Canaries and Whistles: Resilient Drone Communication Networks with (or without) Deep Reinforcement Learning
This addresses network resilience for disaster relief operations, but is incremental as it builds on existing methods.
The paper tackled the problem of maintaining communication bandwidth in drone networks compromised by malicious software, by developing a mixed strategy combining expert techniques and deep reinforcement learning that improved on prior results.
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.