Pierre-Luc Bacon
Appointment
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
About
Appointed Canada CIFAR AI Chair – 2020
Renewed Canada CIFAR AI Chair – 2025
Pierre-Luc Bacon is an Assistant Professor at the University of Montreal, a Canada CIFAR AI Chair and a member of Mila and IVADO. He specializes in developing practical deep reinforcement learning methods through the lens of representation learning and optimization techniques. He applies these methods to real-world challenges in health, environmental sustainability and drug design, collaborating with industry to enhance AI’s practical impact. His recent work focuses on learning world models in continuous time, employing decision-aware methods, and investigating the synergy between sequence modeling and reinforcement learning to tackle the curse of horizon.
Awards
- Outstanding Student Paper Award, Association for the Advancement of AI, 2017
- Best Paper Award, Hierarchical Reinforcement Learning Workshop, Neural Information Processing Systems, 2017
Relevant Publications
- The Primacy Bias in Deep Reinforcement Learning. Evgenii Nikishin, Max Schwarzer, Pierluca D'Oro, Pierre-Luc Bacon, Aaron Courville. ICML 2022 and RLDM 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. NeurIPS 2022 Datasets and Benchmarks
- Continuous-Time Meta-Learning with Forward Mode Differentiation. Tristan Deleu, David Kanaa, Leo Feng, Giancarlo Kerg, Yoshua Bengio, Guillaume Lajoie, Pierre-Luc Bacon. ICLR, 2022.
- Direct Behavior Specification via Constrained Reinforcement Learning. Julien Roy, Roger Girgis, Joshua Romoff, Pierre-Luc Bacon, Christopher Pal. ICML 2022