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Florence Cloutier

Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e
Glen Berseth
Sujets de recherche
Apprentissage automatique appliqué
Apprentissage de représentations
Apprentissage par renforcement
Apprentissage profond
Apprentissage sur graphes
IA et durabilité
IA pour le changement climatique
Optimisation
Réseaux de neurones en graphes
Réseaux électriques
Robotique
Théorie de l'apprentissage automatique
Site web
Google Scholar
GitHub

Publications

Scalable Tree Search over Graphs with Learned Action Pruning for Power Grid Control
Florence Cloutier
Cyrus Neary
Adriana Hugessen
Viktor Todosijevic
Zina Kamel
Glen Berseth
As real-world infrastructure systems become increasingly complex and large-scale, there is a growing need for learning-based control strateg… (voir plus)ies that can make informed decisions in complex and dynamic environments. However, large-scale problems — such as power grid control — introduce high-dimensional action spaces and necessitate transferability across varying grid topologies. We introduce **H**ierarchical **E**xpert-Guided **R**econfiguration **O**ptimization for **G**raph **T**opologies, **HERO-GT**, a model-based planning approach that combines a pretrained graph neural network (GNN) for topology-aware action pruning with a Monte Carlo Tree Search (MCTS) planner for targeted, structured exploration. More specifically, the high-level GNN predicts a promising subset of actions, which the low-level MCTS agent uses to focus its search and reduce computational overhead while remaining adaptable to unseen graph structures. Furthermore, the MCTS planner leverages a given *default policy*---which may be defined, for example, by heuristics, problem relaxations, or rule-based methods---to bias the search and prioritize actions that are expected to improve performance over the default. We deploy HERO-GT in power grid environments, demonstrating that it not only improves over a strong default policy, but also scales to a realistic operational setting where exhaustive search becomes computationally infeasible.
2025-06-17
rl-conference.cc/RLC/2025/Workshop/RL4RS (publié)
openreview.net
openreview.net
Deep learning of chest X-rays can predict mechanical ventilation outcome in ICU-admitted COVID-19 patients
Daniel Gourdeau
Olivier Potvin
Jason Henry Biem
Florence Cloutier
Lyna Abrougui
Patrick Archambault
Carl Chartrand-Lefebvre
Louis Dieumegarde
Christian Gagné
Louis Gagnon
Raphaelle Giguère
Alexandre Hains
Huy Le
Simon Lemieux
Marie-Hélène Lévesque
Simon Nepveu
Lorne Rosenbloom
An Tang
Issac Yang
Nathalie Duchesne … (voir 1 de plus)
Simon Duchesne
2022-04-13
Scientific Reports (publié)
doi.org