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

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

Nishanth Anand Vemgal

PhD - McGill University

PhD - McGill University

Priyesh Vijayan

PhD - McGill University

Co-supervisor :

Samira Ebrahimi Kahou

Zihan Wang

PhD - McGill University

Guangyuan Wang

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

Collaborating Alumni - McGill University

Co-supervisor :

Blog Posts

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February 22, 2024

Mingde Harry Zhao

Safa Alver

Harm van Seijen

Romain Laroche

Doina Precup

Yoshua Bengio

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Publications

META-Learning State-based Eligibility Traces for More Sample-Efficient Policy Evaluation

Mingde Zhao

Sitao Luan

Ian Porada

Xiao-Wen Chang

Temporal-Difference (TD) learning is a standard and very successful reinforcement learning approach, at the core of both algorithms that lea… (see more)rn the value of a given policy, as well as algorithms which learn how to improve policies. TD-learning with eligibility traces provides a way to boost sample efficiency by temporal credit assignment, i.e. deciding which portion of a reward should be assigned to predecessor states that occurred at different previous times, controlled by a parameter

2020-01-01

AAMAS (published)

dblp.uni-trier.de

Reward Propagation Using Graph Convolutional Networks

Martin Klissarov

Reward Redistribution Mechanisms in Multi-agent Reinforcement Learning

Aly Ibrahim

Anirudha Jitani

Piracha

Daoud

In typical Multi-Agent Reinforcement Learning (MARL) settings, each agent acts to maximize its individual reward objective. However, for col… (see more)lective social welfare maximization, some agents may need to act non-selfishly. We propose a reward shaping mechanism using extrinsic motivation for achieving modularity and increased cooperation among agents in Sequential Social Dilemma (SSD) problems. Our mechanism, inspired by capitalism, provides extrinsic motivation to agents by redistributing a portion of collected re-wards based on each agent’s individual contribution towards team rewards. We demonstrate empirically that this mechanism leads to higher collective welfare relative to existing baselines. Furthermore, this reduces free rider issues and leads to more diverse policies. We evaluate our proposed mechanism for already specialised agents that are pre-trained for specific roles. We show that our mechanism, in the most challenging CleanUp environment, significantly out-performs two baselines (based roughly on socialism and anarchy) and accumulates 2-3 times higher rewards in an easier setting of the environment.

Value-driven Hindsight Modelling

Arthur Guez

Fabio Viola

Theophane Weber

Lars Buesing

Steven Kapturowski

David Silver

Nicolas Heess

Value estimation is a critical component of the reinforcement learning (RL) paradigm. The question of how to effectively learn predictors fo… (see more)r value from data is one of the major problems studied by the RL community, and different approaches exploit structure in the problem domain in different ways. Model learning can make use of the rich transition structure present in sequences of observations, but this approach is usually not sensitive to the reward function. In contrast, model-free methods directly leverage the quantity of interest from the future but have to compose with a potentially weak scalar signal (an estimate of the return). In this paper we develop an approach for representation learning in RL that sits in between these two extremes: we propose to learn what to model in a way that can directly help value prediction. To this end we determine which features of the future trajectory provide useful information to predict the associated return. This provides us with tractable prediction targets that are directly relevant for a task, and can thus accelerate learning of the value function. The idea can be understood as reasoning, in hindsight, about which aspects of the future observations could help past value prediction. We show how this can help dramatically even in simple policy evaluation settings. We then test our approach at scale in challenging domains, including on 57 Atari 2600 games.

openreview.net

Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning

Tianyu Li

Bogdan Mazoure

Guillaume Rabusseau

Learning and planning in partially-observable domains is one of the most difficult problems in reinforcement learning. Traditional methods c… (see more)onsider these two problems as independent, resulting in a classical two-stage paradigm: first learn the environment dynamics and then plan accordingly. This approach, however, disconnects the two problems and can consequently lead to algorithms that are sample inefficient and time consuming. In this paper, we propose a novel algorithm that combines learning and planning together. Our algorithm is closely related to the spectral learning algorithm for predicitive state representations and offers appealing theoretical guarantees and time complexity. We empirically show on two domains that our approach is more sample and time efficient compared to classical methods.

