Houghton, Brandon

MineRL: A Large-Scale Dataset of Minecraft Demonstrations

Guss, W. H., Houghton, B., Topin, N., Wang, P., Codel, C., Veloso, M., & Salakhutdinov, R. (2019) · Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 94

Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos

Baker, B., Akkaya, I., Zhokhov, P., Huizinga, J., Tang, J., Ecoffet, A., Houghton, B., Sampedro, R., & Clune, J. (2022) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 75

OpenAI o1 System Card

OpenAI, :, Jaech, A., Kalai, A., Lerer, A., Richardson, A., El-Kishky, A., Low, A., Helyar, A., Madry, A., et al. (2024) · arXiv

Adversarial Robustness in Machine LearningEthics and Social Impacts of AI

Cited by 50

The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors

Guss, W. H., Codel, C., Hofmann, K., Houghton, B., Kuno, N., Milani, S., Mohanty, S., Liebana, D. P., Salakhutdinov, R., Topin, N., et al. (2019) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 46

Multi-task curriculum learning in a complex, visual, hard-exploration domain: Minecraft

Kanitscheider, I., Huizinga, J., Farhi, D., Guss, W. H., Houghton, B., Sampedro, R., Zhokhov, P., Baker, B., Ecoffet, A., Tang, J., et al. (2021) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 9