Pierre-Luc Bacon

According to our database, Pierre-Luc Bacon authored at least 43 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Maxwell's Demon at Work: Efficient Pruning by Leveraging Saturation of Neurons.

Simon Dufort-Labbé, Pierluca D'Oro, Evgenii Nikishin, Razvan Pascanu, Pierre-Luc Bacon, Aristide Baratin

CoRR, 2024

Do Transformer World Models Give Better Policy Gradients?

Michel Ma, Tianwei Ni, Clement Gehring, Pierluca D'Oro, Pierre-Luc Bacon

CoRR, 2024

Bridging State and History Representations: Understanding Self-Predictive RL.

Tianwei Ni, Benjamin Eysenbach, Erfan Seyedsalehi, Michel Ma, Clement Gehring, Aditya Mahajan, Pierre-Luc Bacon

CoRR, 2024

2023

Maximum entropy GFlowNets with soft Q-learning.

Sobhan Mohammadpour, Emmanuel Bengio, Emma Frejinger, Pierre-Luc Bacon

CoRR, 2023

Course Correcting Koopman Representations.

Mahan Fathi, Clement Gehring, Jonathan Pilault, David Kanaa, Pierre-Luc Bacon, Ross Goroshin

CoRR, 2023

Motif: Intrinsic Motivation from Artificial Intelligence Feedback.

Martin Klissarov, Pierluca D'Oro, Shagun Sodhani, Roberta Raileanu, Pierre-Luc Bacon, Pascal Vincent, Amy Zhang, Mikael Henaff

CoRR, 2023

Block-State Transformer.

Mahan Fathi, Jonathan Pilault, Pierre-Luc Bacon, Christopher Pal, Orhan Firat, Ross Goroshin

CoRR, 2023

Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Design.

Julien Roy, Pierre-Luc Bacon, Christopher Pal, Emmanuel Bengio

CoRR, 2023

Policy Optimization in a Noisy Neighborhood: On Return Landscapes in Continuous Control.

Nate Rahn, Pierluca D'Oro, Harley Wiltzer, Pierre-Luc Bacon, Marc G. Bellemare

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Block-State Transformers.

Jonathan Pilault, Mahan Fathi, Orhan Firat, Chris Pal, Pierre-Luc Bacon, Ross Goroshin

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment.

Tianwei Ni, Michel Ma, Benjamin Eysenbach, Pierre-Luc Bacon

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Double Gumbel Q-Learning.

David Yu-Tung Hui, Aaron C. Courville, Pierre-Luc Bacon

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier.

Pierluca D'Oro, Max Schwarzer, Evgenii Nikishin, Pierre-Luc Bacon, Marc G. Bellemare, Aaron C. Courville

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization.

Leo Feng, Padideh Nouri, Aneri Muni, Yoshua Bengio, Pierre-Luc Bacon

CoRR, 2022

Myriad: a real-world testbed to bridge trajectory optimization and deep learning.

Nikolaus H. R. Howe, Simon Dufort-Labbé, Nitarshan Rajkumar, Pierre-Luc Bacon

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Direct Behavior Specification via Constrained Reinforcement Learning.

Julien Roy, Roger Girgis, Joshua Romoff, Pierre-Luc Bacon, Christopher J. Pal

Proceedings of the International Conference on Machine Learning, 2022

The Primacy Bias in Deep Reinforcement Learning.

Evgenii Nikishin, Max Schwarzer, Pierluca D'Oro, Pierre-Luc Bacon, Aaron C. Courville

Proceedings of the International Conference on Machine Learning, 2022

Continuous-Time Meta-Learning with Forward Mode Differentiation.

Tristan Deleu, David Kanaa, Leo Feng, Giancarlo Kerg, Yoshua Bengio, Guillaume Lajoie, Pierre-Luc Bacon

Proceedings of the Tenth International Conference on Learning Representations, 2022

Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation.

Evgenii Nikishin, Romina Abachi, Rishabh Agarwal, Pierre-Luc Bacon

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

An Information-Theoretic Perspective on Credit Assignment in Reinforcement Learning.

Dilip Arumugam, Peter Henderson, Pierre-Luc Bacon

CoRR, 2021

Neural Algorithmic Reasoners are Implicit Planners.

