Lillicrap, Timothy

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

Continuous control with deep reinforcement learning

Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2015) · arXiv

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 10,782

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

Asynchronous Methods for Deep Reinforcement Learning

Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T. P., Harley, T., Silver, D., & Kavukcuoglu, K. (2016) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 7,132

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

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

A simple neural network module for relational reasoning

Santoro, A., Raposo, D., Barrett, D. G. T., Malinowski, M., Pascanu, R., Battaglia, P., & Lillicrap, T. (2017) · arXiv

Multimodal Machine Learning ApplicationsLogic, Reasoning, and Knowledge

Cited by 1,281

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

A deep learning framework for neuroscience

Richards, B. A., Lillicrap, T. P., Beaudoin, P., Bengio, Y., Bogacz, R., Christensen, A., Clopath, C., Costa, R. P., de Berker, A., Ganguli, S., et al. (2019) · Nature Neuroscience

Neural Networks and ApplicationsNeural dynamics and brain function

Cited by 1,027

Gemini: A Family of Highly Capable Multimodal Models

Gemini Team, Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al. (2023) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 990

Backpropagation and the brain

Lillicrap, T. P., Santoro, A., Marris, L., Akerman, C. J., & Hinton, G. (2020) · Nature Reviews Neuroscience

Neural Networks and ApplicationsNeural dynamics and brain function

Cited by 908

Why Copy Others? Insights from the Social Learning Strategies Tournament

Rendell, L., Boyd, R., Cownden, D., Enquist, M., Eriksson, K., Feldman, M. W., Fogarty, L., Ghirlanda, S., Lillicrap, T., & Laland, K. N. (2010) · Science

Language and cultural evolutionEvolutionary Game Theory and Cooperation

Cited by 811

Learning Latent Dynamics for Planning from Pixels

Hafner, D., Lillicrap, T., Fischer, I., Villegas, R., Ha, D., Lee, H., & Davidson, J. (2018) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 708

Dream to Control: Learning Behaviors by Latent Imagination

Hafner, D., Lillicrap, T., Ba, J., & Norouzi, M. (2019) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 558

Experience Replay for Continual Learning

Rolnick, D., Ahuja, A., Schwarz, J., Lillicrap, T. P., & Wayne, G. (2018) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 529

DeepMind Control Suite

Tassa, Y., Doron, Y., Muldal, A., Erez, T., Li, Y., Casas, D. D. L., Budden, D., Abdolmaleki, A., Merel, J., Lefrancq, A., et al. (2018) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 497

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Gemini Team, Georgiev, P., Lei, V. I., Burnell, R., Bai, L., Gulati, A., Tanzer, G., Vincent, D., Pan, Z., Wang, S., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 306

Deep Reinforcement Learning in Large Discrete Action Spaces

Dulac-Arnold, G., Evans, R., van Hasselt, H., Sunehag, P., Lillicrap, T., Hunt, J., Mann, T., Weber, T., Degris, T., & Coppin, B. (2015) · arXiv

Reinforcement Learning in RoboticsAdvanced Bandit Algorithms Research

Cited by 290

Mastering Atari with Discrete World Models

Hafner, D., Lillicrap, T., Norouzi, M., & Ba, J. (2020) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 247

Memory-based control with recurrent neural networks

Heess, N., Hunt, J. J., Lillicrap, T. P., & Silver, D. (2015) · arXiv

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 211

Compressive Transformers for Long-Range Sequence Modelling

Rae, J. W., Potapenko, A., Jayakumar, S. M., & Lillicrap, T. P. (2019) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 205

Mastering diverse control tasks through world models

Hafner, D., Pasukonis, J., Ba, J., & Lillicrap, T. (2025) · Nature

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 189