Doina Precup

Sumana Basu

PhD - McGill University

Co-supervisor :

Adriana Romero Soriano

PhD - McGill University

Lynn Cherif

Master's Research - McGill University

Co-supervisor :

PhD - McGill University

Co-supervisor :

PhD - McGill University

Principal supervisor :

David Meger

Jonathan Colaço Carr

Master's Research - McGill University

Principal supervisor :

Prakash Panangaden

Élodie Coté-Gauthier

Collaborating researcher - McGill University

Franco Del Balso

Research Intern - Université de Montréal

Jesse Farebrother

PhD - McGill University

Principal supervisor :

Marc Gendron-Bellemare

PhD - McGill University

Principal supervisor :

Eilif Benjamin Muller

PhD - McGill University

Haque Ishfaq

PhD - McGill University

Website

Mohammad Sami Nur Islam Islam

Master's Research - McGill University

Arushi Jain

PhD - McGill University

PhD - McGill University

Postdoctorate - McGill University

Elaine Lau

Master's Research - McGill University

Jonathan Lebensold

Collaborating Alumni - McGill University

Undergraduate - McGill University

Ray Luo

PhD - McGill University

Principal supervisor :

G McCracken

PhD - McGill University

Nazanin Mohammadi Sepahvand

PhD - McGill University

Shahrad Mohammadzadeh

Master's Research - McGill University

Principal supervisor :

Gabriela Moisescu-Pareja

Collaborating researcher - McGill University

Co-supervisor :

Irina Rish

Padideh Nouri

PhD - Université de Montréal

Co-supervisor :

Charles Onu

PhD - McGill University

PhD - McGill University

Co-supervisor :

Nate Rahn

PhD - McGill University

Principal supervisor :

Marc Gendron-Bellemare

Sahand Rezaei-Shoshtari

PhD - McGill University

Co-supervisor :

PhD - McGill University

Co-supervisor :

PhD - McGill University

Co-supervisor :

Blake Richards

samiemandana@gmail.com

PhD - McGill University

Website

Nishanth Anand Vemgal

PhD - McGill University

PhD - McGill University

Priyesh Vijayan

PhD - McGill University

Co-supervisor :

Samira Ebrahimi Kahou

Research Intern - McGill University

Steve Wen

Master's Research - McGill University

Co-supervisor :

Gregory Dudek

Zijing Wu

PhD - McGill University

Co-supervisor :

PhD - McGill University

Skipper: Combining Spatial and Temporal Abstraction for Better Generalization

Harry Zhao

PhD - McGill University

Co-supervisor :

Blog Posts

Generic thumbnail for Mila Blog articles.

February 22, 2024

Mingde Harry Zhao

Safa Alver

Harm van Seijen

Romain Laroche

Doina Precup

Yoshua Bengio

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Publications

Flexible Option Learning

Martin Klissarov

2021-11-09

NeurIPS.cc/2021/Conference (spotlight)

openreview.net

Gradient Starvation: A Learning Proclivity in Neural Networks

Mohammad Pezeshki

Sékou-Oumar Kaba

We identify and formalize a fundamental gradient descent phenomenon resulting in a learning proclivity in over-parameterized neural networks… (see more). Gradient Starvation arises when cross-entropy loss is minimized by capturing only a subset of features relevant for the task, despite the presence of other predictive features that fail to be discovered. This work provides a theoretical explanation for the emergence of such feature imbalance in neural networks. Using tools from Dynamical Systems theory, we identify simple properties of learning dynamics during gradient descent that lead to this imbalance, and prove that such a situation can be expected given certain statistical structure in training data. Based on our proposed formalism, we develop guarantees for a novel regularization method aimed at decoupling feature learning dynamics, improving accuracy and robustness in cases hindered by gradient starvation. We illustrate our findings with simple and real-world out-of-distribution (OOD) generalization experiments.

openreview.net

Estimating treatment effect for individuals with progressive multiple sclerosis using deep learning

JR Falet

Joshua D. Durso-Finley

Brennan Nichyporuk

Jan Schroeter

Francesca Bovis

Maria-Pia Sormani

Tal Arbel

Douglas Arnold

2021-11-01

medRxiv (preprint)

Reward is enough

David Silver

Satinder Singh

Richard S. Sutton

2021-10-01

Artificial Intelligence (published)

Reward is enough

David Silver

Satinder Singh

Richard S. Sutton

2021-10-01

Artificial Intelligence (published)

Reward is enough

David Silver

Satinder Singh

Richard S. Sutton

2021-10-01

Artificial Intelligence (published)

Reward is enough

David Silver

Satinder Singh

Richard S. Sutton

2021-10-01

Artificial Intelligence (published)

A Survey of Exploration Methods in Reinforcement Learning

Susan Amin

Maziar Gomrokchi

Harsh Satija

Herke van Hoof

2021-09-01

ArXiv (preprint)

A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation

Scott Fujimoto

David Meger

Marginalized importance sampling (MIS), which measures the density ratio between the state-action occupancy of a target policy and that of a… (see more) 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 (published)

proceedings.mlr.press

Locally Persistent Exploration in Continuous Control Tasks with Sparse Rewards

Susan Amin

Maziar Gomrokchi

Hossein Aboutalebi

Harsh Satija

A major challenge in reinforcement learning is the design of exploration strategies, especially for environments with sparse reward structur… (see more)es and continuous state and action spaces. Intuitively, if the reinforcement signal is very scarce, the agent should rely on some form of short-term memory in order to cover its environment efficiently. We propose a new exploration method, based on two intuitions: (1) the choice of the next exploratory action should depend not only on the (Markovian) state of the environment, but also on the agent's trajectory so far, and (2) the agent should utilize a measure of spread in the state space to avoid getting stuck in a small region. Our method leverages concepts often used in statistical physics to provide explanations for the behavior of simplified (polymer) chains in order to generate persistent (locally self-avoiding) trajectories in state space. We discuss the theoretical properties of locally self-avoiding walks and their ability to provide a kind of short-term memory through a decaying temporal correlation within the trajectory. We provide empirical evaluations of our approach in a simulated 2D navigation task, as well as higher-dimensional MuJoCo continuous control locomotion tasks with sparse rewards.

2021-07-01

Proceedings of the 38th International Conference on Machine Learning (published)

proceedings.mlr.press

Randomized Exploration for Reinforcement Learning with General Value Function Approximation

Haque Ishfaq

Qiwen Cui

Viet Huy Nguyen

Alex Ayoub

Zhuoran Yang

Zhaoran Wang

Lin F. Yang

We propose a model-free reinforcement learning algorithm inspired by the popular randomized least squares value iteration (RLSVI) algorithm … (see more)as well as the optimism principle. Unlike existing upper-confidence-bound (UCB) based approaches, which are often computationally intractable, our algorithm drives exploration by simply perturbing the training data with judiciously chosen i.i.d. scalar noises. To attain optimistic value function estimation without resorting to a UCB-style bonus, we introduce an optimistic reward sampling procedure. When the value functions can be represented by a function class

2021-06-15

ArXiv (preprint)

Correcting Momentum in Temporal Difference Learning

Emmanuel Bengio

Joelle Pineau