  Many emerging Internet of Things (IoT) applications rely on information
collected by sensor nodes where the freshness of information is an important
criterion. \textit{Age of Information} (AoI) is a metric that quantifies
information timeliness, i.e., the freshness of the received information or
status update. This work considers a setup of deployed sensors in an IoT
network, where 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. This ensures that the received information at the base
station is as fresh as possible. The complex optimization problem is
efficiently solved using a deep reinforcement learning (DRL) 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 AoI than the random walk scheme.
Our proposed algorithm reduces the average age by approximately $25\%$ and
requires down to $50\%$ less energy when compared to the baseline scheme.
