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5G and beyond networks need to provide dynamic and efficient infrastructuremanagement to better adapt to time-varying user behaviors (e.g., user mobility,interference, user traffic and evolution of the network topology). In thispaper, we propose to manage the trajectory of Mobile Access Points (MAPs) underall these dynamic constraints with reduced complexity. We first formulate theplacement problem to manage MAPs over time. Our solution addresses time-varyinguser traffic and user mobility through a Multi-Agent Deep ReinforcementLearning (MADRL). To achieve real-time behavior, the proposed solution learnsto perform distributed assignment of MAP-user positions and schedules the MAPpath among all users without centralized user's clustering feedback. Oursolution exploits a dual-attention MADRL model via proximal policy optimizationto dynamically move MAPs in 3D. The dual-attention takes into accountinformation from both users and MAPs. The cooperation mechanism of our solutionallows to manage different scenarios, without a priory information and withoutre-training, which significantly reduces complexity.

Dual-Attention Deep Reinforcement Learning for Multi-MAP 3D Trajectory Optimization in Dynamic 5G Networks