  Data packet routing in aeronautical ad-hoc networks (AANETs) is challenging
due to their high-dynamic topology. In this paper, we invoke deep reinforcement
learning for routing in AANETs aiming at minimizing the end-to-end (E2E) delay.
Specifically, a deep Q-network (DQN) is conceived for capturing the
relationship between the optimal routing decision and the local geographic
information observed by the forwarding node. The DQN is trained in an offline
manner based on historical flight data and then stored by each airplane for
assisting their routing decisions during flight. To boost the learning
efficiency and the online adaptability of the proposed DQN-routing, we further
exploit the knowledge concerning the system's dynamics by using a deep value
network (DVN) conceived with a feedback mechanism. Our simulation results show
that both DQN-routing and DVN-routing achieve lower E2E delay than the
benchmark protocol, and DVN-routing performs similarly to the optimal routing
that relies on perfect global information.

Networking and Internet Architecture;Artificial Intelligence;Machine Learning