Aaron Courville

Alan Alan

PhD - Université de Montréal

Principal supervisor :

PhD - Université de Montréal

Principal supervisor :

Laurent Charlin

Juan Duque

PhD - Université de Montréal

PhD - Université de Montréal

Arian Hosseini

PhD - Université de Montréal

Uday Kapur

PhD - Université de Montréal

Amr Khalifa

PhD - Université de Montréal

Samuel Lavoie

PhD - Université de Montréal

Zhixuan Lin

PhD - Université de Montréal

PhD - Université de Montréal

Principal supervisor :

PhD - Université de Montréal

PhD - Université de Montréal

Co-supervisor :

Rishabh Agarwal

Andrei Nicolicioiu

PhD - Université de Montréal

Michael Noukhovitch

PhD - Université de Montréal

Johan Samir Obando Ceron

PhD - Université de Montréal

Co-supervisor :

Collaborating researcher - Université de Montréal

Dereck Piché

PhD - Université de Montréal

Khaled Rouissi

Master's Research - Université de Montréal

Esra'a Saleh

PhD - Université de Montréal

Principal supervisor :

Glen Berseth

Vedant Shah

PhD - Université de Montréal

PhD - Université de Montréal

Yusong Wu

PhD - Université de Montréal

Principal supervisor :

Anna (Cheng-Zhi) Huang

Sujin yun

PhD - Université de Montréal

Xiaofeng Zhang

PhD - Université de Montréal

Publications

Bigger, Better, Faster: Human-level Atari with human-level efficiency

Max Schwarzer

Johan Obando-Ceron

Bellemare Marc-Emmanuel

Rishabh Agarwal

Pablo Samuel Castro

We introduce a value-based RL agent, which we call BBF, that achieves super-human performance in the Atari 100K benchmark. BBF relies on sca… (see more)ling the neural networks used for value estimation, as well as a number of other design choices that enable this scaling in a sample-efficient manner. We conduct extensive analyses of these design choices and provide insights for future work. We end with a discussion about updating the goalposts for sample-efficient RL research on the ALE. We make our code and data publicly available at https://github.com/google-research/google-research/tree/master/bigger_better_faster.

2023-07-02

Proceedings of the 40th International Conference on Machine Learning (published)

proceedings.mlr.press

Learning with Learning Awareness using Meta-Values

Tim Cooijmans

Milad Aghajohari

2023-06-18

ICML.cc/2023/Workshop/Frontiers4LCD (published)

Generative Augmented Flow Networks

Longbo Huang

The Generative Flow Network is a probabilistic framework where an agent learns a stochastic policy for object generation, such that the prob… (see more)ability of generating an object is proportional to a given reward function. Its effectiveness has been shown in discovering high-quality and diverse solutions, compared to reward-maximizing reinforcement learning-based methods. Nonetheless, GFlowNets only learn from rewards of the terminal states, which can limit its applicability. Indeed, intermediate rewards play a critical role in learning, for example from intrinsic motivation to provide intermediate feedback even in particularly challenging sparse reward tasks. Inspired by this, we propose Generative Augmented Flow Networks (GAFlowNets), a novel learning framework to incorporate intermediate rewards into GFlowNets. We specify intermediate rewards by intrinsic motivation to tackle the exploration problem in sparse reward environments. GAFlowNets can leverage edge-based and state-based intrinsic rewards in a joint way to improve exploration. Based on extensive experiments on the GridWorld task, we demonstrate the effectiveness and efficiency of GAFlowNet in terms of convergence, performance, and diversity of solutions. We further show that GAFlowNet is scalable to a more complex and large-scale molecule generation domain, where it achieves consistent and significant performance improvement.

2023-01-31

International Conference on Learning Representations (notable)

Investigating Multi-Task Pretraining and Generalization in Reinforcement Learning

Bellemare Marc-Emmanuel

Google Brain

2023-01-31

ICLR.cc/2023/Conference (poster)

Latent State Marginalization as a Low-Cost Approach for Improving Exploration

Qinqing Zheng

Ricky T. Q. Chen

While the maximum entropy (MaxEnt) reinforcement learning (RL) framework -- often touted for its exploration and robustness capabilities -- … (see more)is usually motivated from a probabilistic perspective, the use of deep probabilistic models has not gained much traction in practice due to their inherent complexity. In this work, we propose the adoption of latent variable policies within the MaxEnt framework, which we show can provably approximate any policy distribution, and additionally, naturally emerges under the use of world models with a latent belief state. We discuss why latent variable policies are difficult to train, how naive approaches can fail, then subsequently introduce a series of improvements centered around low-cost marginalization of the latent state, allowing us to make full use of the latent state at minimal additional cost. We instantiate our method under the actor-critic framework, marginalizing both the actor and critic. The resulting algorithm, referred to as Stochastic Marginal Actor-Critic (SMAC), is simple yet effective. We experimentally validate our method on continuous control tasks, showing that effective marginalization can lead to better exploration and more robust training. Our implementation is open sourced at https://github.com/zdhNarsil/Stochastic-Marginal-Actor-Critic.

