Legg, Shane

Human-level control through deep reinforcement learning

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 30,357

Deep reinforcement learning from human preferences

Christiano, P., Leike, J., Brown, T. B., Martic, M., Legg, S., & Amodei, D. (2017) · arXiv

Reinforcement Learning in RoboticsMachine Learning and Algorithms

Cited by 1,058

Universal intelligence: a definition of machine intelligence

Legg, S., & Hutter, M. (2007) · Minds and Machines

Psychiatry, Mental Health, NeuroscienceArtificial Intelligence Applications

Cited by 803

Levels of AGI for Operationalizing Progress on the Path to AGI

Morris, M. R., Sohl-Dickstein, J., Fiedel, N., Warkentin, T., Dafoe, A., Faust, A., Farabet, C., & Legg, S. (2023) · arXiv

Psychiatry, Mental Health, NeuroscienceArtificial Intelligence Applications

Cited by 274

AI Safety Gridworlds

Leike, J., Martic, M., Krakovna, V., Ortega, P. A., Everitt, T., Lefrancq, A., Orseau, L., & Legg, S. (2017) · arXiv

Reinforcement Learning in RoboticsEthics and Social Impacts of AI

Cited by 240

The Hydra Effect: Emergent Self-repair in Language Model Computations

McGrath, T., Rahtz, M., Kramar, J., Mikulik, V., & Legg, S. (2023) · arXiv

Explainable Artificial Intelligence (XAI)Topic Modeling

Cited by 140

Scalable agent alignment via reward modeling: a research direction

Leike, J., Krueger, D., Everitt, T., Martic, M., Maini, V., & Legg, S. (2018) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 121

Reward learning from human preferences and demonstrations in Atari

Ibarz, B., Leike, J., Pohlen, T., Irving, G., Legg, S., & Amodei, D. (2018) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 117

DeepMind Lab

Beattie, C., Leibo, J. Z., Teplyashin, D., Ward, T., Wainwright, M., Küttler, H., Lefrancq, A., Green, S., Valdés, V., Sadik, A., et al. (2016) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 68

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

Penalizing side effects using stepwise relative reachability

Krakovna, V., Orseau, L., Kumar, R., Martic, M., & Legg, S. (2018) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 26

Meta-trained agents implement Bayes-optimal agents

Mikulik, V., Delétang, G., McGrath, T., Genewein, T., Martic, M., Legg, S., & Ortega, P. A. (2020) · arXiv

Advanced Bandit Algorithms ResearchReinforcement Learning in Robotics

Cited by 24

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

Randomized Positional Encodings Boost Length Generalization of Transformers

Ruoss, A., Delétang, G., Genewein, T., Grau-Moya, J., Csordás, R., Bennani, M., Legg, S., & Veness, J. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1