1. Canaries and Whistles: Resilient Drone Communication Networks with (or without) Deep Reinforcement Learning
- Author
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Hicks, Chris, Mavroudis, Vasilios, Foley, Myles, Davies, Thomas, Highnam, Kate, and Watson, Tim
- Subjects
Computer Science - Cryptography and Security ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
Communication networks able to withstand hostile environments are critically important for disaster relief operations. In this paper, we consider a challenging scenario where drones have been compromised in the supply chain, during their manufacture, and harbour malicious software capable of wide-ranging and infectious disruption. We investigate multi-agent deep reinforcement learning as a tool for learning defensive strategies that maximise communications bandwidth despite continual adversarial interference. Using a public challenge for learning network resilience strategies, we propose a state-of-the-art expert technique and study its superiority over deep reinforcement learning agents. Correspondingly, we identify three specific methods for improving the performance of our learning-based agents: (1) ensuring each observation contains the necessary information, (2) using expert agents to provide a curriculum for learning, and (3) paying close attention to reward. We apply our methods and present a new mixed strategy enabling expert and learning-based agents to work together and improve on all prior results., Comment: Published in AISec '23. This version fixes some terminology to improve readability
- Published
- 2023
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