| Deep q-learning from demonstrations T Hester, M Vecerik, O Pietquin, M Lanctot, T Schaul, B Piot, D Horgan, ... Thirty-second AAAI conference on artificial intelligence, 2018 | 573 | 2018 |
| Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards M Vecerik, T Hester, J Scholz, F Wang, O Pietquin, B Piot, N Heess, ... arXiv preprint arXiv:1707.08817, 2017 | 342 | 2017 |
| Challenges of real-world reinforcement learning G Dulac-Arnold, D Mankowitz, T Hester arXiv preprint arXiv:1904.12901, 2019 | 247 | 2019 |
| A novel approach to monitor rehabilitation outcomes in stroke survivors using wearable technology S Patel, R Hughes, T Hester, J Stein, M Akay, JG Dy, P Bonato Proceedings of the IEEE 98 (3), 450-461, 2010 | 187 | 2010 |
| Safe exploration in continuous action spaces G Dalal, K Dvijotham, M Vecerik, T Hester, C Paduraru, Y Tassa arXiv preprint arXiv:1801.08757, 2018 | 151 | 2018 |
| Learning from demonstrations for real world reinforcement learning T Hester, M Vecerik, O Pietquin, M Lanctot, T Schaul, B Piot, A Sendonaris, ... | 135 | 2017 |
| Generalized model learning for reinforcement learning on a humanoid robot T Hester, M Quinlan, P Stone 2010 IEEE International Conference on Robotics and Automation, 2369-2374, 2010 | 133 | 2010 |
| Texplore: real-time sample-efficient reinforcement learning for robots T Hester, P Stone Machine learning 90 (3), 385-429, 2013 | 122 | 2013 |
| Using wearable sensors to measure motor abilities following stroke T Hester, R Hughes, DM Sherrill, B Knorr, M Akay, J Stein, P Bonato International Workshop on Wearable and Implantable Body Sensor Networks (BSN …, 2006 | 116 | 2006 |
| Methods and apparatus for using smart environment devices via application program interfaces I Karp, L Stesin, C Pi-Sunyer, MA McBride, A Dubman, J Lyons, SW Kortz, ... US Patent 10,638,292, 2020 | 111 | 2020 |
| Intelligent configuration of a smart environment based on arrival time PP Reddy, M Malhotra, EJ Fisher, T Hester, MA McBride, Y Matsuoka US Patent App. 14/531,805, 2015 | 100 | 2015 |
| Learned overrides for home security MR Malhotra, S Le Guen, JA Boyd, JT Lee, T Hester US Patent 9,520,049, 2016 | 83 | 2016 |
| The utility of temporal abstraction in reinforcement learning. NK Jong, T Hester, P Stone AAMAS (1), 299-306, 2008 | 76 | 2008 |
| Intrinsically motivated model learning for developing curious robots T Hester, P Stone Artificial Intelligence 247, 170-186, 2017 | 73 | 2017 |
| Generalized model learning for reinforcement learning in factored domains. T Hester, P Stone AAMAS (2), 717-724, 2009 | 69 | 2009 |
| RTMBA: A real-time model-based reinforcement learning architecture for robot control T Hester, M Quinlan, P Stone 2012 IEEE International Conference on Robotics and Automation, 85-90, 2012 | 68 | 2012 |
| Analysis of the severity of dyskinesia in patients with Parkinson's disease via wearable sensors S Patel, D Sherrill, R Hughes, T Hester, N Huggins, T Lie-Nemeth, ... International Workshop on Wearable and Implantable Body Sensor Networks (BSN …, 2006 | 67 | 2006 |
| Tracking motor recovery in stroke survivors undergoing rehabilitation using wearable technology S Patel, R Hughes, T Hester, J Stein, M Akay, J Dy, P Bonato 2010 Annual International Conference of the IEEE Engineering in Medicine and …, 2010 | 58 | 2010 |
| A practical approach to insertion with variable socket position using deep reinforcement learning M Vecerik, O Sushkov, D Barker, T Rothörl, T Hester, J Scholz 2019 international conference on robotics and automation (ICRA), 754-760, 2019 | 57 | 2019 |
| Observe and look further: Achieving consistent performance on atari T Pohlen, B Piot, T Hester, MG Azar, D Horgan, D Budden, G Barth-Maron, ... arXiv preprint arXiv:1805.11593, 2018 | 57 | 2018 |
Peter StoneProfessor of Computer Science, The University of Texas at AustinVerified email at cs.utexas.edu
