Skip to content
post_content

Marc G. Bellemare

Appointment

  • Associate Fellow
  • Canada CIFAR AI Chair
  • Learning in Machines & Brains

Connect

Website

About

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).

Support Us

CIFAR is a registered charitable organization supported by the governments of Canada, Alberta, Ontario, and Quebec as well as foundations, individuals, corporations, and international partner organizations.

MaRS Centre, West Tower
661 University Ave., Suite 505
Toronto, ON M5G 1M1 Canada