  Metaverse is expected to create a virtual world closely connected with
reality to provide users with immersive experience with the support of 5G high
data rate communication technique. A huge amount of data in physical world
needs to be synchronized to the virtual world to provide immersive experience
for users, and there will be higher requirements on coverage to include more
users into Metaverse. However, 5G signal suffers severe attenuation, which
makes it more expensive to maintain the same coverage. Unmanned aerial vehicle
(UAV) is a promising candidate technique for future implementation of Metaverse
as a low-cost and high-mobility platform for communication devices. In this
paper, we propose a proximal policy optimization (PPO) based double-agent
cooperative reinforcement learning method for channel allocation and trajectory
control of UAV to collect and synchronize data from the physical world to the
virtual world, and expand the coverage of Metaverse services economically.
Simulation results show that our proposed method is able to achieve better
performance compared to the benchmark approaches.
