Portrait de Zina Kamel

Zina Kamel

Maîtrise recherche - McGill
Superviseur⋅e principal⋅e
David Meger
Co-supervisor
Hsiu-Chin Lin
Sujets de recherche
Apprentissage de représentations
Apprentissage par renforcement
Causalité
Robotique

Publications

Scalable Tree Search over Graphs with Learned Action Pruning for Power Grid Control
Florence Cloutier
Cyrus Neary
Adriana Hugessen
Viktor Todosijević
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