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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
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

PhD - Université de Montréal
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PhD - Université de Montréal
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PhD - McGill University
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PhD - McGill University
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PhD - Université de Montréal
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PhD - McGill University
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Publications

A Geometric Perspective on Optimal Representations for Reinforcement Learning
Will Dabney
Robert Dadashi
Adrien Ali Taiga
Dale Schuurmans
Tor Lattimore
Clare Lyle
Dopamine: A Research Framework for Deep Reinforcement Learning
Subhodeep Moitra
Carles Gelada
Saurabh Kumar
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.