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Adversarial Attacks on Reinforcement Learning Agents for Command and Control
May 6, 2024, 4:11 a.m. | Ahaan Dabholkar, James Z. Hare, Mark Mittrick, John Richardson, Nicholas Waytowich, Priya Narayanan, Saurabh Bagchi
cs.CR updates on arXiv.org arxiv.org
Abstract: Given the recent impact of Deep Reinforcement Learning in training agents to win complex games like StarCraft and DoTA(Defense Of The Ancients) - there has been a surge in research for exploiting learning based techniques for professional wargaming, battlefield simulation and modeling. Real time strategy games and simulators have become a valuable resource for operational planning and military research. However, recent work has shown that such learning based approaches are highly susceptible to adversarial perturbations. …
adversarial adversarial attacks agents arxiv attacks battlefield command command and control control cs.cr defense exploiting games impact modeling professional real real time research simulation strategy techniques training
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