Pierre-Luc Bacon

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 64

Top Topics

Markov Decision Processes

Reinforcement Learning

Policy Gradient

Credit Assignment

Top Venues

Publications

Sobhan Mohammadpour, Emma Frejinger, Pierre-Luc Bacon
Decoupling regularization from the action space. CoRR (2024)
Michel Ma, Tianwei Ni, Clement Gehring, Pierluca D'Oro, Pierre-Luc Bacon
Do Transformer World Models Give Better Policy Gradients? CoRR (2024)
Sobhan Mohammadpour, Emmanuel Bengio, Emma Frejinger, Pierre-Luc Bacon
Maximum entropy GFlowNets with soft Q-learning. AISTATS (2024)
Sobhan Mohammadpour, Emma Frejinger, Pierre-Luc Bacon
Decoupling regularization from the action space. ICLR (2024)
Nikolaus H. R. Howe, Michal Zajac, Ian R. McKenzie, Oskar Hollinsworth, Tom Tseng, Pierre-Luc Bacon, Adam Gleave
Exploring Scaling Trends in LLM Robustness. CoRR (2024)
Simon Dufort-Labbé, Pierluca D'Oro, Evgenii Nikishin, Razvan Pascanu, Pierre-Luc Bacon, Aristide Baratin
Maxwell's Demon at Work: Efficient Pruning by Leveraging Saturation of Neurons. CoRR (2024)
Martin Klissarov, Pierluca D'Oro, Shagun Sodhani, Roberta Raileanu, Pierre-Luc Bacon, Pascal Vincent, Amy Zhang, Mikael Henaff
Motif: Intrinsic Motivation from Artificial Intelligence Feedback. ICLR (2024)
Mahan Fathi, Clement Gehring, Jonathan Pilault, David Kanaa, Pierre-Luc Bacon, Ross Goroshin
Course Correcting Koopman Representations. ICLR (2024)
Maksym Korablyov, Cheng-Hao Liu, Moksh Jain, Almer M. van der Sloot, Eric Jolicoeur, Edward Ruediger, Andrei Cristian Nica, Emmanuel Bengio, Kostiantyn Lapchevskyi, Daniel St-Cyr, Doris Alexandra Schuetz, Victor Ion Butoi, Jarrid Rector-Brooks, Simon Blackburn, Leo Feng, Hadi Nekoei, Sai Krishna Gottipati, Priyesh Vijayan, Prateek Gupta, Ladislav Rampásek, Sasikanth Avancha, Pierre-Luc Bacon, William L. Hamilton, Brooks Paige, Sanchit Misra, Stanislaw Kamil Jastrzebski, Bharat Kaul, Doina Precup, José Miguel Hernández-Lobato, Marwin H. S. Segler, Michael M. Bronstein, Anne Marinier, Mike Tyers, Yoshua Bengio
Generative Active Learning for the Search of Small-molecule Protein Binders. CoRR (2024)
Tianwei Ni, Benjamin Eysenbach, Erfan Seyedsalehi, Michel Ma, Clement Gehring, Aditya Mahajan, Pierre-Luc Bacon
Bridging State and History Representations: Understanding Self-Predictive RL. ICLR (2024)
Tianwei Ni, Benjamin Eysenbach, Erfan Seyedsalehi, Michel Ma, Clement Gehring, Aditya Mahajan, Pierre-Luc Bacon
Bridging State and History Representations: Understanding Self-Predictive RL. CoRR (2024)
David Yu-Tung Hui, Aaron C. Courville, Pierre-Luc Bacon
Double Gumbel Q-Learning. NeurIPS (2023)
Tianwei Ni, Michel Ma, Benjamin Eysenbach, Pierre-Luc Bacon
When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment. CoRR (2023)
Pierluca D'Oro, Max Schwarzer, Evgenii Nikishin, Pierre-Luc Bacon, Marc G. Bellemare, Aaron C. Courville
Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier. ICLR (2023)
Jonathan Pilault, Mahan Fathi, Orhan Firat, Chris Pal, Pierre-Luc Bacon, Ross Goroshin
Block-State Transformers. NeurIPS (2023)
Martin Klissarov, Pierluca D'Oro, Shagun Sodhani, Roberta Raileanu, Pierre-Luc Bacon, Pascal Vincent, Amy Zhang, Mikael Henaff
Motif: Intrinsic Motivation from Artificial Intelligence Feedback. CoRR (2023)
Tianwei Ni, Michel Ma, Benjamin Eysenbach, Pierre-Luc Bacon
When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment. NeurIPS (2023)
Mahan Fathi, Clement Gehring, Jonathan Pilault, David Kanaa, Pierre-Luc Bacon, Ross Goroshin
Course Correcting Koopman Representations. CoRR (2023)
