Pierre-Luc Bacon

Biography

Pierre-Luc Bacon is an assistant professor at Université de Montréal in the Department of Computer Science and Operations Research (DIRO). He is also a core academic member of Mila – Quebec Artificial Intelligence Institute and IVADO, and holds a Facebook CIFAR AI Chair. Bacon leads a research group that investigates the challenges posed by the curse of the horizon in reinforcement learning and optimal control.

Current Students

Mahdieh Adib

Collaborating researcher - Concordia University

mahdieh.adib@mila.quebec

claire.bouillet@mila.quebec

Reza Alvandi

Research Intern - McGill University

reza.alvandi@mila.quebec

Pablo Boitel

Collaborating researcher - ÉTS

pablo.boitel@mila.quebec

Claire Bouillet

Research Intern - Polytechnique Montréal

Diego Calanzone

PhD - Université de Montréal

diego.calanzone@mila.quebec

PhD - Université de Montréal

Co-supervisor :

Emma Frejinger

florence.cloutier@mila.quebec

Kamen Damov

Professional Master's - Université de Montréal

kamen.damov@mila.quebec

esther.derman@mila.quebec

Esther Derman

Collaborating Alumni - Université de Montréal

Co-supervisor :

Erick Delage

Master's Research - Polytechnique Montréal

Principal supervisor :

Hanane Dagdougui

giorgi.gamkrelidze@mila.quebec

Arielle Gazzé

Master's Research - Université de Montréal

arielle.gazze@mila.quebec

Adrien Goldszal

Collaborating researcher - Université de Montréal

adrien.goldszal@polytechnique.edu

Niki Howe

PhD - Université de Montréal

Austin Jang

PhD - Université de Montréal

doseok.jang@mila.quebec

akash.karthikeyan@mila.quebec

Akash Karthikeyan

PhD - Université de Montréal

Postdoctorate - McGill University

Principal supervisor :

Aditya Mahajan

gaspard.lambrechts@mila.quebec

Master's Research - Université de Montréal

Principal supervisor :

Yashar Hezaveh

parker.levesque@mila.quebec

Lu Li

PhD - Université de Montréal

Michel Ma

PhD - Université de Montréal

michel.ma@mila.quebec

artiom.matvei@mila.quebec

Artiom Matvei

Master's Research - Université de Montréal

Aneri Muni

PhD - Université de Montréal

aneri.muni@mila.quebec

Vamsi Krishna Munjuluri V S

Master's Research - Université de Montréal

mvkrishna2001@gmail.com

Tianwei Ni

PhD - Université de Montréal

tianwei.ni@mila.quebec

PhD - Université de Montréal

anushree.rankawat@mila.quebec

Gabriel Sasseville

PhD - Université de Montréal

gabriel.sasseville@mila.quebec

Postdoctorate - Université de Montréal

borna.sayedana@mila.quebec

vincent.taboga@mila.quebec

Yihao Sun

PhD - Université de Montréal

yihao.sun@mila.quebec

Postdoctorate - Polytechnique Montréal

Postdoctorate - Université de Montréal

Principal supervisor :

Yashar Hezaveh

wassim.tenachi@mila.quebec

justin.veilleux@mila.quebec

Justin Veilleux

Master's Research - Université de Montréal

Blog Posts

Diagram of the physics informed AI system

February 26, 2026

Refrigerants are warming up the planet. AI can help.

Adrien Goldszal

Pierre-Luc Bacon

Read the article

July 31, 2024

Neural Differential Equations for Temperature Control in Buildings Under Demand Response Programs

Vincent Taboga

Clement Gehring

Mathieu Le Cam

Hanane Dagdougui

Pierre-Luc Bacon

Read the article

August 31, 2022

Direct Behavior Specification via Constrained Reinforcement Learning

Julien Roy

Roger Girgis

Joshua Romoff

Pierre-Luc Bacon

Chris Pal

Read the article

Publications

Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Design

Julien Roy

Christopher 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… (see more)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.

