Portrait of Marc Gendron-Bellemare is unavailable

Marc Gendron-Bellemare

Core Industry Member
Canada CIFAR AI Chair
Associate Professor, McGill University, School of Computer Science
Adjunct Professor, Université de Montréal, Department of Computer Science and Operations Research
Chief Scientific Officer, Reliant AI
Research Topics
Large Language Models (LLM)
Reinforcement Learning
Representation Learning

Biography

I am Chief Scientific Officer at Reliant AI, an adjunct professor at the School of Computer and Science at McGill University, and an adjunct professor at the Department of Computer Science and Operations Research (DIRO) at Université de Montréal.

Previously, I was a research scientist at Google Brain in Montréal, where my research focused on reinforcement learning effort. From 2013 to 2017, I worked at DeepMind in the U.K. I received my PhD from the University of Alberta under the supervision of Michael Bowling and Joel Veness.

My research lies at the intersection of reinforcement learning and probabilistic prediction. I am also interested in deep learning, generative modelling, online learning and information theory.

Current Students

Pierluca D'Oro
Collaborating Alumni - Université de Montréal
Principal supervisor :
Pierre-Luc Bacon
Website
Github
Google Scholar
Jesse Farebrother
PhD - McGill University
Co-supervisor :
Doina Precup
Website
Github
Google Scholar
Nate Rahn
PhD - McGill University
Co-supervisor :
Doina Precup
Jesse Silverberg
PhD - Université de Montréal
Principal supervisor :
Glen Berseth
jesse@jessesilverberg.com
Website
Github
Google Scholar
Harley Wiltzer
PhD - McGill University
Principal supervisor :
David Meger
Website
Github
Google Scholar

Publications

A Geometric Perspective on Optimal Representations for Reinforcement Learning
Marc Gendron-Bellemare
Will Dabney
Robert Dadashi
Adrien Ali Taiga
Pablo Samuel Castro
Nicolas Le Roux
Dale Schuurmans
Tor Lattimore
Clare Lyle
openreview.net
Dopamine: A Research Framework for Deep Reinforcement Learning
Pablo Samuel Castro
Subhodeep Moitra
Carles Gelada
Saurabh Kumar
Marc Gendron-Bellemare
Deep reinforcement learning (deep RL) research has grown significantly in recent years. A number of software offerings now exist that provid… (see more)e stable, comprehensive implementations for benchmarking. At the same time, recent deep RL research has become more diverse in its goals. In this paper we introduce Dopamine, a new research framework for deep RL that aims to support some of that diversity. Dopamine is open-source, TensorFlow-based, and provides compact and reliable implementations of some state-of-the-art deep RL agents. We complement this offering with a taxonomy of the different research objectives in deep RL research. While by no means exhaustive, our analysis highlights the heterogeneity of research in the field, and the value of frameworks such as ours.
2018-09-27
ArXiv (preprint)
arxiv.org
arxiv.org
Approximate Exploration through State Abstraction
Adrien Ali Taiga
Aaron Courville
Marc Gendron-Bellemare
Although exploration in reinforcement learning is well understood from a theoretical point of view, provably correct methods remain impracti… (see more)cal. In this paper we study the interplay between exploration and approximation, what we call approximate exploration. Our main goal is to further our theoretical understanding of pseudo-count based exploration bonuses (Bellemare et al., 2016), a practical exploration scheme based on density modelling. As a warm-up, we quantify the performance of an exploration algorithm, MBIE-EB (Strehl and Littman, 2008), when explicitly combined with state aggregation. This allows us to confirm that, as might be expected, approximation allows the agent to trade off between learning speed and quality of the learned policy. Next, we show how a given density model can be related to an abstraction and that the corresponding pseudo-count bonus can act as a substitute in MBIE-EB combined with this abstraction, but may lead to either under- or over-exploration. Then, we show that a given density model also defines an implicit abstraction, and find a surprising mismatch between pseudo-counts derived either implicitly or explicitly. Finally we derive a new pseudo-count bonus alleviating this issue.
2018-08-29
ArXiv (preprint)
arxiv.org
arxiv.org