In this thesis, I studied sequential decision making problems, with a focus on the unit commitment problem. Traditionnaly solved by dynamic programming methods, this problem is still a challenge, due to its high dimension and to the sacrifices made on the accuracy of the model to apply state of the art methods. I investigated on the applicability of Monte Carlo Tree Search methods for this problem, and other problems that are single player, stochastic and continuous sequential decision making problems. In doing so, I obtained a consistent and anytime algorithm, that can easily be combined with existing strong heuristic solvers.
Monte Carlo Tree Search for Continuous and Stochastic Sequential Decision Making Problems