Lanctot, Marc

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

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

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

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

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

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

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

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