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2025 Best Warrior Competition

 

BALTIMORE – Fourteen competitors from throughout the Army National Guard are slated to battle it out in a physically and mentally challenging five-day competition to determine the Army Guard’s Soldier and Noncommissioned Officer of the year July 14-18 in locations throughout Maryland. 

Hosted by the Maryland Army National Guard the 2025 Army National Guard Best Warrior Competition tests competitors on a variety of tactical and technical tasks including weapons proficiency, land navigation, emergency medical tasks, and combat casualty care. These tasks are completed over a grueling set of courses throughout the state that includes multiple timed ruck marches and the Army Combat Fitness Test.

Winners in the competition – one Soldier and one NCO – are named the Army Guard’s Soldier and NCO of the Year and will compete in the Department of the Army Best Squad Competition this fall. Runners-up in the Best Warrior Competition fill out the Army Guard squad competing in the Best Squad Competition based on their finish in Best Warrior.

Competitors in this year’s Army Guard Best Warrior Competition include:

Soldier Category
Spc. Adam Andrews - Rhode Island 
Spc. Robert Ruiz-Rhoades – Pennsylvania 
Spc. Jaden Hughes - Alabama 
Spc. Logan Rutledge – Indiana
Spc. Alexander Thomson – Nebraska
Spc. Canyon Blassingame - Montana
Sgt. Michael Fouts – Arizona


NCO Category
Sgt. Kristopher Piwowarczyk - New Jersey 
Staff Sgt. Miles Crawford – Maryland 
Staff Sgt. Nicolas White – Georgia 
Staff Sgt. Brandon Byrne - Wisconsin
Sgt. Luke Entz – Nebraska
Sgt. Matthew Lee – Montana
Sgt. Luke Cloward - Utah

 

Video Gallery
Video by Kevin D Schmidt
Dr. Raj Sharma and Jasper Craig
Air Force Research Laboratory
Sept. 15, 2023 | 55:05
Description:
In this edition of QuEST, Dr. Raj Sharma and Jasper Craig with the SAFE Autonomy Team in ACT3 will discuss their work on implementation of reinforcement learning (RL) in a simulated environment.

Key Moments in the video include:
Quadrotor Dynamics
Differential flatness
Linear Quadratic Controller (LQR)
Formation Consensus
Reinforcement Learning (RL) for Trajectory Control
Benefits of LQR and RL
Future Work

Audience questions:
Kinematics that you used for this control - does that change for battery-powered or certainly gas-powered weight of the craft changes? How do people model that, or is it necessary to model that?
Would there be a place in that matrix you were describing a moment ago to account for some of those things?
Does every agent or node have perfect knowledge of the leader?
What mechanisms were used to work with Kerianne’s group - Summer Faculty Fellowship?
More