Marc G. Bellemare

Marc G. Bellemare


  • Fellow
  • Learning in Machines & Brains


  • McGill University
  • Google Brain


  • Canada


PhD Computing Science, University of Alberta
MSc Computer Science, McGill University
BSc Honours Computer Science, McGill University


Marc G. Bellemare's research lies at the intersection of reinforcement learning and statistical prediction.

His work spans both theoretical and practical contributions, including a novel distributional treatment of 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 work in deep reinforcement learning. His long-term goal is the design of generally competent agents: agents that can successfully operate in a wide range of environments and eventually exhibit the gamut of behaviour that we attribute to humans: curiosity, boredom, competence, and emergent communication, to name a few.

Relevant Publications

Bellemare, M., J. Veness and M. Bowling. "The Arcade Learning Environment: An Evaluation Platform for General Agents." Journal of Artificial Intelligence Research (2013).
Mnih, V. et al. "Human-level control through deep reinforcement learning." Nature (2015).

Bellemare, M.*, W. Dabney* and R. Munos. "A distributional perspective on reinforcement learning." ICML, 2017.

Bellemare, M., S. Srinivasan, G. Ostrovski, T. Schaul, D. Saxton and R. Munos. "Unifying count-based exploration and intrinsic motivation." Artificial Intelligence (2016).