Nate Rahn, Pierluca D'Oro, Harley Wiltzer, Pierre-Luc Bacon, Marc G. Bellemare
Policy Optimization in a Noisy Neighborhood: On Return Landscapes in Continuous Control. NeurIPS (2023)
Mahan Fathi, Jonathan Pilault, Pierre-Luc Bacon, Christopher Pal, Orhan Firat, Ross Goroshin
Block-State Transformer. CoRR (2023)
Sobhan Mohammadpour, Emmanuel Bengio, Emma Frejinger, Pierre-Luc Bacon
Maximum entropy GFlowNets with soft Q-learning. CoRR (2023)
Julien Roy, Pierre-Luc Bacon, Christopher Pal, Emmanuel Bengio
Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Design. CoRR (2023)
Nate Rahn, Pierluca D'Oro, Harley Wiltzer, Pierre-Luc Bacon, Marc G. Bellemare
Policy Optimization in a Noisy Neighborhood: On Return Landscapes in Continuous Control. CoRR (2023)
Evgenii Nikishin, Max Schwarzer, Pierluca D'Oro, Pierre-Luc Bacon, Aaron C. Courville
The Primacy Bias in Deep Reinforcement Learning. ICML (2022)
Evgenii Nikishin, Romina Abachi, Rishabh Agarwal, Pierre-Luc Bacon
Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation. AAAI (2022)
Leo Feng, Padideh Nouri, Aneri Muni, Yoshua Bengio, Pierre-Luc Bacon
Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization. CoRR (2022)
Nikolaus H. R. Howe, Simon Dufort-Labbé, Nitarshan Rajkumar, Pierre-Luc Bacon
Myriad: a real-world testbed to bridge trajectory optimization and deep learning. NeurIPS (2022)
Tristan Deleu, David Kanaa, Leo Feng, Giancarlo Kerg, Yoshua Bengio, Guillaume Lajoie, Pierre-Luc Bacon
Continuous-Time Meta-Learning with Forward Mode Differentiation. CoRR (2022)
Nikolaus H. R. Howe, Simon Dufort-Labbé, Nitarshan Rajkumar, Pierre-Luc Bacon
Myriad: a real-world testbed to bridge trajectory optimization and deep learning. CoRR (2022)
Julien Roy, Roger Girgis, Joshua Romoff, Pierre-Luc Bacon, Christopher J. Pal
Direct Behavior Specification via Constrained Reinforcement Learning. ICML (2022)
Evgenii Nikishin, Max Schwarzer, Pierluca D'Oro, Pierre-Luc Bacon, Aaron C. Courville
The Primacy Bias in Deep Reinforcement Learning. CoRR (2022)
Tristan Deleu, David Kanaa, Leo Feng, Giancarlo Kerg, Yoshua Bengio, Guillaume Lajoie, Pierre-Luc Bacon
Continuous-Time Meta-Learning with Forward Mode Differentiation. ICLR (2022)
Andreea Deac, Petar Velickovic, Ognjen Milinkovic, Pierre-Luc Bacon, Jian Tang, Mladen Nikolic
Neural Algorithmic Reasoners are Implicit Planners. CoRR (2021)
Dilip Arumugam, Peter Henderson, Pierre-Luc Bacon
An Information-Theoretic Perspective on Credit Assignment in Reinforcement Learning. CoRR (2021)
Andreea Deac, Petar Velickovic, Ognjen Milinkovic, Pierre-Luc Bacon, Jian Tang, Mladen Nikolic
Neural Algorithmic Reasoners are Implicit Planners. NeurIPS (2021)
Julien Roy, Roger Girgis, Joshua Romoff, Pierre-Luc Bacon, Christopher J. Pal
Direct Behavior Specification via Constrained Reinforcement Learning. CoRR (2021)
Joshua Romoff, Peter Henderson, David Kanaa, Emmanuel Bengio, Ahmed Touati, Pierre-Luc Bacon, Joelle Pineau
TDprop: Does Adaptive Optimization With Jacobi Preconditioning Help Temporal Difference Learning? AAMAS (2021)
Evgenii Nikishin, Romina Abachi, Rishabh Agarwal, Pierre-Luc Bacon
Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation. CoRR (2021)
Jean Harb, Tom Schaul, Doina Precup, Pierre-Luc Bacon
Policy Evaluation Networks. CoRR (2020)
Khimya Khetarpal, Martin Klissarov, Maxime Chevalier-Boisvert, Pierre-Luc Bacon, Doina Precup
Options of Interest: Temporal Abstraction with Interest Functions. CoRR (2020)
Yao Liu, Pierre-Luc Bacon, Emma Brunskill
Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling. ICML (2020)
Andreea Deac, Pierre-Luc Bacon, Jian Tang
