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… (voir plus), 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.

2024-01-01

European Conference on Artificial Intelligence (publié)

Global Rewards in Multi-Agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems

Heiko Hoppe

Tobias Enders

Maximilian Schiffer

2023-12-14

ArXiv (prépublication)

Decision Diagrams in Space!

Isaac Rudich

Manuel L'opez-Ib'anez

Michael Romer

Louis-Martin Rousseau

2023-12-03

ArXiv (prépublication)

An Exact Framework for Solving the Space-Time Dependent TSP

Isaac Rudich

Manuel L'opez-Ib'anez

Michael Romer

Louis-Martin Rousseau

Many real-world scenarios involve solving bi-level optimization problems in which there is an outer discrete optimization problem, and an in… (voir plus)ner problem involving expensive or black-box computation. This arises in space-time dependent variants of the Traveling Salesman Problem, such as when planning space missions that visit multiple astronomical objects. Planning these missions presents significant challenges due to the constant relative motion of the objects involved. There is an outer combinatorial problem of finding the optimal order to visit the objects and an inner optimization problem that requires finding the optimal departure time and trajectory to travel between each pair of objects. The constant motion of the objects complicates the inner problem, making it computationally expensive. This paper introduces a novel framework utilizing decision diagrams (DDs) and a DD-based branch-and-bound technique, Peel-and-Bound, to achieve exact solutions for such bi-level optimization problems, assuming sufficient inner problem optimizer quality. The framework leverages problem-specific knowledge to expedite search processes and minimize the number of expensive evaluations required. As a case study, we apply this framework to the Asteroid Routing Problem (ARP), a benchmark problem in global trajectory optimization. Experimental results demonstrate the framework's scalability and ability to generate robust heuristic solutions for ARP instances. Many of these solutions are exact, contingent on the assumed quality of the inner problem's optimizer.

2023-12-03

ArXiv (prépublication)

An Exact Framework for Solving the Space-Time Dependent TSP

Isaac Rudich

Manuel L'opez-Ib'anez

Michael Romer

Louis-Martin Rousseau

2023-12-03

ArXiv (prépublication)

The Unsolved Challenges of LLMs as Generalist Web Agents: A Case Study

Rim Assouel

Tom Marty

Massimo Caccia

Issam Hadj Laradji

Alexandre Drouin

Sai Rajeswar

Hector Palacios

David Vazquez

Nicolas Chapados

Maxime Gasse

Alexandre Lacoste

2023-11-07

NeurIPS.cc/2023/Workshop/FMDM (publié)

openreview.net

MARCO: A Memory-Augmented Reinforcement Framework for Combinatorial Optimization

Andoni I. Garmendia

Josu Ceberio

Alexander Mendiburu

2023-08-19

Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence (publié)

Dynamic Routing and Wavelength Assignment with Reinforcement Learning

Peyman Kafaei

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… (voir plus)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.

2023-06-08

INFORMS Journal on Optimization (publié)

The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights

Maxime Gasse

Simon Bowly

Jonas Charfreitag

Laurent Charlin

Didier Chételat

Antonia Chmiela

Justin Dumouchelle

Ambros Gleixner

Aleksandr Kazachkov

Elias Boutros Khalil

Paweł Lichocki

Andrea Lodi

Miles Lubin

Chris J. Maddison

Christopher Morris

D. Papageorgiou

Augustin Parjadis

Sebastian Pokutta

Antoine Prouvost … (voir 22 de plus)

Lara Scavuzzo

Giulia Zarpellon

Linxin Yangm

Sha Lai

Akang Wang

Xiaodong Luo

Xiang Zhou

Haohan Huang

Sheng Cheng Shao

Yuanming Zhu

Dong Dong Zhang

Tao Manh Quan

Zixuan Cao

Yang Xu

Zhewei Huang

Shuchang Zhou

C. Binbin

He Minggui

Haoren Ren Hao

Zhang Zhiyu

An Zhiwu

Mao Kun

Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused … (voir plus)on solving problem instances in isolation, ignoring that they often stem from related data distributions in practice. However, recent years have seen a surge of interest in using machine learning as a new approach for solving combinatorial problems, either directly as solvers or by enhancing exact solvers. Based on this context, the ML4CO aims at improving state-of-the-art combinatorial optimization solvers by replacing key heuristic components. The competition featured three challenging tasks: finding the best feasible solution, producing the tightest optimality certificate, and giving an appropriate solver configuration. Three realistic datasets were considered: balanced item placement, workload apportionment, and maritime inventory routing. This last dataset was kept anonymous for the contestants.

2021-01-01

NeurIPS (Competition and Demos) (publié)