Todd Hester
Todd Hester
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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
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
Challenges of real-world reinforcement learning
G Dulac-Arnold, D Mankowitz, T Hester
arXiv preprint arXiv:1904.12901, 2019
Safe exploration in continuous action spaces
G Dalal, K Dvijotham, M Vecerik, T Hester, C Paduraru, Y Tassa
arXiv preprint arXiv:1801.08757, 2018
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
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
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
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
Learned overrides for home security
MR Malhotra, S Le Guen, JA Boyd, JT Lee, T Hester
US Patent 9,520,049, 2016
Texplore: real-time sample-efficient reinforcement learning for robots
T Hester, P Stone
Machine learning 90, 385-429, 2013
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
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
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
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
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
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
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
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
The utility of temporal abstraction in reinforcement learning.
NK Jong, T Hester, P Stone
AAMAS (1), 299-306, 2008
Intrinsically motivated model learning for developing curious robots
T Hester, P Stone
Artificial Intelligence 247, 170-186, 2017
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