Doina Precup

Samin Yeasar Arnob

PhD - McGill University

Sumana Basu

Collaborating Alumni - McGill University

Co-supervisor :

Adriana Romero Soriano

Collaborating Alumni - McGill University

Raymond Chua

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

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

Jonathan Hu

Master's Research - McGill University

Howard Huang

PhD - McGill University

Haque Ishfaq

Collaborating Alumni - McGill University

Mohammad Sami Nur Islam Islam

Master's Research - McGill University

Hangzhan Jin

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 :

Research Intern - McGill University

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

Research Intern - McGill University

Zihan Wang

PhD - McGill University

Skipper: Combining Spatial and Temporal Abstraction for Better Generalization

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

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Publications

A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms

Philip Amortila

Prakash Panangaden

Bellemare Marc-Emmanuel

We present a distributional approach to theoretical analyses of reinforcement learning algorithms for constant step-sizes. We demonstrate it… (see more)s effectiveness by presenting simple and unified proofs of convergence for a variety of commonly-used methods. We show that value-based methods such as TD(

2019-12-31

AISTATS (published)

proceedings.mlr.press

On Efficiency in Hierarchical Reinforcement Learning

Zheng Wen

Morteza Ibrahimi

Andre Barreto

Benjamin Van Roy

Satinder Singh

Hierarchical Reinforcement Learning (HRL) approaches promise to provide more efﬁcient solutions to sequential decision making problems, bo… (see more)th in terms of statistical as well as computational efﬁciency. While this has been demonstrated empirically over time in a variety of tasks, theoretical results quantifying the ben-eﬁts of such methods are still few and far between. In this paper, we discuss the kind of structure in a Markov decision process which gives rise to efﬁcient HRL methods. Speciﬁcally, we formalize the intuition that HRL can exploit well repeating "subMDPs", with similar reward and transition structure. We show that, under reasonable assumptions, a model-based Thompson sampling-style HRL algorithm that exploits this structure is statistically efﬁcient, as established through a ﬁnite-time regret bound. We also establish conditions under which planning with structure-induced options is near-optimal and computationally efﬁcient.

2019-12-31

Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (published)

dblp.uni-trier.de

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

2019-12-31

International Conference on Artificial Intelligence and Statistics (published)

proceedings.mlr.press

An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay

Scott Fujimoto

David Meger

Prioritized Experience Replay (PER) is a deep reinforcement learning technique in which agents learn from transitions sampled with non-unifo… (see more)rm probability proportionate to their temporal-difference error. We show that any loss function evaluated with non-uniformly sampled data can be transformed into another uniformly sampled loss function with the same expected gradient. Surprisingly, we find in some environments PER can be replaced entirely by this new loss function without impact to empirical performance. Furthermore, this relationship suggests a new branch of improvements to PER by correcting its uniformly sampled loss function equivalent. We demonstrate the effectiveness of our proposed modifications to PER and the equivalent loss function in several MuJoCo and Atari environments.

2019-12-31

Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (published)

Forethought and Hindsight in Credit Assignment

Veronica Chelu

Hado van Hasselt

We address the problem of credit assignment in reinforcement learning and explore fundamental questions regarding the way in which an agent … (see more)can best use additional computation to propagate new information, by planning with internal models of the world to improve its predictions. Particularly, we work to understand the gains and peculiarities of planning employed as forethought via forward models or as hindsight operating with backward models. We establish the relative merits, limitations and complementary properties of both planning mechanisms in carefully constructed scenarios. Further, we investigate the best use of models in planning, primarily focusing on the selection of states in which predictions should be (re)-evaluated. Lastly, we discuss the issue of model estimation and highlight a spectrum of methods that stretch from explicit environment-dynamics predictors to more abstract planner-aware models.

2019-12-31

Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (published)

Reward Propagation Using Graph Convolutional Networks

Martin Klissarov

Potential-based reward shaping provides an approach for designing good reward functions, with the purpose of speeding up learning. However, … (see more)automatically finding potential functions for complex environments is a difficult problem (in fact, of the same difficulty as learning a value function from scratch). We propose a new framework for learning potential functions by leveraging ideas from graph representation learning. Our approach relies on Graph Convolutional Networks which we use as a key ingredient in combination with the probabilistic inference view of reinforcement learning. More precisely, we leverage Graph Convolutional Networks to perform message passing from rewarding states. The propagated messages can then be used as potential functions for reward shaping to accelerate learning. We verify empirically that our approach can achieve considerable improvements in both small and high-dimensional control problems.

2019-12-31

NeurIPS (published)

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.

2019-12-31

(published)

www.semanticscholar.org

Value Preserving State-Action Abstractions

David Abel

Nathan Umbanhowar

Khimya Khetarpal

Dilip Arumugam

Michael L. Littman

2019-12-31

International Conference on Artificial Intelligence and Statistics (published)

proceedings.mlr.press

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.

