Portrait de Donna Vakalis

Donna Vakalis

Postdoctorat - UdeM
Superviseur⋅e principal⋅e
Yoshua Bengio
Co-supervisor
David Rolnick
Sujets de recherche
Apprentissage par renforcement
Apprentissage profond
Réseaux de neurones en graphes
Site web
Google Scholar
GitHub

Publications

Graph Dreamer: Temporal Graph World Models for Sample-Efficient and Generalisable Reinforcement Learning
Anaïs Berkes
Donna Vakalis
Yoshua Bengio
David Rolnick
2025-09-22
NeurIPS.cc/2025/Workshop/WiML (publié)
openreview.net
openreview.net
HVAC-GRACE: Transferable Building Control via Heterogeneous Graph Neural Network Policies
Anaïs Berkes
Donna Vakalis
David Rolnick
Yoshua Bengio
Buildings consume 40% of global energy, with HVAC systems responsible for up to half of that demand. As energy use grows, optimizing HVAC ef… (voir plus)ficiency is critical to meeting climate goals. While reinforcement learning (RL) offers a promising alternative to rule-based control, real-world adoption is limited by poor sample efficiency and generalisation. We introduce HVAC-GRACE, a graph-based RL framework that models buildings as heterogeneous graphs and integrates spatial message passing directly into temporal GRU gates. This enables each zone to learn control actions informed by both its own history and its structural context. Our architecture supports zero-shot transfer by learning topology-agnostic functions—but initial experiments reveal that this benefit depends on sufficient conditioned zone connectivity to maintain gradient flow. These findings highlight both the promise and the architectural requirements of scalable, transferable RL for building control
2025-07-01
ICML.cc/2025/Workshop/CO-BUILD (poster)
openreview.net
openreview.net