Marc Lanctot
Google (United States)(US)DeepMind (United Kingdom)(GB)
Publications by Year
Research Areas
Artificial Intelligence in Games, Reinforcement Learning in Robotics, Sports Analytics and Performance, Advanced Bandit Algorithms Research, Game Theory and Applications
Most-Cited Works
- → Mastering the game of Go with deep neural networks and tree search(2016)15,568 cited
- → A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play(2018)3,450 cited
- → Dueling Network Architectures for Deep Reinforcement Learning(2015)1,813 cited
- → Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm(2017)1,079 cited
- → Deep Q-learning From Demonstrations(2018)795 cited
- → Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward(2018)486 cited
- → Multi-agent Reinforcement Learning in Sequential Social Dilemmas(2017)274 cited
- → The Hanabi challenge: A new frontier for AI research(2019)233 cited
- → Monte Carlo Sampling for Regret Minimization in Extensive Games(2009)190 cited