Professor, Computer Science, LAMSADE, University Paris-Dauphine, PSL (Sep. 2008-). Editor in Chief: International Computer Games Association Journal (2017-).
Topics of interest
Artificial Intelligence, Monte Carlo Search, Deep Learning, Games, Optimization
Project in Prairie
Tristan Cazenave will address Monte Carlo Search and Deep Reinforcement Learning for games and optimization problems. He will act as the director of the IASD research master in Artificial Intelligence of PSL.
My current research topics are:
• Tailor the Monte Carlo search to the problem being learned. For example for single player games a Nested Monte Carlo search might learn better than the standard PUCT search. Similarly for incomplete information problems PUCT might not be the best algorithm.
• Accelerate Alpha Zero type Deep Reinforcement Learning. The current algorithm requires huge computations. Finding ways to learn faster is important.
• Apply a combination of Monte Carlo search and Deep Learning to various
• Explore various network architectures. For example residual networks enable to train deeper net-works faster and with better results. Layers such as Average Spatial Pooling improve much the quality of value networks. New network architectures may improve substantially the level of play.