Glen Berseth

I am an assistant professor at the University de Montreal and Mila. My research explores how to use deep learning and reinforcement learning to develop generalist robots.

#UnsupervisedLearning Articles


SMiRL: Surprise Minimizing RL in Unstable Environments

Glen Berseth, Daniel Geng, Coline Devin, Nicholas Rhinehart, Chelsea Finn, Dinesh Jayaraman, Sergey Levine

All living organisms carve out environmental niches within which they can maintain relative predictability amidst the ever-increasing entropy around them [schneider1994, friston2009]. Humans, for example, go to great lengths to shield themselves from surprise --- we band together in millions to build cities with homes, supplying water, food, gas, and electricity to control the deterioration of our bodies and living spaces amidst heat and cold, wind and storm. The need to discover and maintain such surprise-free equilibria has driven great resourcefulness and skill in organisms across very diverse natural habitats. Motivated by this, we ask: could the motive of preserving order amidst chaos guide the automatic acquisition of useful behaviors in artificial agents?


Info

Glen Berseth

I am a PostDoc at the Berkeley Artificial Intelligence Research (BAIR) group working in the Robotic AI & Learning Lab (RAIL) lab with Sergey Levine. I received my PhD at the Department of Computer Science at the University of British Columbia in 2019 where I worked on reinforcement learning, machine learning and motion planning with Michiel van de Panne. I received my BSc degree in Computer Science from York University in 2012 and my MSc from York University under the supervision of Petros Faloutsos in 2014 for optimization and authoring crowd simulations. I have published in a wide range of areas including computer animation, machine learning and robotics and was an NSERC scholarship award winner. You can find a list of projects and publications here.