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6 June 2022 Deep reinforcement learning to assist command and control
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Abstract
Multi-domain operations drastically increase the scale and speed required to generate, evaluate, and disseminate command and control (C2) directives. In this work we evaluate the effectiveness of using reinforcement learning (RL) within an Army C2 system to design an artificial intelligence (AI) agent that accelerates the commander and staff’s decision making process. Leveraging RL’s superior ability to explore and exploit produces novel strategies that widen a commander’s decision space without increasing cognitive burden. Integrating RL into an efficient course of action war-gaming simulator and training hundreds of thousands of simulated battles using the DoD supercomputing resources generated an AI that produces acceptable strategic actions during a simulated operation. Moreover, this approach played an unexpected but significant role in strengthening the underlying wargame simulation engine by discovering and exploiting weaknesses in its design. This highlights a future role for the use of RL to test and improve DoD systems during their development.
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Song Jun Park, Manuel M. Vindiola, Anne C. Logie, Priya Narayanan, and Jared Davies "Deep reinforcement learning to assist command and control", Proc. SPIE 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV, 121131D (6 June 2022); https://doi.org/10.1117/12.2618907
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KEYWORDS
Artificial intelligence

Computer simulations

Interfaces

Neural networks

Sensors

Weapons

Stochastic processes

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