He is also a principal investigator in the Reinforcement Learning and Artificial Intelligence (RLAI) Lab and leader of the Computer Poker Research Group – both at the University of Alberta. Michael is a Fellow and Canada CIFAR AI Chair at Amii, a full professor at the University of Alberta and a Research Scientist at DeepMind in Edmonton, AB. The Arcade Learning Environment was instrumental in establishing the subfield of deep reinforcement learning. In leading the development of the Arcade Learning Environment, which launched in 2013, Michael played a pivotal role in the adoption of Atari as a key challenge problem and testbed for AI researchers across the world. Both systems represent theoretical leaps forward in the world of imperfect (or hidden) information games. He is best known for his work in poker, most notably on two milestone advances, both published in Science: Cepheus ‘essentially’ solved the game of heads-up limit Texas hold’em in 2015, and in late 2016, DeepStack became the first AI to beat human professionals at heads-up no-limit Texas hold’em. Michael Bowling is fascinated by the problem of how computers can learn to play games through experience.
