Papers
3
Total Citations
89
H-Index
3
About
Gabriel Synnaeve is a leading researcher in artificial intelligence, with a primary focus on game AI, deep reinforcement learning, and natural language processing. His pioneering work in real-time strategy (RTS) games, particularly StarCraft, has been foundational to the field. Synnaeve's major contributions include developing a Bayesian model for RTS unit control, which enabled artificial agents to manage complex micro-management tasks while reasoning about high-level strategy—a paper that has garnered 56 citations. He also created one of the first comprehensive datasets of full StarCraft game states, allowing researchers to analyze tactics and strategy beyond simple player commands, with over 33 combined citations. This dataset became a critical resource for advancing AI in complex, multi-agent environments. Beyond gaming, Synnaeve has made significant impacts in deep learning for NLP and code generation, notably contributing to the development of large language models at Meta AI. His work bridges the gap between strategic reasoning and practical AI deployment, inspiring a generation of researchers to tackle hierarchical decision-making problems.
Research Focus
Key Achievements
Top Papers
- 1A Bayesian model for RTS units control applied to StarCraft56 citations · 2011
- 2A Dataset for StarCraft AI \& an Example of Armies Clustering25 citations · 2012
- 3A Dataset for StarCraft AI and an Example of Armies Clustering8 citations · 2012