Subramanya Prasad Nageshrao
Subramanya Prasad Nageshrao
University of Michigan, Ford research center
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Zitiert von
Zitiert von
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
Autonomous highway driving using deep reinforcement learning
S Nageshrao, HE Tseng, D Filev
2019 IEEE International Conference on Systems, Man and Cybernetics (SMC …, 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
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
Reinforcement learning for port-Hamiltonian systems
O Sprangers, R Babuška, SP Nageshrao, GAD Lopes
IEEE transactions on cybernetics 45 (5), 1017-1027, 2014
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
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
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
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
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
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
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
Vehicle adaptive learning
S Nageshrao, HE Tseng, DP Filev, RL Baker, C Cruise, L Daehler, ...
US Patent 10,733,510, 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
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
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
Interpretable approximation of a deep reinforcement learning agent as a set of if-then rules
S Nageshrao, B Costa, D Filev
2019 18th IEEE International Conference On Machine Learning And Applications …, 2019
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
Rapid learning in sequential composition control
E Najafi, GAD Lopes, SP Nageshrao, R Babuška
53rd IEEE Conference on Decision and Control, 5171-5176, 2014
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
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