2023-01-31

ICLR.cc/2023/Conference (poster)

Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier

Bellemare Marc-Emmanuel

2023-01-31

ICLR.cc/2023/Conference (notable)

Simplicial Embeddings in Self-Supervised Learning and Downstream Classification

Samuel Lavoie

Simplicial Embeddings (SEM) are representations learned through self-supervised learning (SSL), wherein a representation is projected into …

2023-01-31

ICLR.cc/2023/Conference (notable)

Noisy Pairing and Partial Supervision for Stylized Opinion Summarization

Reinald Kim

Mirella Lapata. 2020

Un-611

David M. Krueger

Maxinder S. Kan-620

Somnath Basu

Roy Chowdhury

Chao Zhao

Tanya Goyal

Junyi Jiacheng Xu

Jessy Li

Ivor W. Tsang

James T. Kwok

Neil Houlsby

Andrei Giurgiu

Stanisław Jastrzębski … (see 22 more)

Bruna Morrone

Quentin de Laroussilhe

Mona Gesmundo

Attariyan Sylvain

Gelly

Thomas Wolf

Lysandre Debut

Julien Victor Sanh

Clement Chaumond

Anthony Delangue

Pier-339 Moi

Tim ric Cistac

R´emi Rault

Morgan Louf

Funtow-900 Joe

Sam Davison

Patrick Shleifer

Von Platen

Clara Ma

Yacine Jernite

Julien Plu

Canwen Xu

Opinion summarization research has primar-001 ily focused on generating summaries reflect-002 ing important opinions from customer reviews 0… (see more)03 without paying much attention to the writing 004 style. In this paper, we propose the stylized 005 opinion summarization task, which aims to 006 generate a summary of customer reviews in 007 the desired (e.g., professional) writing style. 008 To tackle the difficulty in collecting customer 009 and professional review pairs, we develop a 010 non-parallel training framework, Noisy Pair-011 ing and Partial Supervision ( NAPA ), which 012 trains a stylized opinion summarization sys-013 tem from non-parallel customer and profes-014 sional review sets. We create a benchmark P RO - 015 S UM by collecting customer and professional 016 reviews from Yelp and Michelin. Experimental 017 results on P RO S UM and FewSum demonstrate 018 that our non-parallel training framework con-019 sistently improves both automatic and human 020 evaluations, successfully building a stylized 021 opinion summarization model that can gener-022 ate professionally-written summaries from cus-023 tomer reviews. 024

2022-12-31

(published)

www.semanticscholar.org

SUNMASK: Mask Enhanced Control in Step Unrolled Denoising Autoencoders

2022-12-31

EvoMUSART@EvoStar (published)

Versatile Energy-Based Probabilistic Models for High Energy Physics

Taoli Cheng

As a classical generative modeling approach, energy-based models have the natural advantage of flexibility in the form of the energy functio… (see more)n. Recently, energy-based models have achieved great success in modeling high-dimensional data in computer vision and natural language processing. In line with these advancements, we build a multi-purpose energy-based probabilistic model for High Energy Physics events at the Large Hadron Collider. This framework builds on a powerful generative model and describes higher-order inter-particle interactions. It suits different encoding architectures and builds on implicit generation. As for applicative aspects, it can serve as a powerful parameterized event generator for physics simulation, a generic anomalous signal detector free from spurious correlations, and an augmented event classifier for particle identification.

2022-12-31

arXiv.org (preprint)

Unleashing The Potential of Data Sharing in Ensemble Deep Reinforcement Learning

This work studies a crucial but often overlooked element of ensemble methods in deep reinforcement learning: data sharing between ensemble m… (see more)embers. We show that data sharing enables peer learning, a powerful learning process in which individual agents learn from each other's experience to significantly improve their performance. When given access to the experience of other ensemble members, even the worst agent can match or outperform the previously best agent, triggering a virtuous circle. However, we show that peer learning can be unstable when the agents' ability to learn is impaired due to overtraining on early data. We thus employ the recently proposed solution of periodic resets and show that it ensures effective peer learning. We perform extensive experiments on continuous control tasks from both dense states and pixels to demonstrate the strong effect of peer learning and its interaction with resets.

2022-12-08

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