Rowland, Mark

Distributional Reinforcement Learning With Quantile Regression

Dabney, W., Rowland, M., Bellemare, M., & Munos, R. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 612

Meta-learning of Sequential Strategies

Ortega, P. A., Wang, J. X., Rowland, M., Genewein, T., Kurth-Nelson, Z., Pascanu, R., Heess, N., Veness, J., Pritzel, A., Sprechmann, P., et al. (2019) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 47

A General Theoretical Paradigm to Understand Learning from Human Preferences

Azar, M. G., Rowland, M., Piot, B., Guo, D., Calandriello, D., Valko, M., & Munos, R. (2023) · arXiv

Reinforcement Learning in RoboticsMachine Learning and Algorithms

Cited by 21

Olympiad-level formal mathematical reasoning with reinforcement learning

Hubert, T., Mehta, R., Sartran, L., Horváth, M. Z., Žužić, G., Wieser, E., Huang, A., Schrittwieser, J., Schroecker, Y., Masoom, H., et al. (2026) · Nature

Artificial Intelligence ApplicationsMathematics, Computing, and Information Processing

Cited by 19

Agency Is Frame-Dependent

Abel, D., Barreto, A., Bowling, M., Dabney, W., Dong, S., Hansen, S., Harutyunyan, A., Khetarpal, K., Lyle, C., Pascanu, R., et al. (2025) · arXiv

Philosophy of Biology and Scientific ExplanationEmbodied and Extended Cognition

Cited by 0