  5G and beyond networks need to provide dynamic and efficient infrastructure
management to better adapt to time-varying user behaviors (e.g., user mobility,
interference, user traffic and evolution of the network topology). In this
paper, we propose to manage the trajectory of Mobile Access Points (MAPs) under
all these dynamic constraints with reduced complexity. We first formulate the
placement problem to manage MAPs over time. Our solution addresses time-varying
user traffic and user mobility through a Multi-Agent Deep Reinforcement
Learning (MADRL). To achieve real-time behavior, the proposed solution learns
to perform distributed assignment of MAP-user positions and schedules the MAP
path among all users without centralized user's clustering feedback. Our
solution exploits a dual-attention MADRL model via proximal policy optimization
to dynamically move MAPs in 3D. The dual-attention takes into account
information from both users and MAPs. The cooperation mechanism of our solution
allows to manage different scenarios, without a priory information and without
re-training, which significantly reduces complexity.
