  In many emerging Internet of Things (IoT) applications, the freshness of the
is an important design criterion. Age of Information (AoI) quantifies the
freshness of the received information or status update. This work considers a
setup of deployed IoT devices in an IoT network; multiple unmanned aerial
vehicles (UAVs) serve as mobile relay nodes between the sensors and the base
station. We formulate an optimization problem to jointly plan the UAVs'
trajectory, while minimizing the AoI of the received messages and the devices'
energy consumption. The solution accounts for the UAVs' battery lifetime and
flight time to recharging depots to ensure the UAVs' green operation. The
complex optimization problem is efficiently solved using a deep reinforcement
learning algorithm. In particular, we propose a deep Q-network, which works as
a function approximation to estimate the state-action value function. The
proposed scheme is quick to converge and results in a lower ergodic age and
ergodic energy consumption when compared with benchmark algorithms such as
greedy algorithm (GA), nearest neighbour (NN), and random-walk (RW).
