Harley, Tim

Asynchronous Methods for Deep Reinforcement Learning

Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T. P., Harley, T., Silver, D., & Kavukcuoglu, K. (2016) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 7,132

Hybrid computing using a neural network with dynamic external memory

Graves, A., Wayne, G., Reynolds, M., Harley, T., Danihelka, I., Grabska-Barwińska, A., Colmenarejo, S. G., Grefenstette, E., Ramalho, T., Agapiou, J., et al. (2016) · Nature

Neural Networks and ApplicationsTopic Modeling

Cited by 1,412

Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes

Rae, J. W., Hunt, J. J., Harley, T., Danihelka, I., Senior, A., Wayne, G., Graves, A., & Lillicrap, T. P. (2016) · arXiv

Neural Networks and ApplicationsTopic Modeling

Cited by 89

Imitating Interactive Intelligence

Abramson, J., Ahuja, A., Barr, I., Brussee, A., Carnevale, F., Cassin, M., Chhaparia, R., Clark, S., Damoc, B., Dudzik, A., et al. (2020) · arXiv

Reinforcement Learning in RoboticsSocial Robot Interaction and HRI

Cited by 43

Scaling Instructable Agents Across Many Simulated Worlds

SIMA Team, Raad, M. A., Ahuja, A., Barros, C., Besse, F., Bolt, A., Bolton, A., Brownfield, B., Buttimore, G., Cant, M., et al. (2024) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 9