Hester, Todd

Deep Q-learning From Demonstrations

Hester, T., Vecerik, M., Pietquin, O., Lanctot, M., Schaul, T., Piot, B., Horgan, D., Quan, J., Sendonaris, A., Osband, I., et al. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 889

Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards

Vecerik, M., Hester, T., Scholz, J., Wang, F., Pietquin, O., Piot, B., Heess, N., Rothörl, T., Lampe, T., & Riedmiller, M. (2017) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 478

A Novel Approach to Monitor Rehabilitation Outcomes in Stroke Survivors Using Wearable Technology

Patel, S., Hughes, R., Hester, T., Stein, J., Akay, M., Dy, J. G., & Bonato, P. (2010) · Proceedings of the IEEE

Stroke Rehabilitation and RecoveryContext-Aware Activity Recognition Systems

Cited by 170

Using Wearable Sensors to Measure Motor Abilities following Stroke

Hester, T., Hughes, R., Sherrill, D. M., Knorr, B., Akay, M., Stein, J., & Bonato, P. (2006) · International Workshop on Wearable and Implantable Body Sensor Networks (BSN'06)

Stroke Rehabilitation and RecoveryContext-Aware Activity Recognition Systems

Cited by 82

An empirical investigation of the challenges of real-world reinforcement learning

Dulac-Arnold, G., Levine, N., Mankowitz, D. J., Li, J., Paduraru, C., Gowal, S., & Hester, T. (2020) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 78