Portrait de David Meger

David Meger

Membre académique associé
Professeur adjoint, McGill University, École d'informatique
Sujets de recherche
Apprentissage par renforcement
Vision par ordinateur

Biographie

David Meger est professeur adjoint à l'École d'informatique de l'Université McGill. Il codirige le Laboratoire de robotique mobile au sein du Centre sur les machines intelligentes, qui est l'un des groupes de recherche en robotique les plus importants et les plus anciens du Canada. Les travaux de recherche du professeur Meger portent notamment sur les robots à guidage visuel dotés d'une vision et d'un apprentissage actifs, sur les modèles d'apprentissage par renforcement profond qui sont largement cités et utilisés par les chercheurs et l'industrie dans le monde entier, et sur la robotique de terrain, y compris les déploiements autonomes sous l'eau et sur la terre ferme. Il a été le président général de la première conférence conjointe CS-CAN au Canada en 2023.

Étudiants actuels

Julie Alhosh
Maîtrise recherche - McGill
juliealhosh@gmail.com
Site web
Github
Google Scholar
Rambod Azimi
Collaborateur·rice de recherche - McGill
Superviseur⋅e principal⋅e :
Samira Ebrahimi Kahou
Site web
Github
Google Scholar
William Bonilla
Doctorat - McGill
Github
Valliappan Chidambaram Adaikkappan
Doctorat - McGill
Github
Google Scholar
Wesley Chung
Doctorat - McGill
Co-superviseur⋅e :
Doina Precup
Farnoosh Faraji
Doctorat - McGill
Co-superviseur⋅e :
Gregory Dudek
Github
Google Scholar
Anas Houssaini
Maîtrise recherche - McGill
Co-superviseur⋅e :
Hsiu-Chin Lin
Github
Zina Kamel
Maîtrise recherche - McGill
Co-superviseur⋅e :
Hsiu-Chin Lin
Sahand Rezaei-Shoshtari
Doctorat - McGill
Superviseur⋅e principal⋅e :
Doina Precup
Site web
Github
Google Scholar
Arian Sargazi
Doctorat - McGill
Steven Wang
Maîtrise recherche - McGill
Harley Wiltzer
Doctorat - McGill
Co-superviseur⋅e :
Marc Gendron-Bellemare
Site web
Github
Google Scholar
Hanna Yurchyk
Maîtrise recherche - McGill
Github

Publications

Learning active tactile perception through belief-space control
Jean-François Tremblay
Johanna Hansen
David Meger
Francois Hogan
Gregory Dudek
Robot operating in an open world can encounter novel objects with unknown physical properties, such as mass, friction, or size. It is desira… (voir plus)ble to be able to sense those property through contact-rich interaction, before performing downstream tasks with the objects. We propose a method for autonomously learning active tactile perception policies, by learning a generative world model leveraging a differentiable bayesian filtering algorithm, and designing an information- gathering model predictive controller. We test the method on three simulated tasks: mass estimation, height estimation and toppling height estimation. Our method is able to discover policies which gather information about the desired property in an intuitive manner.
2022-05-12
ICRA.org/2022/Workshop/Contact-Rich (poster)
doi.org
openreview.net
Continuous MDP Homomorphisms and Homomorphic Policy Gradient
Sahand Rezaei-Shoshtari
Rosie Zhao
Prakash Panangaden
David Meger
Doina Precup
openreview.net
A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation
Scott Fujimoto
David Meger
Doina Precup
Marginalized importance sampling (MIS), which measures the density ratio between the state-action occupancy of a target policy and that of a… (voir plus) sampling distribution, is a promising approach for off-policy evaluation. However, current state-of-the-art MIS methods rely on complex optimization tricks and succeed mostly on simple toy problems. We bridge the gap between MIS and deep reinforcement learning by observing that the density ratio can be computed from the successor representation of the target policy. The successor representation can be trained through deep reinforcement learning methodology and decouples the reward optimization from the dynamics of the environment, making the resulting algorithm stable and applicable to high-dimensional domains. We evaluate the empirical performance of our approach on a variety of challenging Atari and MuJoCo environments.
2021-07-01
Proceedings of the 38th International Conference on Machine Learning (publié)
proceedings.mlr.press
arxiv.org
An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay
Scott Fujimoto
David Meger
Doina Precup
Prioritized Experience Replay (PER) is a deep reinforcement learning technique in which agents learn from transitions sampled with non-unifo… (voir plus)rm probability proportionate to their temporal-difference error. We show that any loss function evaluated with non-uniformly sampled data can be transformed into another uniformly sampled loss function with the same expected gradient. Surprisingly, we find in some environments PER can be replaced entirely by this new loss function without impact to empirical performance. Furthermore, this relationship suggests a new branch of improvements to PER by correcting its uniformly sampled loss function equivalent. We demonstrate the effectiveness of our proposed modifications to PER and the equivalent loss function in several MuJoCo and Atari environments.
arxiv.org