Graph neural induction of value iteration. CoRR (2020)
Andreea Deac, Petar Velickovic, Ognjen Milinkovic, Pierre-Luc Bacon, Jian Tang, Mladen Nikolic
XLVIN: eXecuted Latent Value Iteration Nets. CoRR (2020)
Joshua Romoff, Peter Henderson, David Kanaa, Emmanuel Bengio, Ahmed Touati, Pierre-Luc Bacon, Joelle Pineau
TDprop: Does Jacobi Preconditioning Help Temporal Difference Learning? CoRR (2020)
Khimya Khetarpal, Martin Klissarov, Maxime Chevalier-Boisvert, Pierre-Luc Bacon, Doina Precup
Options of Interest: Temporal Abstraction with Interest Functions. AAAI (2020)
Benjamin Petit, Loren Amdahl-Culleton, Yao Liu, Jimmy Smith, Pierre-Luc Bacon
All-Action Policy Gradient Methods: A Numerical Integration Approach. CoRR (2019)
Riashat Islam, Raihan Seraj, Pierre-Luc Bacon, Doina Precup
Entropy Regularization with Discounted Future State Distribution in Policy Gradient Methods. CoRR (2019)
Yao Liu, Pierre-Luc Bacon, Emma Brunskill
Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling. CoRR (2019)
Tom Schaul, Hado van Hasselt, Joseph Modayil, Martha White, Adam White, Pierre-Luc Bacon, Jean Harb, Shibl Mourad, Marc G. Bellemare, Doina Precup
The Barbados 2018 List of Open Issues in Continual Learning. CoRR (2018)
Daniel J. Mankowitz, Timothy A. Mann, Pierre-Luc Bacon, Doina Precup, Shie Mannor
Learning Robust Options. CoRR (2018)
Ahmed Touati, Pierre-Luc Bacon, Doina Precup, Pascal Vincent
Convergent TREE BACKUP and RETRACE with Function Approximation. ICML (2018)
Anna Harutyunyan, Peter Vrancx, Pierre-Luc Bacon, Doina Precup, Ann Nowé
Learning With Options That Terminate Off-Policy. AAAI (2018)
Pierre-Luc Bacon, Doina Precup
Constructing Temporal Abstractions Autonomously in Reinforcement Learning. AI Mag. 39 (1) (2018)
Peter Henderson, Wei-Di Chang, Pierre-Luc Bacon, David Meger, Joelle Pineau, Doina Precup
OptionGAN: Learning Joint Reward-Policy Options Using Generative Adversarial Inverse Reinforcement Learning. AAAI (2018)
Daniel J. Mankowitz, Timothy A. Mann, Pierre-Luc Bacon, Doina Precup, Shie Mannor
Learning Robust Options. AAAI (2018)
Jean Harb, Pierre-Luc Bacon, Martin Klissarov, Doina Precup
When Waiting Is Not an Option: Learning Options With a Deliberation Cost. AAAI (2018)
Pierre-Luc Bacon, Jean Harb, Doina Precup
The Option-Critic Architecture. AAAI (2017)
Peter Henderson, Wei-Di Chang, Pierre-Luc Bacon, David Meger, Joelle Pineau, Doina Precup
OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learning. CoRR (2017)
Jean Harb, Pierre-Luc Bacon, Martin Klissarov, Doina Precup
When Waiting is not an Option : Learning Options with a Deliberation Cost. CoRR (2017)
Anna Harutyunyan, Peter Vrancx, Pierre-Luc Bacon, Doina Precup, Ann Nowé
Learning with Options that Terminate Off-Policy. CoRR (2017)
Martin Klissarov, Pierre-Luc Bacon, Jean Harb, Doina Precup
Learnings Options End-to-End for Continuous Action Tasks. CoRR (2017)
Ahmed Touati, Pierre-Luc Bacon, Doina Precup, Pascal Vincent
Convergent Tree-Backup and Retrace with Function Approximation. CoRR (2017)
Pierre-Luc Bacon, Jean Harb, Doina Precup
The Option-Critic Architecture. CoRR (2016)
Pierre-Luc Bacon, Doina Precup
A Matrix Splitting Perspective on Planning with Options. CoRR (2016)
Joelle Pineau, Pierre-Luc Bacon
Analyzing Open Data from the City of Montreal. MUD@ICML (2015)
Emmanuel Bengio, Pierre-Luc Bacon, Joelle Pineau, Doina Precup
Conditional Computation in Neural Networks for faster models. CoRR (2015)
Pierre-Luc Bacon, Borja Balle, Doina Precup
Learning and Planning with Timing Information in Markov Decision Processes. UAI (2015)