Marc G. Bellemare
Appointment
Associate Fellow
Canada CIFAR AI Chair
Learning in Machines & Brains
Pan-Canadian AI Strategy
About
Appointed Canada CIFAR AI Chair – 2019
Renewed Canada CIFAR AI Chair – 2024
Marc G. Bellemare is a Canada CIFAR AI Chair at Mila, a CIFAR associate fellow of the Learning in Machines & Brains program, an adjunct professor at the School of Computer Science at McGill University, and Chief Scientific Officer at Reliant AI, a Montréal-Berlin startup specializing in reinforcement learning for data.
Bellemare’s research lies at the intersection of reinforcement learning and generative modelling. His work spans both theoretical and practical contributions, including distributional reinforcement learning, a theory of exploration in high-dimensional state spaces, the development of the highly-successful Arcade Learning Environment for evaluating artificial agents, and applied deep reinforcement learning. He previously led the design of reinforcement learning systems to control Loon’s fleet of stratospheric balloons. He now studies the application of reinforcement learning to large language models and representation learning for large-scale reinforcement learning.
Awards
- Best Paper Award, Advances in Neural Processing Systems, 2021
- Best Paper Award, ICML Exploration in Reinforcement Learning Workshop, 2019
- Best Paper Award, Reinforcement Learning and Decision-Making Symposium (RLDM), 2019
Relevant Publications
- Bellemare, M., Dabney, W., Rowland, M. (2023). Distributional reinforcement learning. MIT Press.
- D'Oro, P., Schwarzer, M., Nikishin, E., Bacon, P., Bellemare, M., Courville, A. (2022). Sample-efficient reinforcement learning by breaking the replay ratio barrier.
- Agarwal, R., Schwarzer, M., Castro P. S., Courville A., Bellemare, Marc G. (2021). Deep reinforcement learning at the edge of the statistical precipice. Advances in neural information processing systems.
- Bellemare M. G., Candido S., Castro P. S., Gong J., Machado M. C., Moitra S., Ponda S. S., Wang Z. (2020). Autonomous navigation of stratospheric balloons using reinforcement learning. Nature.
François-Lavet, V., Henderson, P., Islam, R., Bellemare, M. G., & Pineau, J. (2018). An introduction to deep reinforcement learning.
Bellemare, M. G., Dabney, W., & Munos, R. (2017). A distributional perspective on reinforcement learning. In International Conference on Machine Learning (pp. 449-458). PMLR.
Bellemare, M., Srinivasan, S., Ostrovski, G., Schaul, T., Saxton, D., & Munos, R. (2016). Unifying count-based exploration and intrinsic motivation. Advances in neural information processing systems, 29, 1471-1479.