2019-12-31

Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (published)

openreview.net

Assessment of Extubation Readiness Using Spontaneous Breathing Trials in Extremely Preterm Neonates

Wissam Shalish

Lara Kanbar

Lajos Kovacs

Sanjay Chawla

Martin Keszler

Smita Rao

Samantha Latremouille

Karen Brown

Robert E. Kearney

Guilherme M. Sant’Anna

Importance Spontaneous breathing trials (SBTs) are used to determine extubation readiness in extremely preterm neonates (gestational age ≤… (see more)28 weeks), but these trials rely on empirical combinations of clinical events during endotracheal continuous positive airway pressure (ET-CPAP). Objectives To describe clinical events during ET-CPAP and to assess accuracy of comprehensive clinical event combinations in predicting successful extubation compared with clinical judgment alone. Design, Setting, and Participants This multicenter diagnostic study used data from 259 neonates seen at 5 neonatal intensive care units from the prospective Automated Prediction of Extubation Readiness (APEX) study from September 1, 2013, through August 31, 2018. Neonates with birth weight less than 1250 g who required mechanical ventilation were eligible. Neonates deemed to be ready for extubation and who underwent ET-CPAP before extubation were included. Interventions In the APEX study, cardiorespiratory signals were recorded during 5-minute ET-CPAP, and signs of clinical instability were monitored. Main Outcomes and Measures Four clinical events were documented during ET-CPAP: apnea requiring stimulation, presence and cumulative durations of bradycardia and desaturation, and increased supplemental oxygen. Clinical event occurrence was assessed and compared between extubation pass and fail (defined as reintubation within 7 days). An automated algorithm was developed to generate SBT definitions using all clinical event combinations and to compute diagnostic accuracies of an SBT in predicting extubation success. Results Of 259 neonates (139 [54%] male) with a median gestational age of 26.1 weeks (interquartile range [IQR], 24.9-27.4 weeks) and median birth weight of 830 g (IQR, 690-1019 g), 147 (57%) had at least 1 clinical event during ET-CPAP. Apneas occurred in 10% (26 of 259) of neonates, bradycardias in 19% (48), desaturations in 53% (138), and increased oxygen needs in 41% (107). Neonates with successful extubation (71% [184 of 259]) had significantly fewer clinical events (51% [93 of 184] vs 72% [54 of 75], P = .002), shorter cumulative bradycardia duration (median, 0 seconds [IQR, 0 seconds] vs 0 seconds [IQR, 0-9 seconds], P .001), shorter cumulative desaturation duration (median, 0 seconds [IQR, 0-59 seconds] vs 25 seconds [IQR, 0-90 seconds], P = .003),

2019-12-19

JAMA Pediatrics (published)

Shaping representations through communication: community size effect in artificial learning systems

Olivier Tieleman

Angeliki Lazaridou

Shibl Mourad

Charles Blundell

Motivated by theories of language and communication that explain why communities with large numbers of speakers have, on average, simpler la… (see more)nguages with more regularity, we cast the representation learning problem in terms of learning to communicate. Our starting point sees the traditional autoencoder setup as a single encoder with a fixed decoder partner that must learn to communicate. Generalizing from there, we introduce community-based autoencoders in which multiple encoders and decoders collectively learn representations by being randomly paired up on successive training iterations. We find that increasing community sizes reduce idiosyncrasies in the learned codes, resulting in representations that better encode concept categories and correlate with human feature norms.

2019-12-11

ArXiv (preprint)

Entropy Regularization with Discounted Future State Distribution in Policy Gradient Methods

The policy gradient theorem is defined based on an objective with respect to the initial distribution over states. In the discounted case, t… (see more)his results in policies that are optimal for one distribution over initial states, but may not be uniformly optimal for others, no matter where the agent starts from. Furthermore, to obtain unbiased gradient estimates, the starting point of the policy gradient estimator requires sampling states from a normalized discounted weighting of states. However, the difficulty of estimating the normalized discounted weighting of states, or the stationary state distribution, is quite well-known. Additionally, the large sample complexity of policy gradient methods is often attributed to insufficient exploration, and to remedy this, it is often assumed that the restart distribution provides sufficient exploration in these algorithms. In this work, we propose exploration in policy gradient methods based on maximizing entropy of the discounted future state distribution. The key contribution of our work includes providing a practically feasible algorithm to estimate the normalized discounted weighting of states, i.e, the \textit{discounted future state distribution}. We propose that exploration can be achieved by entropy regularization with the discounted state distribution in policy gradients, where a metric for maximal coverage of the state space can be based on the entropy of the induced state distribution. The proposed approach can be considered as a three time-scale algorithm and under some mild technical conditions, we prove its convergence to a locally optimal policy. Experimentally, we demonstrate usefulness of regularization with the discounted future state distribution in terms of increased state space coverage and faster learning on a range of complex tasks.

2019-12-10

ArXiv (preprint)