Pierre-Luc Bacon

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

Diego Calanzone

Stagiaire de recherche - UdeM

Maîtrise recherche - UdeM

Franciszek Czyzowicz

Stagiaire de recherche - UdeM

Pierluca D'Oro

Doctorat - UdeM

Co-superviseur⋅e :

Marc Gendron-Bellemare

Postdoctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Mahan Fathi

Maîtrise recherche - UdeM

Maîtrise recherche - UdeM

Clement Gehring

Postdoctorat - UdeM

Niki Howe

Doctorat - UdeM

Site web

Github

David Kanaa

Collaborateur·rice alumni

Lu Li

Collaborateur·rice de recherche - Université de Montréal

Michel Ma

Doctorat - UdeM

Github

Équations différentielles neuronales pour la régulation de la température dans les bâtiments dans le cadre de programmes de réponse à la demande

Sobhan Mohammadpour

Maîtrise recherche - UdeM

Aneri Muni

Doctorat - UdeM

Tianwei Ni

Doctorat - UdeM

Doctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Samy Rasmy

Stagiaire de recherche - UdeM

Github

Julien Roy

Doctorat - Polytechnique

Superviseur⋅e principal⋅e :

Postdoctorat - Polytechnique

Superviseur⋅e principal⋅e :

Stagiaire de recherche - UdeM

Billets de blogue

7 août 2024

par

Vincent Taboga

Clement Gehring

Mathieu Le Cam

Hanane Dagdougui

Pierre-Luc Bacon

Lire l'article

Direct Behavior Specification via Constrained Reinforcement Learning

31 août 2022

Spécification directe du comportement par apprentissage par renforcement sous contrainte

par

Julien Roy

Roger Girgis

Joshua Romoff

Pierre-Luc Bacon

Chris Pal

Lire l'article

Publications

Neural differential equations for temperature control in buildings under demand response programs

Vincent Taboga

Clement Gehring

Mathieu Le Cam

Hanane Dagdougui

2024-08-01

Applied Energy (publié)

Do Transformer World Models Give Better Policy Gradients?

Michel Ma

Tianwei Ni

Clement Gehring

Pierluca D'Oro

2024-07-08

Proceedings of the 41st International Conference on Machine Learning (publié)

Exploring Scaling Trends in LLM Robustness

Nikolaus H. R. Howe

Ian R. McKenzie

Oskar John Hollinsworth

Michał Zając

Tom Tseng

Aaron David Tucker

Adam Gleave

Language model capabilities predictably improve from scaling a model's size and training data. Motivated by this, increasingly large languag… (voir plus)e models have been trained, yielding an array of impressive capabilities. Yet these models are vulnerable to adversarial prompts, such as"jailbreaks"that hijack models to perform undesired behaviors, posing a significant risk of misuse. Prior work indicates that computer vision models become more robust with model and data scaling, raising the question: does language model robustness also improve with scale? We study this question empirically, finding that larger models respond substantially better to adversarial training, but there is little to no benefit from model scale in the absence of explicit defenses.

2024-06-28

ICML.cc/2024/Workshop/NextGenAISafety (poster)

Generative Active Learning for the Search of Small-molecule Protein Binders

Maksym Korablyov

Cheng-Hao Liu

Moksh J. Jain

Almer M. van der Sloot

Eric Jolicoeur

Edward Ruediger

Andrei Cristian Nica

Emmanuel Bengio

Kostiantyn Lapchevskyi

Daniel St-Cyr

Doris Alexandra Schuetz

Victor I Butoi

Jarrid Rector-Brooks

Simon R. Blackburn

Leo Feng

Hadi Nekoei

Sai Krishna Gottipati

Priyesh Vijayan

Prateek Gupta

Ladislav Rampášek … (voir 14 de plus)

Sasikanth Avancha

William L. Hamilton

Brooks Paige

Sanchit Misra

Stanisław Jastrzębski

Bharat Kaul

Doina Precup

Jos'e Miguel Hern'andez-Lobato

Marwin Segler

Michael M. Bronstein

Anne Marinier

Mike Tyers

Yoshua Bengio

Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exh… (voir plus)ibit a property of interest remains a significant challenge. We introduce LambdaZero, a generative active learning approach to search for synthesizable molecules. Powered by deep reinforcement learning, LambdaZero learns to search over the vast space of molecules to discover candidates with a desired property. We apply LambdaZero with molecular docking to design novel small molecules that inhibit the enzyme soluble Epoxide Hydrolase 2 (sEH), while enforcing constraints on synthesizability and drug-likeliness. LambdaZero provides an exponential speedup in terms of the number of calls to the expensive molecular docking oracle, and LambdaZero de novo designed molecules reach docking scores that would otherwise require the virtual screening of a hundred billion molecules. Importantly, LambdaZero discovers novel scaffolds of synthesizable, drug-like inhibitors for sEH. In in vitro experimental validation, a series of ligands from a generated quinazoline-based scaffold were synthesized, and the lead inhibitor N-(4,6-di(pyrrolidin-1-yl)quinazolin-2-yl)-N-methylbenzamide (UM0152893) displayed sub-micromolar enzyme inhibition of sEH.

