Graepel, Thore

Mastering the game of Go with deep neural networks and tree search

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature

Artificial Intelligence in GamesArtificial Intelligence Applications

Cited by 15,612

Mastering the game of Go without human knowledge

Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al. (2017) · Nature

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 9,084

A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play

Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al. (2018) · Science

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 3,562

Private traits and attributes are predictable from digital records of human behavior

Kosinski, M., Stillwell, D., & Graepel, T. (2013) · Proceedings of the National Academy of Sciences

Privacy, Security, and Data ProtectionAuthorship Attribution and Profiling

Cited by 2,829

Mastering Atari, Go, chess and shogi by planning with a learned model

Schrittwieser, J., Antonoglou, I., Hubert, T., Simonyan, K., Sifre, L., Schmitt, S., Guez, A., Lockhart, E., Hassabis, D., Graepel, T., et al. (2020) · Nature

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 1,214

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Schrittwieser, J., Antonoglou, I., Hubert, T., Simonyan, K., Sifre, L., Schmitt, S., Guez, A., Lockhart, E., Hassabis, D., Graepel, T., et al. (2019) · arXiv

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 1,214

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al. (2017) · arXiv

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 1,102

Human-level performance in 3D multiplayer games with population-based reinforcement learning

Jaderberg, M., Czarnecki, W. M., Dunning, I., Marris, L., Lever, G., Castañeda, A. G., Beattie, C., Rabinowitz, N. C., Morcos, A. S., Ruderman, A., et al. (2019) · Science

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 662

ML Confidential: Machine Learning on Encrypted Data

Graepel, T., Lauter, K., & Naehrig, M. (2013) · Lecture Notes in Computer Science

Cryptography and Data SecurityPrivacy-Preserving Technologies in Data

Cited by 423

Personality and patterns of Facebook usage

Bachrach, Y., Kosinski, M., Graepel, T., Kohli, P., & Stillwell, D. (2012) · Proceedings of the 4th Annual ACM Web Science Conference

Personality Traits and PsychologyPrivacy, Security, and Data Protection

Cited by 380

A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning

Lanctot, M., Zambaldi, V., Gruslys, A., Lazaridou, A., Tuyls, K., Perolat, J., Silver, D., & Graepel, T. (2017) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 295

Cooperative AI: machines must learn to find common ground

Dafoe, A., Bachrach, Y., Hadfield, G., Horvitz, E., Larson, K., & Graepel, T. (2021) · Nature

Ethics and Social Impacts of AIMulti-Agent Systems and Negotiation

Cited by 232

Inequity aversion improves cooperation in intertemporal social dilemmas

Hughes, E., Leibo, J. Z., Phillips, M. G., Tuyls, K., Duéñez-Guzmán, E. A., Castañeda, A. G., Dunning, I., Zhu, T., McKee, K. R., Koster, R., et al. (2018) · arXiv

Evolutionary Game Theory and CooperationExperimental Behavioral Economics Studies

Cited by 134

A multi-agent reinforcement learning model of common-pool resource appropriation

Perolat, J., Leibo, J. Z., Zambaldi, V., Beattie, C., Tuyls, K., & Graepel, T. (2017) · arXiv

Experimental Behavioral Economics StudiesEvolutionary Game Theory and Cooperation

Cited by 109

Open Problems in Cooperative AI

Dafoe, A., Hughes, E., Bachrach, Y., Collins, T., McKee, K. R., Leibo, J. Z., Larson, K., & Graepel, T. (2020) · arXiv

Multi-Agent Systems and NegotiationGame Theory and Voting Systems

Cited by 93

From motor control to team play in simulated humanoid football

Liu, S., Lever, G., Wang, Z., Merel, J., Eslami, S. M. A., Hennes, D., Czarnecki, W. M., Tassa, Y., Omidshafiei, S., Abdolmaleki, A., et al. (2022) · Science Robotics

Reinforcement Learning in RoboticsHuman Motion and Animation

Cited by 88

Open-ended Learning in Symmetric Zero-sum Games

Balduzzi, D., Garnelo, M., Bachrach, Y., Czarnecki, W. M., Perolat, J., Jaderberg, M., & Graepel, T. (2019) · arXiv

Artificial Intelligence in GamesEvolutionary Algorithms and Applications

Cited by 88

Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research

Leibo, J. Z., Hughes, E., Lanctot, M., & Graepel, T. (2019) · arXiv

Complex Systems and DynamicsMulti-Agent Systems and Negotiation

Cited by 60

Biases for Emergent Communication in Multi-agent Reinforcement Learning

Eccles, T., Bachrach, Y., Lever, G., Lazaridou, A., & Graepel, T. (2019) · arXiv

Reinforcement Learning in RoboticsLanguage and cultural evolution

Cited by 42

Multi-agent Reinforcement Learning in Sequential Social Dilemmas

Leibo, J. Z., Zambaldi, V., Lanctot, M., Marecki, J., & Graepel, T. (2017) · International Joint Conference on Autonomous Agents and Multiagent Systems

Reinforcement Learning in RoboticsEvolutionary Game Theory and Cooperation

Cited by 35

Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot

Leibo, J. Z., Duéñez-Guzmán, E., Vezhnevets, A. S., Agapiou, J. P., Sunehag, P., Koster, R., Matyas, J., Beattie, C., Mordatch, I., & Graepel, T. (2021) · arXiv

Reinforcement Learning in RoboticsMulti-Agent Systems and Negotiation

Cited by 31

PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis

Wu, Y., Wershof, E., Schmon, S. M., Nassar, M., Osiński, B., Eksi, R., Yan, Z., Stark, R., Zhang, K., & Graepel, T. (2024) · arXiv

Single-cell and spatial transcriptomicsGene Regulatory Network Analysis

Cited by 20

PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis

Wu, Y., Wershof, E., Schmon, S., Nassar, M., Osiński, B., Eksi, R., Yan, Z., Stark, R., Zhang, K., & Graepel, T. (2025) · Advances in Neural Information Processing Systems 38

Single-cell and spatial transcriptomicsGene Regulatory Network Analysis

Cited by 20

A multi-agent reinforcement learning model of reputation and cooperation in human groups

McKee, K. R., Hughes, E., Zhu, T. O., Chadwick, M. J., Koster, R., Castaneda, A. G., Beattie, C., Graepel, T., Botvinick, M., & Leibo, J. Z. (2021) · arXiv

Evolutionary Game Theory and CooperationExperimental Behavioral Economics Studies

Cited by 5