Doina Precup

Sumana Basu

Collaborating Alumni - McGill University

Co-supervisor :

Adriana Romero Soriano

Collaborating Alumni - McGill University

Lynn Cherif

Collaborating Alumni - 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

Co-supervisor :

Isabeau Prémont-Schwarz

Franco Del Balso

Collaborating researcher - Université de Montréal

Jesse Farebrother

PhD - McGill University

Principal supervisor :

Marc Gendron-Bellemare

PhD - McGill University

Principal supervisor :

Collaborating researcher - Birla Institute of Technology

Howard Huang

PhD - McGill University

Haque Ishfaq

Collaborating Alumni - McGill University

Mohammad Sami Nur Islam Islam

Master's Research - McGill University

Arushi Jain

Collaborating Alumni - McGill University

PhD - Polytechnique Montréal

Martin Klissarov

PhD - McGill University

Postdoctorate - McGill University

Jonathan Lebensold

Collaborating Alumni - McGill University

Collaborating Alumni - McGill University

Ray Luo

PhD - McGill University

Principal supervisor :

G McCracken

PhD - McGill University

Nazanin Mohammadi Sepahvand

Collaborating Alumni - 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 :

PhD - McGill University

Co-supervisor :

Nate Rahn

PhD - McGill University

Principal supervisor :

Marc Gendron-Bellemare

Manoosh Samiei

PhD - McGill University

Co-supervisor :

PhD - McGill University

Co-supervisor :

PhD - McGill University

Nishanth Anand Vemgal

PhD - McGill University

PhD - McGill University

Co-supervisor :

Samira Ebrahimi Kahou

Zihan Wang

PhD - McGill University

Skipper: Combining Spatial and Temporal Abstraction for Better Generalization

Guangyuan Wang

Research Intern - McGill University

Steve Wen

Master's Research - McGill University

Co-supervisor :

Gregory Dudek

Zijing Wu

PhD - McGill University

Principal supervisor :

PhD - McGill University

Harry Zhao

Collaborating Alumni - 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

Read the article

Publications

Multi-Environment Pretraining Enables Transfer to Action Limited Datasets

David Venuto

Sherry Yang

Pieter Abbeel

Igor Mordatch

Ofir Nachum

Using massive datasets to train large-scale models has emerged as a dominant approach for broad generalization in natural language and visio… (see more)n applications. In reinforcement learning, however, a key challenge is that available data of sequential decision making is often not annotated with actions - for example, videos of game-play are much more available than sequences of frames paired with their logged game controls. We propose to circumvent this challenge by combining large but sparsely-annotated datasets from a \emph{target} environment of interest with fully-annotated datasets from various other \emph{source} environments. Our method, Action Limited PreTraining (ALPT), leverages the generalization capabilities of inverse dynamics modelling (IDM) to label missing action data in the target environment. We show that utilizing even one additional environment dataset of labelled data during IDM pretraining gives rise to substantial improvements in generating action labels for unannotated sequences. We evaluate our method on benchmark game-playing environments and show that we can significantly improve game performance and generalization capability compared to other approaches, using annotated datasets equivalent to only

2022-12-31

ICML (published)

proceedings.mlr.press

Optimism and Adaptivity in Policy Optimization

Veronica Chelu

Tom Zahavy

Arthur Guez

Sebastian Flennerhag

2022-12-31

arXiv.org (preprint)

Temporal Abstraction in Reinforcement Learning with the Successor Representation

Marlos C. Machado

Andre Barreto

Michael Bowling

2022-12-31

J. Mach. Learn. Res. (published)

Offline Policy Optimization in RL with Variance Regularizaton

Riashat Islam

Samarth Sinha

Homanga Bharadhwaj

Samin Yeasar Arnob

Zhuoran Yang

Animesh Garg

Zhaoran Wang

Lihong Li

2022-12-28

ArXiv (preprint)

Towards Continual Reinforcement Learning: A Review and Perspectives

Khimya Khetarpal

Matthew D Riemer

Irina Rish

2022-12-21

Journal of Artificial Intelligence Research (published)

Bayesian Q-learning With Imperfect Expert Demonstrations

Xiru Zhu

Guided exploration with expert demonstrations improves data efficiency for reinforcement learning, but current algorithms often overuse expe… (see more)rt information. We propose a novel algorithm to speed up Q-learning with the help of a limited amount of imperfect expert demonstrations. The algorithm avoids excessive reliance on expert data by relaxing the optimal expert assumption and gradually reducing the usage of uninformative expert data. Experimentally, we evaluate our approach on a sparse-reward chain environment and six more complicated Atari games with delayed rewards. With the proposed methods, we can achieve better results than Deep Q-learning from Demonstrations (Hester et al., 2017) in most environments.