2024-05-02

ArXiv (prépublication)

arxiv.org

Maxwell's Demon at Work: Efficient Pruning by Leveraging Saturation of Neurons

Simon Dufort-Labbé

Pierluca D'Oro

Evgenii Nikishin

Razvan Pascanu

Aristide Baratin

2024-03-12

ArXiv (prépublication)

arxiv.org

Bridging State and History Representations: Understanding Self-Predictive RL

Tianwei Ni

Benjamin Eysenbach

Erfan SeyedSalehi

Michel Ma

Clement Gehring

Aditya Mahajan

Representations are at the core of all deep reinforcement learning (RL) methods for both Markov decision processes (MDPs) and partially obse… (voir plus)rvable Markov decision processes (POMDPs). Many representation learning methods and theoretical frameworks have been developed to understand what constitutes an effective representation. However, the relationships between these methods and the shared properties among them remain unclear. In this paper, we show that many of these seemingly distinct methods and frameworks for state and history abstractions are, in fact, based on a common idea of self-predictive abstraction. Furthermore, we provide theoretical insights into the widely adopted objectives and optimization, such as the stop-gradient technique, in learning self-predictive representations. These findings together yield a minimalist algorithm to learn self-predictive representations for states and histories. We validate our theories by applying our algorithm to standard MDPs, MDPs with distractors, and POMDPs with sparse rewards. These findings culminate in a set of preliminary guidelines for RL practitioners.

2024-01-16

ICLR.cc/2024/Conference (poster)

Course Correcting Koopman Representations

Mahan Fathi

Clement Gehring

Jonathan Pilault

David Kanaa

Ross Goroshin

2024-01-16

ICLR.cc/2024/Conference (poster)

Decoupling regularization from the action space

Sobhan Mohammadpour

Emma Frejinger

Regularized reinforcement learning (RL), particularly the entropy-regularized kind, has gained traction in optimal control and inverse RL. W… (voir plus)hile standard unregularized RL methods remain unaffected by changes in the number of actions, we show that it can severely impact their regularized counterparts. This paper demonstrates the importance of decoupling the regularizer from the action space: that is, to maintain a consistent level of regularization regardless of how many actions are involved to avoid over-regularization. Whereas the problem can be avoided by introducing a task-specific temperature parameter, it is often undesirable and cannot solve the problem when action spaces are state-dependent. In the state-dependent action context, different states with varying action spaces are regularized inconsistently. We introduce two solutions: a static temperature selection approach and a dynamic counterpart, universally applicable where this problem arises. Implementing these changes improves performance on the DeepMind control suite in static and dynamic temperature regimes and a biological design task.

2024-01-16

ICLR.cc/2024/Conference (poster)

Decoupling regularization from the action space

Sobhan Mohammadpour

Emma Frejinger

2024-01-16

ICLR.cc/2024/Conference (poster)

Motif: Intrinsic Motivation from Artificial Intelligence Feedback

Martin Klissarov

Pierluca D'Oro

Shagun Sodhani

Roberta Raileanu

Pascal Vincent

Amy Zhang

Mikael Henaff

Exploring rich environments and evaluating one's actions without prior knowledge is immensely challenging. In this paper, we propose Motif, … (voir plus)a general method to interface such prior knowledge from a Large Language Model (LLM) with an agent. Motif is based on the idea of grounding LLMs for decision-making without requiring them to interact with the environment: it elicits preferences from an LLM over pairs of captions to construct an intrinsic reward, which is then used to train agents with reinforcement learning. We evaluate Motif's performance and behavior on the challenging, open-ended and procedurally-generated NetHack game. Surprisingly, by only learning to maximize its intrinsic reward, Motif achieves a higher game score than an algorithm directly trained to maximize the score itself. When combining Motif's intrinsic reward with the environment reward, our method significantly outperforms existing approaches and makes progress on tasks where no advancements have ever been made without demonstrations. Finally, we show that Motif mostly generates intuitive human-aligned behaviors which can be steered easily through prompt modifications, while scaling well with the LLM size and the amount of information given in the prompt.

2024-01-16

ICLR.cc/2024/Conference (poster)

Maximum entropy GFlowNets with soft Q-learning

Sobhan Mohammadpour

Emmanuel Bengio

Emma Frejinger

2024-01-01

AISTATS (publié)