Quentin Cappart

Affiliate Member

Associate Professor, Polytechnique Montréal, Department of Computer Engineering and Software Engineering

Research Topics

Graph Neural Networks

Learning on Graphs

Reasoning

Website

Google Scholar

Biography

Quentin Cappart is an associate professor in the Department of Computer and Software Engineering at Polytechnique Montréal and an Affiliate member at Mila. He leads the CORAIL research group, which he co-founded with Louis-Martin Rousseau. Cappart obtained a BSc in engineering (2012), a MSc in computer engineering (2014), a MSc in management (2018) and a PhD (2017) at the Université catholique de Louvain (Belgium).

After his PhD, he joined Polytechnique Montréal and the International Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT) as a postdoctoral fellow (2018–2020). During these two years, he was also a research intern at ElementAI. Cappart’s main research area is the integration of machine learning with search procedures for solving combinatorial problems.

Current Students

Léo Boisvert

PhD - Polytechnique Montréal

Principal supervisor :

Alexandre Drouin

Francesco Morri

Postdoctorate - Polytechnique Montréal

Principal supervisor :

Publications

WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?

Alexandre Drouin

Maxime Gasse

Massimo Caccia

Issam H. Laradji

Alexandre Lacoste

We study the use of large language model-based agents for interacting with software via web browsers. Unlike prior work, we focus on measuri… (see more)ng the agents' ability to perform tasks that span the typical daily work of knowledge workers utilizing enterprise software systems. To this end, we propose WorkArena, a remote-hosted benchmark of 33 tasks based on the widely-used ServiceNow platform. We also introduce BrowserGym, an environment for the design and evaluation of such agents, offering a rich set of actions as well as multimodal observations. Our empirical evaluation reveals that while current agents show promise on WorkArena, there remains a considerable gap towards achieving full task automation. Notably, our analysis uncovers a significant performance disparity between open and closed-source LLMs, highlighting a critical area for future exploration and development in the field.

2024-07-22

International Conference on Machine Learning (Accept (Poster))

doi.org

proceedings.mlr.press

Global rewards in multi-agent deep reinforcement learning for autonomous mobility on demand systems

Heiko Hoppe

Tobias Enders

Quentin Cappart

Maximilian Schiffer

We study vehicle dispatching in autonomous mobility on demand (AMoD) systems, where a central operator assigns vehicles to customer requests… (see more) or rejects these with the aim of maximizing its total profit. Recent approaches use multi-agent deep reinforcement learning (MADRL) to realize scalable yet performant algorithms, but train agents based on local rewards, which distorts the reward signal with respect to the system-wide profit, leading to lower performance. We therefore propose a novel global-rewards-based MADRL algorithm for vehicle dispatching in AMoD systems, which resolves so far existing goal conflicts between the trained agents and the operator by assigning rewards to agents leveraging a counterfactual baseline. Our algorithm shows statistically significant improvements across various settings on real-world data compared to state-of-the-art MADRL algorithms with local rewards. We further provide a structural analysis which shows that the utilization of global rewards can improve implicit vehicle balancing and demand forecasting abilities. An extended version of our paper, including an appendix, can be found at https://arxiv.org/abs/2312.08884. Our code is available at https://github.com/tumBAIS/GR-MADRL-AMoD.

2024-06-10

Proceedings of the 6th Annual Learning for Dynamics & Control Conference (published)

doi.org

proceedings.mlr.press

Towards a Generic Representation of Combinatorial Problems for Learning-Based Approaches

Léo Boisvert

Hélène Verhaeghe

Quentin Cappart

2024-05-24

Integration of Constraint Programming, Artificial Intelligence, and Operations Research (published)

doi.org

arxiv.org

Deep Learning for Data-Driven Districting-and-Routing

Arthur Ferraz

Cheikh Ahmed

Quentin Cappart

Thibaut Vidal

2024-02-07

ArXiv (preprint)

arxiv.org

Dynamic Routing and Wavelength Assignment with Reinforcement Learning.

Peyman Kafaei

Quentin Cappart

Nicolas Chapados

Hamed Pouya

Louis-Martin Rousseau

With the rapid developments in communication systems, and considering their dynamic nature, all-optical networks are becoming increasingly c… (see more)omplex. This study proposes a novel method based on deep reinforcement learning for the routing and wavelength assignment problem in all-optical wavelength-decision-multiplexing networks. We consider dynamic incoming requests, in which their arrival and holding times are not known in advance. The objective is to devise a strategy that minimizes the number of rejected packages due to the lack of resources in the long term. We use graph neural networks to capture crucial latent information from the graph-structured input to develop the optimal strategy. The proposed deep reinforcement learning algorithm selects a route and a wavelength simultaneously for each incoming traffic connection as they arrive. The results demonstrate that the learned agent outperforms the methods used in practice and can be generalized on network topologies that did not participate in training.

2023-12-31

INFORMS Journal on Optimization (published)

doi.org

An Improved Neuro-Symbolic Architecture to Fine-Tune Generative AI Systems

Chao Yin

Quentin Cappart

Gilles Pesant

2023-12-31

Integration of AI and OR Techniques in Constraint Programming (published)

doi.org

Learning Lagrangian Multipliers for the Travelling Salesman Problem

Augustin Parjadis

Quentin Cappart

Bistra Dilkina

Aaron M. Ferber

Louis-Martin Rousseau

2023-12-31

CP (published)

doi.org

arxiv.org

Learning Precedences for Scheduling Problems with Graph Neural Networks

Hélène Verhaeghe

Quentin Cappart

Gilles Pesant

Claude-Guy Quimper

2023-12-31

CP (published)

doi.org

Winning the 2023 CityLearn Challenge: A Community-Based Hierarchical Energy Systems Coordination Algorithm

Andoni I. Garmendia

Francesco Morri

Quentin Cappart

Hélène Le Cadre

. The effective management and control of building energy systems are crucial for reducing the energy consumption peak loads, CO 2 emissions… (see more), and ensuring the stability of the power grid, while maintaining optimal comfort levels within buildings. The difﬁculty to accommodate this trade-off is ampliﬁed by dynamic environmental conditions and the need for scalable solutions that can adapt across various building types and geographic locations. Acknowledging the importance of this problem, NeurIPS conference hosted since 2020 the CityLearn control challenge to foster the design of innovative solutions in building energy management. Participants were tasked with developing strategies that not only enhance energy efﬁciency but also prioritize sustainability and occupant comfort. This paper introduces the Community-based Hierarchical Energy Systems Co-ordination Algorithm ( CHESCA ), the winning approach of the 2023 edition. We rely on a hierarchical approach adaptable to an arbitrary number of buildings, ﬁrst optimizing building-level metrics individually, and later reﬁning these through a central community-level controller to improve grid-related metrics. Compared to the other high-ranked competitors, our approach demonstrated fast inference capabilities like learning-based methods, while offering a better interpretability and a superior generalization capabilities with minimal data requirements. This paper details our approach, supported by comprehensive experimental results and ablation studies.

2023-12-31

ECAI (published)

doi.org

Decision Diagrams in Space!

Isaac Rudich

Quentin Cappart

Manuel L'opez-Ib'anez

Michael Romer

Louis-Martin Rousseau

2023-12-02

ArXiv (preprint)

arxiv.org