S Levine International Conference on Machine Learning, 10358-10368, 2021 | 81 | 2021 |
| 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 | 1610 | 2020 |
| Conservative safety critics for exploration H Bharadhwaj, A Kumar, N Rhinehart, S Levine, F Shkurti, A Garg arXiv preprint arXiv:2010.14497, 2020 | 132 | 2020 |
| D4rl: Datasets for deep data-driven reinforcement learning J Fu, A Kumar, O Nachum, G Tucker, S Levine arXiv preprint arXiv:2004.07219, 2020 | 993 | 2020 |
| Data-driven deep reinforcement learning A Kumar Berkeley Artificial Intelligence Research (BAIR), Tech. Rep, 2019 | 18 | 2019 |
| Data-driven offline decision-making via invariant representation learning H Qi, Y Su, A Kumar, S Levine Advances in Neural Information Processing Systems 35, 13226-13237, 2022 | 14 | 2022 |
Sergey LevineUC Berkeley, Physical Intelligence在 eecs.berkeley.edu 的电子邮件经过验证
