Schaul, Tom

Grandmaster level in StarCraft II using multi-agent reinforcement learning

Vinyals, O., Babuschkin, I., Czarnecki, W. M., Mathieu, M., Dudzik, A., Chung, J., Choi, D. H., Powell, R., Ewalds, T., Georgiev, P., et al. (2019) · Nature

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 3,455

Dueling Network Architectures for Deep Reinforcement Learning

Wang, Z., Schaul, T., Hessel, M., van Hasselt, H., Lanctot, M., & de Freitas, N. (2015) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 3,300

Prioritized Experience Replay

Schaul, T., Quan, J., Antonoglou, I., & Silver, D. (2015) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 3,120

Rainbow: Combining Improvements in Deep Reinforcement Learning

Hessel, M., Modayil, J., Van Hasselt, H., Schaul, T., Ostrovski, G., Dabney, W., Horgan, D., Piot, B., Azar, M., & Silver, D. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 1,846

Learning to learn by gradient descent by gradient descent

Andrychowicz, M., Denil, M., Gomez, S., Hoffman, M. W., Pfau, D., Schaul, T., Shillingford, B., & de Freitas, N. (2016) · arXiv

Stochastic Gradient Optimization TechniquesNeural Networks and Applications

Cited by 1,486

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

Ai for social good: unlocking the opportunity for positive impact

Tomašev, N., Cornebise, J., Hutter, F., Mohamed, S., Picciariello, A., Connelly, B., Belgrave, D. C. M., Ezer, D., Haert, F. C. V. D., Mugisha, F., et al. (2020) · Nature Communications

Ethics and Social Impacts of AIArtificial Intelligence Applications

Cited by 357

Unifying Count-Based Exploration and Intrinsic Motivation

Bellemare, M. G., Srinivasan, S., Ostrovski, G., Schaul, T., Saxton, D., & Munos, R. (2016) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 264

A video game description language for model-based or interactive learning

Schaul, T. (2013) · 2013 IEEE Conference on Computational Inteligence in Games (CIG)

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 214

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 69

Open-Endedness is Essential for Artificial Superhuman Intelligence

Hughes, E., Dennis, M., Parker-Holder, J., Behbahani, F., Mavalankar, A., Shi, Y., Schaul, T., & Rocktaschel, T. (2024) · arXiv

Psychiatry, Mental Health, NeuroscienceArtificial Intelligence Applications

Cited by 4

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 and History of ScienceEmbodied and Extended Cognition

Cited by 0