| Learning complex dexterous manipulation with deep reinforcement learning and demonstrations A Rajeswaran, V Kumar, A Gupta, G Vezzani, J Schulman, E Todorov, ... Robotics: Science and Systems (RSS), 2018 | 257 | 2018 |
| EPOpt: Learning Robust Neural Network Policies Using Model Ensembles A Rajeswaran, S Ghotra, B Ravindran, S Levine International Conference on Learning Representations (ICLR), 2017 | 180 | 2017 |
| Towards generalization and simplicity in continuous control A Rajeswaran, K Lowrey, E Todorov, S Kakade arXiv preprint arXiv:1703.02660, 2017 | 179 | 2017 |
| Meta-learning with implicit gradients A Rajeswaran, C Finn, S Kakade, S Levine arXiv preprint arXiv:1909.04630, 2019 | 118 | 2019 |
| Online Meta-Learning C Finn, A Rajeswaran, S Kakade, S Levine International Conference on Machine Learning (ICML), 2019 | 111 | 2019 |
| Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control K Lowrey, A Rajeswaran, S Kakade, E Todorov, I Mordatch International Conference on Learning Representations (ICLR), 2019 | 75 | 2019 |
| Variance reduction for policy gradient with action-dependent factorized baselines C Wu, A Rajeswaran, Y Duan, V Kumar, AM Bayen, S Kakade, I Mordatch, ... International Conference on Learning Representations (ICLR), 2018 | 74 | 2018 |
| Identifying topology of low voltage distribution networks based on smart meter data SJ Pappu, N Bhatt, R Pasumarthy, A Rajeswaran IEEE Transactions on Smart Grid 9 (5), 5113-5122, 2017 | 63 | 2017 |
| Divide-and-conquer reinforcement learning D Ghosh, A Singh, A Rajeswaran, V Kumar, S Levine International Conference on Learning Representations (ICLR), 2018 | 58 | 2018 |
| Dexterous manipulation with deep reinforcement learning: Efficient, general, and low-cost H Zhu, A Gupta, A Rajeswaran, S Levine, V Kumar International Conference on Robotics and Automation (ICRA), 2019 | 48 | 2019 |
| Reinforcement learning for non-prehensile manipulation: Transfer from simulation to physical system K Lowrey, S Kolev, J Dao, A Rajeswaran, E Todorov 2018 IEEE International Conference on Simulation, Modeling, and Programming …, 2018 | 27 | 2018 |
| A graph partitioning algorithm for leak detection in water distribution networks A Rajeswaran, S Narasimhan, S Narasimhan Computers & Chemical Engineering 108, 11-23, 2018 | 19 | 2018 |
| Morel: Model-based offline reinforcement learning R Kidambi, A Rajeswaran, P Netrapalli, T Joachims arXiv preprint arXiv:2005.05951, 2020 | 16 | 2020 |
| A novel approach for phase identification in smart grids using graph theory and principal component analysis SP Jayadev, A Rajeswaran, NP Bhatt, R Pasumarthy 2016 American Control Conference (ACC), 5026-5031, 2016 | 15 | 2016 |
| Learning deep visuomotor policies for dexterous hand manipulation D Jain, A Li, S Singhal, A Rajeswaran, V Kumar, E Todorov 2019 International Conference on Robotics and Automation (ICRA), 3636-3643, 2019 | 10 | 2019 |
| A game theoretic framework for model based reinforcement learning A Rajeswaran, I Mordatch, V Kumar International Conference on Machine Learning, 7953-7963, 2020 | 9 | 2020 |
| Network topology identification using PCA and its graph theoretic interpretations A Rajeswaran, S Narasimhan arXiv preprint arXiv:1506.00438, 2015 | 8 | 2015 |
| Lyceum: An efficient and scalable ecosystem for robot learning C Summers, K Lowrey, A Rajeswaran, S Srinivasa, E Todorov Learning for Dynamics and Control, 793-803, 2020 | 4 | 2020 |
| Reinforcement Learning with Latent Flow W Shang, X Wang, A Srinivas, A Rajeswaran, Y Gao, P Abbeel, M Laskin arXiv preprint arXiv:2101.01857, 2021 | | 2021 |
| Offline Reinforcement Learning from Images with Latent Space Models R Rafailov, T Yu, A Rajeswaran, C Finn arXiv preprint arXiv:2012.11547, 2020 | | 2020 |
Sergey LevineUC Berkeley, GoogleVerified email at eecs.berkeley.edu
