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Julie Parish
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2020 – today
- 2024
- [j4]Ali K. Raz
, Kshitij Mall
Explainable AI and Robustness-Based Test and Evaluation of Reinforcement Learning. IEEE Trans. Aerosp. Electron. Syst. 60(5): 6110-6123 (2024) - 2023
- [j3]Joseph L. Hart
Sensitivity-Driven Experimental Design to Facilitate Control of Dynamical Systems. J. Optim. Theory Appl. 196(3): 855-881 (2023) - [j2]Kyle R. Williams
Trajectory Planning With Deep Reinforcement Learning in High-Level Action Spaces. IEEE Trans. Aerosp. Electron. Syst. 59(3): 2513-2529 (2023) - [c3]Christian T. Coletti, Kyle A. Williams, Hannah C. Lehman, Zahi M. Kakish
Effectiveness of Warm-Start PPO for Guidance with Highly Constrained Nonlinear Fixed-Wing Dynamics. ACC 2023: 3288-3295 - 2022
- [j1]Zachary C. Goddard
Utilizing Reinforcement Learning to Continuously Improve a Primitive-Based Motion Planner. J. Aerosp. Inf. Syst. 19(7): 468-479 (2022) - 2021
- [c2]Kyle R. Williams, J. Justin Wilbanks, Rachel Schlossman, David Kozlowski, Julie Parish:
Dakota and Pyomo for Closed and Open Box Controller Gain Tuning. CDC 2021: 4854-4860 - [c1]Manan Gandhi, Rachel Schlossman, Kyle A. Williams, Ryan Melzer, Julie Parish:
CUDA for Rapid Controller Robustness Evaluation: A Tutorial. CDC 2021: 4861-4867 - [i1]Kyle R. Williams, Rachel Schlossman, Daniel Whitten, Joe Ingram, Srideep Musuvathy, Anirudh Patel, James Pagan, Kyle A. Williams, Sam Green, Anirban Mazumdar, Julie Parish:
Trajectory Planning with Deep Reinforcement Learning in High-Level Action Spaces. CoRR abs/2110.00044 (2021)
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