Deep reinforcement learning for UAV-assisted emergency response

In the aftermath of a disaster, the ability to reliably communicate and coordinate emergency response could make a meaningful difference in the number of lives saved or lost. However, post-disaster areas tend to have limited functioning communication network infrastructure while emergency response teams are carrying increasingly more devices, such as sensors and video transmitting equipment, which can be low-powered with limited transmission ranges. In such scenarios, unmanned aerial vehicles (UAVs) can be used as relays to connect these devices with each other. Since first responders are likely to be constantly mobile, the problem of where these UAVs are placed and how they move in response to the changing environment could have a large effect on the number of connections this UAV relay network is able to maintain. In this work, we propose DroneDR, a reinforcement learning framework for UAV positioning that uses information about connectivity requirements and user node positions to decide how to move each UAV in the network while maintaining connectivity between UAVs. The proposed approach is shown to outperform other greedy heuristics across a broad range of scenarios and demonstrates the potential in using reinforcement learning techniques to aid communication during disaster relief operations.