About Me

I'm a PhD Candidate at University of Alberta in Edmonton, Alberta, Canada. I have a BS in Physics and an MS in Computer Science both from Indiana University Bloomington. My current PhD work is focused on reinforcement learning, and specifically in understanding how agents may perceive their world. I focus primarily on prediction making, but have been known to dabble in control from time-to-time. My active research interests include: predictions as a component in intelligence (both artificial and biological), off-policy prediction and policy evaluation, deep learning and resulting learned representations in the reinforcement learning context, and discovery or attention of important abstractions (described as predictions) through interaction.

Recently Published papers

Investigating Action Encodings in Recurrent Neural Networks in Reinforcement Learning. Matthew Schlegel, Volodmyr Tkachuk, Adam White, Martha White. Transactions on Machine Learning Research, 2022.

General Value Function Networks. Matthew Schlegel, Andrew Jacobsen, Zaheer Abbas, Andrew Patterson, Adam White, Martha White. Journal of Artificial Intelligence Research, 2021.

Continual Auxiliary Task Learning. Matthew McLeod, Chunlok Lo, Matthew Schlegel, Andrew Jacobsen, Raksha Kumaraswamy, Martha White, Adam White. Advances in Neural Information Processing Systems, 2021.

Structural Credit Assignment in Neural Networks using Reinforcement Learning. Dhawal Gupta, Gabor Mihucz, Matthew Schlegel, James Kostas, Philip S Thomas, Martha White. Advances in Neural Information Processing Systems, 2021.

Importance Resampling for Off-policy Prediction. Matthew Schlegel, Wesley Chung, Daniel Graves, Jian Qian, Martha White. Advances in Neural Information Processing Systems 32, 2019.