| Optimal model-free output synchronization of heterogeneous systems using off-policy reinforcement learning H Modares, SP Nageshrao, GAD Lopes, R Babuška, FL Lewis Automatica 71, 334-341, 2016 | 176 | 2016 |
| Autonomous highway driving using deep reinforcement learning S Nageshrao, HE Tseng, D Filev 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC …, 2019 | 120 | 2019 |
| Reinforcement learning based compensation methods for robot manipulators YP Pane, SP Nageshrao, J Kober, R Babuška Engineering Applications of Artificial Intelligence 78, 236-247, 2019 | 103 | 2019 |
| Port-Hamiltonian systems in adaptive and learning control: A survey SP Nageshrao, GAD Lopes, D Jeltsema, R Babuška IEEE Transactions on Automatic Control 61 (5), 1223-1238, 2015 | 74 | 2015 |
| Deep reinforcement learning with enhanced safety for autonomous highway driving A Baheri, S Nageshrao, HE Tseng, I Kolmanovsky, A Girard, D Filev 2020 IEEE Intelligent Vehicles Symposium (IV), 1550-1555, 2020 | 58 | 2020 |
| Reinforcement learning for port-Hamiltonian systems O Sprangers, R Babuška, SP Nageshrao, GAD Lopes IEEE transactions on cybernetics 45 (5), 1017-1027, 2014 | 56 | 2014 |
| Explaining deep learning models through rule-based approximation and visualization E Soares, PP Angelov, B Costa, MPG Castro, S Nageshrao, D Filev IEEE Transactions on Fuzzy Systems 29 (8), 2399-2407, 2020 | 45 | 2020 |
| Game-theoretic lane-changing decision making and payoff learning for autonomous vehicles VG Lopez, FL Lewis, M Liu, Y Wan, S Nageshrao, D Filev IEEE Transactions on Vehicular Technology 71 (4), 3609-3620, 2022 | 41 | 2022 |
| Actor-critic reinforcement learning for tracking control in robotics YP Pane, SP Nageshrao, R Babuška 2016 IEEE 55th conference on decision and control (CDC), 5819-5826, 2016 | 36 | 2016 |
| Passivity-based reinforcement learning control of a 2-DOF manipulator arm SP Nageshrao, GAD Lopes, D Jeltsema, R Babuška Mechatronics 24 (8), 1001-1007, 2014 | 32 | 2014 |
| A three-level game-theoretic decision-making framework for autonomous vehicles M Liu, Y Wan, FL Lewis, S Nageshrao, D Filev IEEE Transactions on Intelligent Transportation Systems 23 (11), 20298-20308, 2022 | 23 | 2022 |
| Charging cost optimization for EV buses using neural network based energy predictor SP Nageshrao, J Jacob, S Wilkins IFAC-PapersOnLine 50 (1), 5947-5952, 2017 | 21 | 2017 |
| Conflict-aware safe reinforcement learning: A meta-cognitive learning framework M Mazouchi, S Nageshrao, H Modares IEEE/CAA Journal of Automatica Sinica 9 (3), 466-481, 2021 | 19 | 2021 |
| Discretionary lane change decision making using reinforcement learning with model-based exploration S Zhang, H Peng, S Nageshrao, E Tseng 2019 18th IEEE International Conference On Machine Learning And Applications …, 2019 | 19 | 2019 |
| Vehicle adaptive learning S Nageshrao, HE Tseng, DP Filev, RL Baker, C Cruise, L Daehler, ... US Patent 10,733,510, 2020 | 18 | 2020 |
| Interpretable-AI policies using evolutionary nonlinear decision trees for discrete action systems Y Dhebar, K Deb, S Nageshrao, L Zhu, D Filev arXiv preprint arXiv:2009.09521, 2020 | 18 | 2020 |
| Explainable density-based approach for self-driving actions classification E Soares, P Angelov, D Filev, B Costa, M Castro, S Nageshrao 2019 18th IEEE International Conference On Machine Learning And Applications …, 2019 | 17 | 2019 |
| Learning complex behaviors via sequential composition and passivity-based control GAD Lopes, E Najafi, SP Nageshrao, R Babuška Handling Uncertainty and Networked Structure in Robot Control, 53-74, 2015 | 16 | 2015 |
| Toward interpretable-AI policies using evolutionary nonlinear decision trees for discrete-action systems Y Dhebar, K Deb, S Nageshrao, L Zhu, D Filev IEEE Transactions on Cybernetics 54 (1), 50-62, 2022 | 15 | 2022 |
| Rapid learning in sequential composition control E Najafi, GAD Lopes, SP Nageshrao, R Babuška 53rd IEEE Conference on Decision and Control, 5171-5176, 2014 | 15 | 2014 |
Robert BabuskaProfessor, Delft University of Technology, Czech Technical University Prague在 tudelft.nl 的电子邮件经过验证
