Real-Time Stochastic Predictive Control for Hybrid Vehicle Energy Management. - Kyle R. Williams (CoRR, 2018)

Dakota and Pyomo for Closed and Open Box Controller Gain Tuning. - Kyle R. Williams; J. Justin Wilbanks; Rachel Schlossman; David Kozlowski; Julie Parish (CDC, 2021)

Utilizing Reinforcement Learning to Continuously Improve a Primitive-Based Motion Planner. - Zachary C. Goddard; Kenneth Wardlaw; Kyle R. Williams; Julie Parish; Anirban Mazumdar (J. Aerosp. Inf. Syst., 2022)

Selecting Minimal Motion Primitive Libraries with Genetic Algorithms. - Zachary C. Goddard; Kenneth Wardlaw; Kyle R. Williams; Anirban Mazumdar (J. Aerosp. Inf. Syst., 2023)

Trajectory Planning With Deep Reinforcement Learning in High-Level Action Spaces. - Kyle R. Williams; Rachel Schlossman; Daniel Whitten; Joe Ingram; Srideep Musuvathy; James Pagan; Kyle A. Williams; Sam Green; Anirudh Patel; Anirban Mazumdar; Julie Parish (IEEE Trans. Aerosp. Electron. Syst., 2023)

Leveraging Machine Learning to Improve Adaptive Primitive-Based Motion Planning. - Kevin S. Choi; Zachary C. Goddard; Samuel J. Deal; Anirban Mazumdar; Kyle R. Williams (J. Aerosp. Inf. Syst., 2024)

Leveraging Machine Learning for Generating and Utilizing Motion Primitives in Adversarial Environments. - Zachary C. Goddard; Rithesh Rajasekar; Madhumita Mocharla; Garrett Manaster; Kyle R. Williams; Anirban Mazumdar (J. Aerosp. Inf. Syst., 2024)

Explainable AI and Robustness-Based Test and Evaluation of Reinforcement Learning. - Ali K. Raz; Kshitij Mall; Sean Matthew Nolan; Winston Levin; Linas Mockus; Kristopher L. Ezra; Ahmad Mia; Kyle R. Williams; Julie Parish (IEEE Trans. Aerosp. Electron. Syst., 2024)