2022-12-08

NeurIPS.cc/2022/Workshop/DeepRL (accepted)

Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks

Mingde Zhao

Xiao-Wen Chang

The core operation of current Graph Neural Networks (GNNs) is the aggregation enabled by the graph Laplacian or message passing, which filte… (see more)rs the neighborhood node information. Though effective for various tasks, in this paper, we show that they are potentially a problematic factor underlying all GNN methods for learning on certain datasets, as they force the node representations similar, making the nodes gradually lose their identity and become indistinguishable. Hence, we augment the aggregation operations with their dual, i.e. diversification operators that make the node more distinct and preserve the identity. Such augmentation replaces the aggregation with a two-channel filtering process that, in theory, is beneficial for enriching the node representations. In practice, the proposed two-channel filters can be easily patched on existing GNN methods with diverse training strategies, including spectral and spatial (message passing) methods. In the experiments, we observe desired characteristics of the models and significant performance boost upon the baselines on 9 node classification tasks.

2022-11-21

NeurIPS.cc/2022/Workshop/GLFrontiers (published)

When Do We Need Graph Neural Networks for Node Classification?

Sitao Luan

Chenqing Hua

Qincheng Lu

Jiaqi Zhu

Xiao-Wen Chang

2022-10-29

arXiv.org (preprint)

Simulating Human Gaze with Neural Visual Attention

Leo Schwinn

Bjoern Eskofier

Dario Zanca

2022-10-19

NeurIPS.cc/2022/Workshop/GMML (oral)

The Paradox of Choice: On the Role of Attention in Hierarchical Reinforcement Learning

Andrei Cristian Nica

Khimya Khetarpal

Decision-making AI agents are often faced with two important challenges: the depth of the planning horizon, and the branching factor due to … (see more)having many choices. Hierarchical reinforcement learning methods aim to solve the first problem, by providing shortcuts that skip over multiple time steps. To cope with the breadth, it is desirable to restrict the agent's attention at each step to a reasonable number of possible choices. The concept of affordances (Gibson, 1977) suggests that only certain actions are feasible in certain states. In this work, we first characterize "affordances" as a "hard" attention mechanism that strictly limits the available choices of temporally extended options. We then investigate the role of hard versus soft attention in training data collection, abstract value learning in long-horizon tasks, and handling a growing number of choices. To this end, we present an online, model-free algorithm to learn affordances that can be used to further learn subgoal options. Finally, we identify and empirically demonstrate the settings in which the "paradox of choice" arises, i.e. when having fewer but more meaningful choices improves the learning speed and performance of a reinforcement learning agent.

2022-10-19

NeurIPS.cc/2022/Workshop/Attention (poster)

Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement Learning

Jacob Shkrob

My Duc Tran

Sumana Basu

Mechanical ventilation is a key form of life support for patients with pulmonary impairment. Healthcare workers are required to continuously… (see more) adjust ventilator settings for each patient, a challenging and time consuming task. Hence, it would be beneficial to develop an automated decision support tool to optimize ventilation treatment. We present DeepVent, a Conservative Q-Learning (CQL) based offline Deep Reinforcement Learning (DRL) agent that learns to predict the optimal ventilator parameters for a patient to promote 90 day survival. We design a clinically relevant intermediate reward that encourages continuous improvement of the patient vitals as well as addresses the challenge of sparse reward in RL. We find that DeepVent recommends ventilation parameters within safe ranges, as outlined in recent clinical trials. The CQL algorithm offers additional safety by mitigating the overestimation of the value estimates of out-of-distribution states/actions. We evaluate our agent using Fitted Q Evaluation (FQE) and demonstrate that it outperforms physicians from the MIMIC-III dataset.

2022-10-04

ArXiv (preprint)

Assessing Intrapartum Risk of Hypoxic-Ischemic Encephalopathy using Fetal Heart Rate with Long Short-term Memory Networks

"Derek Kweku DEGBEDZUI

Michael Kuzniewicz

Cornet Marie-Coralie

Yvonne Wu

Heather Forquer

Lawrence Gerstley

Emily Hamilton

Philip Warrick

Robert Kearney"

This study investigated the prediction of the risk of hypoxic ischemic encephalopathy using intrapartum cardiotocography records with a long… (see more) short-term memory re-current neural network. Across the 12 hours of labour, HIE sensitivity rose from 0.25 to 0.56 as delivery approached while specificity remained approximately constant with a mean of 0.71 and standard deviation of 0.04. The results show that classification improves as delivery approaches but that performance needs improvement. Future work will address the limitations of this preliminary study by investigating input signal transformations and the use of other network architectures to improve the model performance.

2022-09-03

2022 Computing in Cardiology (CinC) (published)