  Due to the advantages of flexible deployment and extensive coverage, unmanned
aerial vehicles (UAVs) have great potential for sensing applications in the
next generation of cellular networks, which will give rise to a cellular
Internet of UAVs. In this paper, we consider a cellular Internet of UAVs, where
the UAVs execute sensing tasks through cooperative sensing and transmission to
minimize the age of information (AoI). However, the cooperative sensing and
transmission is tightly coupled with the UAVs' trajectories, which makes the
trajectory design challenging. To tackle this challenge, we propose a
distributed sense-and-send protocol, where the UAVs determine the trajectories
by selecting from a discrete set of tasks and a continuous set of locations for
sensing and transmission. Based on this protocol, we formulate the trajectory
design problem for AoI minimization and propose a compound-action actor-critic
(CA2C) algorithm to solve it based on deep reinforcement learning. The CA2C
algorithm can learn the optimal policies for actions involving both continuous
and discrete variables and is suited for the trajectory design. {Our simulation
results show that the CA2C algorithm outperforms four baseline algorithms}.
Also, we show that by dividing the tasks, cooperative UAVs can achieve a lower
AoI compared to non-cooperative UAVs.
