Portrait de Pierre-Luc Bacon

Pierre-Luc Bacon

Membre académique principal
pierre-luc.bacon@mila.quebec
Chaire en IA Canada-CIFAR
Professeur adjoint, Université de Montréal, Département d'informatique et de recherche opérationnelle
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Biographie

Pierre-Luc Bacon est professeur agrégé au Département d'informatique et de recherche opérationnelle de l'Université de Montréal. Il est également membre de Mila – Institut québécois d’intelligence artificielle et d’IVADO et titulaire d'une chaire Facebook-CIFAR. Il dirige un groupe de recherche qui travaille sur le défi posé par la malédiction de l'horizon dans l'apprentissage par renforcement et le contrôle optimal.

Étudiants actuels

Aneri Muni
Doctorat - Université de Montréal
aneri.muni@mila.quebec
Anushree Rankawat
Doctorat - Université de Montréal
anushree.rankawat@mila.quebec
Arielle Gazzé
Stagiaire de recherche - Université de Montréal
arielle.gazze@mila.quebec
Clement Gehring
Postdoctorat - Université de Montréal
clement.gehring@mila.quebec
David Kanaa
Collaborateur·rice alumni
kanaadjs@mila.quebec
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Diego Calanzone
Collaborateur·rice de recherche - University of Trento
diego.calanzone@mila.quebec
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Esther Derman
Postdoctorat - Université de Montréal
Co-superviseur⋅e :
Érick Delage
esther.derman@mila.quebec
Evgenii Nikishin
Doctorat - Université de Montréal
Co-superviseur⋅e :
Aaron Courville
evgenii.nikishin@mila.quebec
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Github
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Julien Roy
Doctorat - Polytechnique Montréal
Superviseur⋅e principal⋅e :
Chris Pal
royjulie@mila.quebec
Github
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Justin Veilleux
Stagiaire de recherche - Université de Montréal
justin.veilleux@mila.quebec
Léa Côté-Turcotte
Maîtrise recherche - Université de Montréal
lea.cote-turcotte@mila.quebec
Mahan Fathi
Maîtrise recherche - Université de Montréal
mahan.fathi@mila.quebec
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Michel Ma
Doctorat - Université de Montréal
michel.ma@mila.quebec
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Niki Howe
Doctorat - Université de Montréal
howeniko@mila.quebec
Site web
Github
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Pierluca D'Oro
Doctorat - Université de Montréal
Co-superviseur⋅e :
Marc Gendron-Bellemare
pierluca.doro@mila.quebec
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Github
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Samy Rasmy
Stagiaire de recherche - Université de Montréal
samy.rasmy@mila.quebec
Github
Sobhan Mohammadpour
Maîtrise recherche - Université de Montréal
sobhan.mohammadpour@mila.quebec
Tianwei Ni
Doctorat - Université de Montréal
tianwei.ni@mila.quebec
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Vincent Taboga
Doctorat - Polytechnique Montréal
Superviseur⋅e principal⋅e :
Hanane Dagdougui
vincent.taboga@mila.quebec
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Publications

Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Design
Julien Roy
Pierre-Luc Bacon
Chris Pal
Emmanuel Bengio
In recent years, in-silico molecular design has received much attention from the machine learning community. When designing a new compound f… (voir plus)or pharmaceutical applications, there are usually multiple properties of such molecules that need to be optimised: binding energy to the target, synthesizability, toxicity, EC50, and so on. While previous approaches have employed a scalarization scheme to turn the multi-objective problem into a preference-conditioned single objective, it has been established that this kind of reduction may produce solutions that tend to slide towards the extreme points of the objective space when presented with a problem that exhibits a concave Pareto front. In this work we experiment with an alternative formulation of goal-conditioned molecular generation to obtain a more controllable conditional model that can uniformly explore solutions along the entire Pareto front.
2023-06-23
ICML.cc/2023/Workshop/DeployableGenerativeAI (publié)
doi.org
openreview.net
Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier
Pierluca D'Oro
Max Schwarzer
Evgenii Nikishin
Pierre-Luc Bacon
Marc Gendron-Bellemare
Aaron Courville
Increasing the replay ratio, the number of updates of an agent's parameters per environment interaction, is an appealing strategy for improv… (voir plus)ing the sample efficiency of deep reinforcement learning algorithms. In this work, we show that fully or partially resetting the parameters of deep reinforcement learning agents causes better replay ratio scaling capabilities to emerge. We push the limits of the sample efficiency of carefully-modified algorithms by training them using an order of magnitude more updates than usual, significantly improving their performance in the Atari 100k and DeepMind Control Suite benchmarks. We then provide an analysis of the design choices required for favorable replay ratio scaling to be possible and discuss inherent limits and tradeoffs.
2023-02-01
ICLR.cc/2023/Conference (notable)
openreview.net
openreview.net
Block-State Transformers
Mahan Fathi
Jonathan Pilault
Pierre-Luc Bacon
Chris Pal
Orhan Firat
Ross Goroshin
2023-01-01
NeurIPS (publié)
arxiv.org
arxiv.org
Options of Interest: Temporal Abstraction with Interest Functions
Khimya Khetarpal
Martin Klissarov
Maxime Chevalier-Boisvert
Pierre-Luc Bacon
Doina Precup
Temporal abstraction refers to the ability of an agent to use behaviours of controllers which act for a limited, variable amount of time. Th… (voir plus)e options framework describes such behaviours as consisting of a subset of states in which they can initiate, an internal policy and a stochastic termination condition. However, much of the subsequent work on option discovery has ignored the initiation set, because of difficulty in learning it from data. We provide a generalization of initiation sets suitable for general function approximation, by defining an interest function associated with an option. We derive a gradient-based learning algorithm for interest functions, leading to a new interest-option-critic architecture. We investigate how interest functions can be leveraged to learn interpretable and reusable temporal abstractions. We demonstrate the efficacy of the proposed approach through quantitative and qualitative results, in both discrete and continuous environments.
2020-04-03
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
arxiv.org