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X DRLSGT

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: HQ0860-23-C-7511
Agency Tracking Number: B22B-T004-0167
Amount: $149,823.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: MDA22-T004
Solicitation Number: 22.B
Timeline
Solicitation Year: 2022
Award Year: 2023
Award Start Date (Proposal Award Date): 2022-11-23
Award End Date (Contract End Date): 2023-05-22
Small Business Information
4075 Wilson Blvd, STE 800
Arlington, VA 22203-1798
United States
DUNS: 080709338
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Dylan Miller
 (856) 630-8984
 dylan.miller@cynnovative.com
Business Contact
 Matt Puglisi
Phone: (410) 533-3817
Email: admin@cynnovative.com
Research Institution
 Georgia Institute of Technology
 Katirce Green
 
926 Dalney ST NW
Atlanta, GA 30332
United States

 (404) 385-2077
 Nonprofit College or University
Abstract

Cynnovative proposes Explainable Deep Reinforcement Learning with Symbolically Guided Transitions (X DRLSGT) to improve the transparency and, thus, the explainability of deep reinforcement learning (DRL) algorithms. The inability to understand the reasoning behind an Artificial Intelligence’s (AI) decision is a major limiting factor that prevents AI-enabled physical systems from being deployed alongside humans. This is especially true for the warfighter and analysts who must regularly manage high volumes of information at any given time and who could benefit significantly from working with an AI, which could process large amounts of information quickly and expose only the necessary elements to the operator. Our proposed approach will improve explainability in DRL by extracting meaningful and transparent information from the model that the agent uses to reason about the world. We will accomplish this by leveraging a model-based reinforcement learning algorithm that learns to represent the world in a way that can be used to ultimately derive meaningful information from the mind of the agent. This will provide insight into how the agent sees the world and how it expects the world to change. Approved for Public Release | 22-MDA-11339 (13 Dec 22)

* Information listed above is at the time of submission. *

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