About Me
I'm a Machine Learning and Reinforcement Learning researcher currently working on games. I have a BS in Physics and an MS in Computer Science both from Indiana University Bloomington. My PhD work was focused on reinforcement learning, and specifically in understanding how agents may perceive their world. I am experienced in applying reinforcement learning, imitation learning, and traditional machine learning to a wide array of applications, identifying problems with off-the-shelf approaches, and developing learning methods to improve agent behavior. Currently, I am interested in rethinking industrial control problems for the new world of data driven control algorithms. I deeply believe reinforcement learning has the capability to solve hard control problems not approachable by traditional control algorithms.
Recently Published papers
Offline Reinforcement Learning via Tsallis Regularization. Lingwei Zhu, Matthew Kyle Schlegel, Han Wang, Martha White. Transactions on Machine Learning Research, 2024.
General Munchausen Reinforcement Learning with Tsallis Kullback-Leibler Divergence. Lingwei Zhu, Zheng Chen, Matthew Schlegel, Martha White. Advances in Neural Information Processing Systems, 2023.
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.
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.