Canaries and Whistles: Resilient Drone Communication Networks with (or without) Deep Reinforcement 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 symbolic technique and study its superiority over deep reinforcement learning agents. Correspondingly, we identify three specific methods for improving the performance of our neural agents: (1) ensuring each observation contains the necessary information, (2) using symbolic 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 symbolic and neural agents to work together and improve on all prior results.