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Kyle R. Williams
Person information
- affiliation: Sandia National Laboratories, Albuquerque, NM, USA
- affiliation (PhD 2018): Purdue University, West Lafayette, IN, USA
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2020 – today
- 2024
- [j4]Zachary C. Goddard
, Rithesh Rajasekar, Madhumita Mocharla, Garrett Manaster, Kyle R. Williams
, Anirban Mazumdar
:
Leveraging Machine Learning for Generating and Utilizing Motion Primitives in Adversarial Environments. J. Aerosp. Inf. Syst. 21(2): 127-139 (2024) - 2023
- [j3]Zachary C. Goddard
, Kenneth Wardlaw, Kyle R. Williams
, Anirban Mazumdar:
Selecting Minimal Motion Primitive Libraries with Genetic Algorithms. J. Aerosp. Inf. Syst. 20(10): 618-626 (2023) - [j2]Kyle R. Williams
, Rachel Schlossman, Daniel Whitten
, Joe Ingram, Srideep Musuvathy
, James Pagan, Kyle A. Williams, Sam Green, Anirudh Patel, Anirban Mazumdar
, Julie Parish:
Trajectory Planning With Deep Reinforcement Learning in High-Level Action Spaces. IEEE Trans. Aerosp. Electron. Syst. 59(3): 2513-2529 (2023) - 2022
- [j1]Zachary C. Goddard
, Kenneth Wardlaw, Kyle R. Williams
, Julie Parish, Anirban Mazumdar
:
Utilizing Reinforcement Learning to Continuously Improve a Primitive-Based Motion Planner. J. Aerosp. Inf. Syst. 19(7): 468-479 (2022) - 2021
- [c1]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 - [i2]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)
2010 – 2019
- 2018
- [i1]Kyle R. Williams:
Real-Time Stochastic Predictive Control for Hybrid Vehicle Energy Management. CoRR abs/1804.08766 (2018)
Coauthor Index
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last updated on 2024-05-02 21:48 CEST by the dblp team
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