Canaries and Whistles: Resilient Drone Communication Networks with (or
  without) Deep Reinforcement Learning
Chris Hicks Vasilios Mavroudis Myles Foley Thomas Davies Kate Highnam Tim Watson
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.