2023-06-22

ICML.cc/2023/Workshop/DeployableGenerativeAI (accepted)

Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier

Bellemare Marc-Emmanuel

Aaron Courville

2023-04-30

International Conference on Learning Representations (Accept: notable-top-5%)

Myriad: a real-world testbed to bridge trajectory optimization and deep learning

Nikolaus H. R. Howe

Simon Dufort-Labbé

Nitarshan Rajkumar

We present Myriad, a testbed written in JAX for learning and planning in real-world continuous environments. The primary contributions of My… (see more)riad are threefold. First, Myriad provides machine learning practitioners access to trajectory optimization techniques for application within a typical automatic differentiation workflow. Second, Myriad presents many real-world optimal control problems, ranging from biology to medicine to engineering, for use by the machine learning community. Formulated in continuous space and time, these environments retain some of the complexity of real-world systems often abstracted away by standard benchmarks. As such, Myriad strives to serve as a stepping stone towards application of modern machine learning techniques for impactful real-world tasks. Finally, we use the Myriad repository to showcase a novel approach for learning and control tasks. Trained in a fully end-to-end fashion, our model leverages an implicit planning module over neural ordinary differential equations, enabling simultaneous learning and planning with complex environment dynamics.

2022-11-28

Neural Information Processing Systems (Accept)

Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization

The ability to accelerate the design of biological sequences can have a substantial impact on the progress of the medical field. The problem… (see more) can be framed as a global optimization problem where the objective is an expensive black-box function such that we can query large batches restricted with a limitation of a low number of rounds. Bayesian Optimization is a principled method for tackling this problem. However, the astronomically large state space of biological sequences renders brute-force iterating over all possible sequences infeasible. In this paper, we propose MetaRLBO where we train an autoregressive generative model via Meta-Reinforcement Learning to propose promising sequences for selection via Bayesian Optimization. We pose this problem as that of finding an optimal policy over a distribution of MDPs induced by sampling subsets of the data acquired in the previous rounds. Our in-silico experiments show that meta-learning over such ensembles provides robustness against reward misspecification and achieves competitive results compared to existing strong baselines.

2022-09-12

ArXiv (preprint)

arxiv.org

Direct Behavior Specification via Constrained Reinforcement Learning

Christopher Pal

The standard formulation of Reinforcement Learning lacks a practical way of specifying what are admissible and forbidden behaviors. Most oft… (see more)en, practitioners go about the task of behavior specification by manually engineering the reward function, a counter-intuitive process that requires several iterations and is prone to reward hacking by the agent. In this work, we argue that constrained RL, which has almost exclusively been used for safe RL, also has the potential to significantly reduce the amount of work spent for reward specification in applied RL projects. To this end, we propose to specify behavioral preferences in the CMDP framework and to use Lagrangian methods to automatically weigh each of these behavioral constraints. Specifically, we investigate how CMDPs can be adapted to solve goal-based tasks while adhering to several constraints simultaneously. We evaluate this framework on a set of continuous control tasks relevant to the application of Reinforcement Learning for NPC design in video games.

2022-07-18

International Conference on Machine Learning (Accept for Short Presentation)

proceedings.mlr.press

The Primacy Bias in Deep Reinforcement Learning

This work identifies a common flaw of deep reinforcement learning (RL) algorithms: a tendency to rely on early interactions and ignore usefu… (see more)l evidence encountered later. Because of training on progressively growing datasets, deep RL agents incur a risk of overfitting to earlier experiences, negatively affecting the rest of the learning process. Inspired by cognitive science, we refer to this effect as the primacy bias. Through a series of experiments, we dissect the algorithmic aspects of deep RL that exacerbate this bias. We then propose a simple yet generally-applicable mechanism that tackles the primacy bias by periodically resetting a part of the agent. We apply this mechanism to algorithms in both discrete (Atari 100k) and continuous action (DeepMind Control Suite) domains, consistently improving their performance.

2022-07-18

ICML (Accept for Short Presentation)

proceedings.mlr.press

Continuous-Time Meta-Learning with Forward Mode Differentiation

Drawing inspiration from gradient-based meta-learning methods with infinitely small gradient steps, we introduce Continuous-Time Meta-Learni… (see more)ng (COMLN), a meta-learning algorithm where adaptation follows the dynamics of a gradient vector field. Specifically, representations of the inputs are meta-learned such that a task-specific linear classifier is obtained as a solution of an ordinary differential equation (ODE). Treating the learning process as an ODE offers the notable advantage that the length of the trajectory is now continuous, as opposed to a fixed and discrete number of gradient steps. As a consequence, we can optimize the amount of adaptation necessary to solve a new task using stochastic gradient descent, in addition to learning the initial conditions as is standard practice in gradient-based meta-learning. Importantly, in order to compute the exact meta-gradients required for the outer-loop updates, we devise an efficient algorithm based on forward mode differentiation, whose memory requirements do not scale with the length of the learning trajectory, thus allowing longer adaptation in constant memory. We provide analytical guarantees for the stability of COMLN, we show empirically its efficiency in terms of runtime and memory usage, and we illustrate its effectiveness on a range of few-shot image classification problems.

