Monte Carlo Tree Search for Continuous and Stochastic Sequential Decision Making Problems
type: Dataset
description: 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.
issued: 2026-05-07T13:16:43.358029
language: en
modified: 2026-05-07T13:16:43.358035
contact point: Organization
  fn: CCSD
keyword: apprentissage-par-renforcement
keyword: energie
keyword: energy
keyword: infoeu-reposemanticsdoctoralthesis
keyword: infoinfo-dscomputer-science-csdata-structures-and-algorithms-csds
keyword: infoinfo-gtcomputer-science-cscomputer-science-and-game-theory-csgt
keyword: infoinfo-lgcomputer-science-csmachine-learning-cslg
keyword: optimisation
keyword: optimization
keyword: reinforcement-learning
keyword: theses
landing page: https://theses.hal.science/tel-00927252

Monte Carlo Tree Search for Continuous and Stochastic Sequential Decision Making Problems
type: Distribution
format: HTML
issued: 2026-05-07T13:16:43.365943
modified: 2026-05-07T13:16:43.340725
access URL: https://theses.hal.science/tel-00927252

test_moissonnage_selune
type: Agent