Portrait de Cyrus Neary

Cyrus Neary

Postdoctorat - UdeM
Superviseur⋅e principal⋅e
Glen Berseth
Co-supervisor
Yoshua Bengio
Sujets de recherche
Apprentissage automatique scientifique
Apprentissage par renforcement
Apprentissage profond
IA appliquée
IA digne de confiance
Méthodes formelles
Modèles de fondation
Robotique
Systèmes dynamiques
Systèmes multi-agents
Site web
Google Scholar
GitHub

Publications

Zero-Shot Constraint Satisfaction with Forward- Backward Representations
Adriana Hugessen
Harley Wiltzer
Cyrus Neary
Amy Zhang
Glen Berseth
Traditionally, constrained policy optimization with Reinforcement Learning (RL) requires learning a new policy from scratch for any new envi… (voir plus)ronment, goal or cost function, with limited generalization to new tasks and constraints. Given the sample inefficiency of many common deep RL methods, this procedure can be impractical for many real-world scenarios, particularly when constraints or tasks are changing. As an alternative, in the unconstrained setting, various works have sought to pre-train representations from offline datasets to accelerate policy optimization upon specification of a reward. Such methods can permit faster adaptation to new tasks in a given environment, dramatically improving sample efficiency. Recently, zero-shot policy optimization has been explored by leveraging a particular
2025-07-01
rl-conference.cc/RLC/2025/Workshop/RLBrew (publié)
openreview.net
openreview.net
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