2022-04-24

ICLR (Accept (Spotlight))

Long-Term Credit Assignment via Model-based Temporal Shortcuts

2021-12-12

NeurIPS.cc/2021/Workshop/DeepRL (accepted)

Neural Algorithmic Reasoners are Implicit Planners

Andreea Deac

Petar Veličković

Ognjen Milinković

Jian Tang

Mladen Nikolić

Implicit planning has emerged as an elegant technique for combining learned models of the world with end-to-end model-free reinforcement lea… (see more)rning. We study the class of implicit planners inspired by value iteration, an algorithm that is guaranteed to yield perfect policies in fully-specified tabular environments. We find that prior approaches either assume that the environment is provided in such a tabular form -- which is highly restrictive -- or infer "local neighbourhoods" of states to run value iteration over -- for which we discover an algorithmic bottleneck effect. This effect is caused by explicitly running the planning algorithm based on scalar predictions in every state, which can be harmful to data efficiency if such scalars are improperly predicted. We propose eXecuted Latent Value Iteration Networks (XLVINs), which alleviate the above limitations. Our method performs all planning computations in a high-dimensional latent space, breaking the algorithmic bottleneck. It maintains alignment with value iteration by carefully leveraging neural graph-algorithmic reasoning and contrastive self-supervised learning. Across eight low-data settings -- including classical control, navigation and Atari -- XLVINs provide significant improvements to data efficiency against value iteration-based implicit planners, as well as relevant model-free baselines. Lastly, we empirically verify that XLVINs can closely align with value iteration.

2021-12-06

Conference on Neural Information Processing Systems (Accept (Spotlight))

TDprop: Does Adaptive Optimization With Jacobi Preconditioning Help Temporal Difference Learning?

We investigate whether Jacobi preconditioning, accounting for the bootstrap term in temporal difference (TD) learning, can help boost perfor… (see more)mance of adaptive optimizers. Our method, TDprop, computes a per-parameter learning rate based on the diagonal preconditioning of the TD update rule. We show how this can be used in both n-step returns and TD(λ). Our theoretical findings demonstrate that including this additional preconditioning information is comparable to normal semi-gradient TD if the optimal learning rate is found for both via a hyperparameter search. This matches our experimental results. In Deep RL experiments using Expected SARSA, TDprop meets or exceeds the performance of Adam in all tested games under near-optimal learning rates, but a well-tuned SGD can yield similar performance in most settings. Our findings suggest that Jacobi preconditioning may improve upon Adam in Deep RL, but despite incorporating additional information from the TD bootstrap term, may not always be better than SGD. Moreover, they suggest that more theoretical investigations are needed to understand adaptive optimizers under optimal hyperparameter regimes in TD learning: simpler methods may, surprisingly, be theoretically comparable after a hyperparameter search.

2021-05-02

Adaptive Agents and Multi-Agent Systems (published)

Options of Interest: Temporal Abstraction with Interest Functions

Khimya Khetarpal

Martin Klissarov

Maxime Chevalier-Boisvert

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… (see more)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.

2020-04-02

Proceedings of the AAAI Conference on Artificial Intelligence (published)

arxiv.org

Policy Evaluation Networks

Jean Harb

Tom Schaul

Doina Precup

Many reinforcement learning algorithms use value functions to guide the search for better policies. These methods estimate the value of a si… (see more)ngle policy while generalizing across many states. The core idea of this paper is to flip this convention and estimate the value of many policies, for a single set of states. This approach opens up the possibility of performing direct gradient ascent in policy space without seeing any new data. The main challenge for this approach is finding a way to represent complex policies that facilitates learning and generalization. To address this problem, we introduce a scalable, differentiable fingerprinting mechanism that retains essential policy information in a concise embedding. Our empirical results demonstrate that combining these three elements (learned Policy Evaluation Network, policy fingerprints, gradient ascent) can produce policies that outperform those that generated the training data, in zero-shot manner.

2020-02-25

ArXiv (preprint)