Andreea Deac, Petar Velickovic, Ognjen Milinkovic, Pierre-Luc Bacon, Jian Tang, Mladen Nikolic

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

TDprop: Does Adaptive Optimization With Jacobi Preconditioning Help Temporal Difference Learning?

Joshua Romoff, Peter Henderson, David Kanaa, Emmanuel Bengio, Ahmed Touati, Pierre-Luc Bacon, Joelle Pineau

Proceedings of the AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems, 2021

2020

XLVIN: eXecuted Latent Value Iteration Nets.

Andreea Deac, Petar Velickovic, Ognjen Milinkovic, Pierre-Luc Bacon, Jian Tang, Mladen Nikolic

CoRR, 2020

Graph neural induction of value iteration.

Andreea Deac, Pierre-Luc Bacon, Jian Tang

CoRR, 2020

TDprop: Does Jacobi Preconditioning Help Temporal Difference Learning?

Joshua Romoff, Peter Henderson, David Kanaa, Emmanuel Bengio, Ahmed Touati, Pierre-Luc Bacon, Joelle Pineau

CoRR, 2020

Policy Evaluation Networks.

Jean Harb, Tom Schaul, Doina Precup, Pierre-Luc Bacon

CoRR, 2020

Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling.

Yao Liu, Pierre-Luc Bacon, Emma Brunskill

Proceedings of the 37th International Conference on Machine Learning, 2020

Options of Interest: Temporal Abstraction with Interest Functions.

Khimya Khetarpal, Martin Klissarov, Maxime Chevalier-Boisvert, Pierre-Luc Bacon, Doina Precup

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Entropy Regularization with Discounted Future State Distribution in Policy Gradient Methods.

Riashat Islam, Raihan Seraj, Pierre-Luc Bacon, Doina Precup

CoRR, 2019

All-Action Policy Gradient Methods: A Numerical Integration Approach.

Benjamin Petit, Loren Amdahl-Culleton, Yao Liu, Jimmy Smith, Pierre-Luc Bacon

CoRR, 2019

2018

The Barbados 2018 List of Open Issues in Continual Learning.

Tom Schaul, Hado van Hasselt, Joseph Modayil, Martha White, Adam White, Pierre-Luc Bacon, Jean Harb, Shibl Mourad, Marc G. Bellemare, Doina Precup

CoRR, 2018

Constructing Temporal Abstractions Autonomously in Reinforcement Learning.

Pierre-Luc Bacon, Doina Precup

AI Mag., 2018

Convergent TREE BACKUP and RETRACE with Function Approximation.

Ahmed Touati, Pierre-Luc Bacon, Doina Precup, Pascal Vincent

Proceedings of the 35th International Conference on Machine Learning, 2018

Learning Robust Options.

Daniel J. Mankowitz, Timothy A. Mann, Pierre-Luc Bacon, Doina Precup, Shie Mannor

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

Learning With Options That Terminate Off-Policy.

Anna Harutyunyan, Peter Vrancx, Pierre-Luc Bacon, Doina Precup, Ann Nowé

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

When Waiting Is Not an Option: Learning Options With a Deliberation Cost.

Jean Harb, Pierre-Luc Bacon, Martin Klissarov, Doina Precup

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

OptionGAN: Learning Joint Reward-Policy Options Using Generative Adversarial Inverse Reinforcement Learning.

Peter Henderson, Wei-Di Chang, Pierre-Luc Bacon, David Meger, Joelle Pineau, Doina Precup

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Learnings Options End-to-End for Continuous Action Tasks.

Martin Klissarov, Pierre-Luc Bacon, Jean Harb, Doina Precup

CoRR, 2017

The Option-Critic Architecture.

Pierre-Luc Bacon, Jean Harb, Doina Precup

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

A Matrix Splitting Perspective on Planning with Options.

Pierre-Luc Bacon, Doina Precup

CoRR, 2016

2015

Conditional Computation in Neural Networks for faster models.

Emmanuel Bengio, Pierre-Luc Bacon, Joelle Pineau, Doina Precup

CoRR, 2015

Learning and Planning with Timing Information in Markov Decision Processes.

Pierre-Luc Bacon, Borja Balle, Doina Precup

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Analyzing Open Data from the City of Montreal.

Joelle Pineau, Pierre-Luc Bacon

Proceedings of the 2nd International Workshop on Mining Urban Data co-located with 32nd International Conference on Machine Learning (ICML 2015), 2015