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Reinforcement Learning for Avionics Applications

Award Information

Agency:
Department of Defense
Branch:
Air Force
Award ID:
36220
Program Year/Program:
1997 / SBIR
Agency Tracking Number:
36220
Solicitation Year:
N/A
Solicitation Topic Code:
N/A
Solicitation Number:
N/A
Small Business Information
Barron Associates, Inc.
1410 Sachem Place Suite 202 Charlottesville, VA 22901-2496
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Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No
 
Phase 1
Fiscal Year: 1997
Title: Reinforcement Learning for Avionics Applications
Agency / Branch: DOD / USAF
Contract: N/A
Award Amount: $97,943.00
 

Abstract:

Simulation-based optimization techniques that enlist reinforcement learning controllers are ideally suited for complex and multi-objective optimization problems that cannot be solved easily using traditional techniques, especially when the stochastic natures or the environment, resources, and external interactive entities are taken into account. Reinforcement learning based on incremental value iteration may be unstable when sequential system updates are close together and when general compact function approximators are required. Residual methods can preclude these problems. The proposed work will investigate and refine residual reinforcement learning techniques suitable for high-dimensional systems with many complex subsystem interactions and characterized by an aggregation of continuous, discrete, logical-element and binary states. The work shall demonstrate, via simulation, residual reinforcement learning solutions for providing optimal detection, classification, and prioritization of multiple targets through uninhabited air vehicle (UAV) intelligent sensor allocation strategies and flight path optimizations. In addition to immediate benefits for the U.S. Air Force and its UAV research, the proposed work will result in reinforcement learning methods that are directly applicable to numerous military and commercial-sector systems, including navigation and trajectory optimization, commercial air-traffic control, and general simulation-based optimization techniques for complex systems.

Principal Investigator:

David G. Ward/jeffrey F.
8049731215

Business Contact:

Small Business Information at Submission:

Barron Assoc., Inc.
3046A Berkmar Drive Charlottesville, VA 22901

EIN/Tax ID:
DUNS: N/A
Number of Employees:
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No