| Deep q-learning from demonstrations T Hester, M Vecerik, O Pietquin, M Lanctot, T Schaul, B Piot, D Horgan, ... Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 1387 | 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 | 901 | 2017 |
| Challenges of real-world reinforcement learning G Dulac-Arnold, D Mankowitz, T Hester arXiv preprint arXiv:1904.12901, 2019 | 722 | 2019 |
| Challenges of real-world reinforcement learning: definitions, benchmarks and analysis G Dulac-Arnold, N Levine, DJ Mankowitz, J Li, C Paduraru, S Gowal, ... Machine Learning 110 (9), 2419-2468, 2021 | 604 | 2021 |
| Safe exploration in continuous action spaces G Dalal, K Dvijotham, M Vecerik, T Hester, C Paduraru, Y Tassa arXiv preprint arXiv:1801.08757, 2018 | 582 | 2018 |
| Eta prediction with graph neural networks in google maps A Derrow-Pinion, J She, D Wong, O Lange, T Hester, L Perez, ... Proceedings of the 30th ACM international conference on information …, 2021 | 311 | 2021 |
| 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 | 232 | 2020 |
| 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 | 219 | 2010 |
| Texplore: real-time sample-efficient reinforcement learning for robots T Hester, P Stone Machine learning 90, 385-429, 2013 | 197 | 2013 |
| Learned overrides for home security MR Malhotra, S Le Guen, JA Boyd, JT Lee, T Hester US Patent 9,520,049, 2016 | 191 | 2016 |
| Learning from demonstrations for real world reinforcement learning T Hester, M Vecerik, O Pietquin, M Lanctot, T Schaul, B Piot, A Sendonaris, ... arXiv preprint arXiv:1704.03732, 2017 | 185 | 2017 |
| 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 | 169 | 2015 |
| An empirical investigation of the challenges of real-world reinforcement learning G Dulac-Arnold, N Levine, DJ Mankowitz, J Li, C Paduraru, S Gowal, ... arXiv preprint arXiv:2003.11881, 2020 | 157 | 2020 |
| 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 | 155 | 2010 |
| 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 | 146 | 2018 |
| 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 | 134 | 2006 |
| Robust reinforcement learning for continuous control with model misspecification DJ Mankowitz, N Levine, R Jeong, Y Shi, J Kay, A Abdolmaleki, ... arXiv preprint arXiv:1906.07516, 2019 | 132 | 2019 |
| 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 | 131 | 2019 |
| Intrinsically motivated model learning for developing curious robots T Hester, P Stone Artificial Intelligence 247, 170-186, 2017 | 114 | 2017 |
| The utility of temporal abstraction in reinforcement learning. NK Jong, T Hester, P Stone AAMAS (1), 299-306, 2008 | 114 | 2008 |
Peter StoneProfessor of Computer Science, The University of Texas at AustinE-mailová adresa ověřena na: cs.utexas.edu
