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

Keywords: Graph Neural Networks, Power Grids, MCTS, Hierarchical Methods, Action Space Pruning, Large Scale Systems

TL;DR: We present a novel algorithm for sequential decision making on graphs with large action spaces changing topologies.

Abstract: As real-world infrastructure systems become increasingly complex and large-scale, there is a growing need for learning-based control strategies 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.

Submission Number: 17