  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.
