| 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 | 541 | 2018 |
| Meta-Learning with Implicit Gradients A Rajeswaran, C Finn, S Kakade, S Levine Advances in Neural Information Processing Systems (NeurIPS), 2019 | 430 | 2019 |
| Online Meta-Learning C Finn, A Rajeswaran, S Kakade, S Levine International Conference on Machine Learning (ICML), 2019 | 298 | 2019 |
| EPOpt: Learning Robust Neural Network Policies Using Model Ensembles A Rajeswaran, S Ghotra, B Ravindran, S Levine International Conference on Learning Representations (ICLR), 2017 | 293 | 2017 |
| Towards generalization and simplicity in continuous control A Rajeswaran, K Lowrey, EV Todorov, SM Kakade Advances in Neural Information Processing Systems 30, 2017 | 247 | 2017 |
| MOReL: Model-Based Offline Reinforcement Learning R Kidambi, A Rajeswaran, P Netrapalli, T Joachims Advances in Neural Information Processing Systems (NeurIPS), 2020 | 240 | 2020 |
| Decision transformer: Reinforcement learning via sequence modeling L Chen, K Lu, A Rajeswaran, K Lee, A Grover, M Laskin, P Abbeel, ... Advances in neural information processing systems 34, 15084-15097, 2021 | 163 | 2021 |
| 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 | 158 | 2019 |
| 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 | 154 | 2017 |
| 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 | 121 | 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 | 108 | 2019 |
| Divide-and-conquer reinforcement learning D Ghosh, A Singh, A Rajeswaran, V Kumar, S Levine International Conference on Learning Representations (ICLR), 2018 | 95 | 2018 |
| 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 | 84 | 2021 |
| A Game Theoretic Framework for Model Based Reinforcement Learning A Rajeswaran, I Mordatch, V Kumar International Conference on Machine Learning, 7953-7963, 2020 | 64 | 2020 |
| 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 | 49 | 2018 |
| Offline reinforcement learning from images with latent space models R Rafailov, T Yu, A Rajeswaran, C Finn Learning for Dynamics and Control, 1154-1168, 2021 | 40 | 2021 |
| A graph partitioning algorithm for leak detection in water distribution networks A Rajeswaran, S Narasimhan, S Narasimhan Computers & Chemical Engineering 108, 11-23, 2018 | 32 | 2018 |
| 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 | 30 | 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 | 26 | 2019 |
| The unsurprising effectiveness of pre-trained vision models for control S Parisi, A Rajeswaran, S Purushwalkam, A Gupta arXiv preprint arXiv:2203.03580, 2022 | 9 | 2022 |
Sergey LevineUC Berkeley, GoogleVerified email at eecs.berkeley.edu