2019-11-01

ArXiv (preprint)

Nazanin Mohammadi Sepahvand

Improving Pathological Structure Segmentation via Transfer Learning Across Diseases

Barleen Kaur

Paul Lemaitre

Raghav Mehta

Douglas Arnold

Tal Arbel

2019-10-13

Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data (published)

Avoidance Learning Using Observational Reinforcement Learning

David Venuto

Léonard Boussioux

Junhao Wang

Rola Dali

Jhelum Chakravorty

Yoshua Bengio

Imitation learning seeks to learn an expert policy from sampled demonstrations. However, in the real world, it is often difficult to find a … (see more)perfect expert and avoiding dangerous behaviors becomes relevant for safety reasons. We present the idea of \textit{learning to avoid}, an objective opposite to imitation learning in some sense, where an agent learns to avoid a demonstrator policy given an environment. We define avoidance learning as the process of optimizing the agent's reward while avoiding dangerous behaviors given by a demonstrator. In this work we develop a framework of avoidance learning by defining a suitable objective function for these problems which involves the \emph{distance} of state occupancy distributions of the expert and demonstrator policies. We use density estimates for state occupancy measures and use the aforementioned distance as the reward bonus for avoiding the demonstrator. We validate our theory with experiments using a wide range of partially observable environments. Experimental results show that we are able to improve sample efficiency during training compared to state of the art policy optimization and safety methods.

2019-09-24

ArXiv (preprint)

An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation

Vincent Michalski

Vikram Voleti

Samira Ebrahimi Kahou

Anthony Ortiz

Pascal Vincent

Chris Pal

Batch normalization has been widely used to improve optimization in deep neural networks. While the uncertainty in batch statistics can act … (see more)as a regularizer, using these dataset statistics specific to the training set impairs generalization in certain tasks. Recently, alternative methods for normalizing feature activations in neural networks have been proposed. Among them, group normalization has been shown to yield similar, in some domains even superior performance to batch normalization. All these methods utilize a learned affine transformation after the normalization operation to increase representational power. Methods used in conditional computation define the parameters of these transformations as learnable functions of conditioning information. In this work, we study whether and where the conditional formulation of group normalization can improve generalization compared to conditional batch normalization. We evaluate performances on the tasks of visual question answering, few-shot learning, and conditional image generation.

2019-07-31

ArXiv (preprint)

Combined Reinforcement Learning via Abstract Representations

Vincent Francois-Lavet

Yoshua Bengio

Joelle Pineau

In the quest for efficient and robust reinforcement learning methods, both model-free and model-based approaches offer advantages. In this p… (see more)aper we propose a new way of explicitly bridging both approaches via a shared low-dimensional learned encoding of the environment, meant to capture summarizing abstractions. We show that the modularity brought by this approach leads to good generalization while being computationally efficient, with planning happening in a smaller latent state space. In addition, this approach recovers a sufficient low-dimensional representation of the environment, which opens up new strategies for interpretable AI, exploration and transfer learning.

2019-07-17

Proceedings of the AAAI Conference on Artificial Intelligence (published)

Learning Options with Interest Functions

Khimya Khetarpal

Learning temporal abstractions which are partial solutions to a task and could be reused for solving other tasks is an ingredient that can h… (see more)elp agents to plan and learn efficiently. In this work, we tackle this problem in the options framework. We aim to autonomously learn options which are specialized in different state space regions by proposing a notion of interest functions, which generalizes initiation sets from the options framework for function approximation. We build on the option-critic framework to derive policy gradient theorems for interest functions, leading to a new interest-option-critic architecture.

2019-07-17

Proceedings of the AAAI Conference on Artificial Intelligence (published)

Leveraging Observations in Bandits: Between Risks and Benefits

Andrei-Stefan Lupu

Audrey Durand

Imitation learning has been widely used to speed up learning in novice agents, by allowing them to leverage existing data from experts. Allo… (see more)wing an agent to be influenced by external observations can benefit to the learning process, but it also puts the agent at risk of following sub-optimal behaviours. In this paper, we study this problem in the context of bandits. More specifically, we consider that an agent (learner) is interacting with a bandit-style decision task, but can also observe a target policy interacting with the same environment. The learner observes only the target’s actions, not the rewards obtained. We introduce a new bandit optimism modifier that uses conditional optimism contingent on the actions of the target in order to guide the agent’s exploration. We analyze the effect of this modification on the well-known Upper Confidence Bound algorithm by proving that it preserves a regret upper-bound of order O(lnT), even in the presence of a very poor target, and we derive the dependency of the expected regret on the general target policy. We provide empirical results showing both great benefits as well as certain limitations inherent to observational learning in the multi-armed bandit setting. Experiments are conducted using targets satisfying theoretical assumptions with high probability, thus narrowing the gap between theory and application.

2019-07-17

Proceedings of the AAAI Conference on Artificial Intelligence (published)

Prediction of Disease Progression in Multiple Sclerosis Patients using Deep Learning Analysis of MRI Data

Adrian Tousignant

Paul Lemaitre