  In modern cell-less wireless networks, mobility management is undergoing a
significant transformation, transitioning from single-link handover management
to a more adaptable multi-connectivity cluster reconfiguration approach,
including often conflicting objectives like energy-efficient power allocation
and satisfying varying reliability requirements. In this work, we address the
challenge of dynamic clustering and power allocation for unmanned aerial
vehicle (UAV) communication in wireless interference networks. Our objective
encompasses meeting varying reliability demands, minimizing power consumption,
and reducing the frequency of cluster reconfiguration. To achieve these
objectives, we introduce a novel approach based on reinforcement learning using
a masked soft actor-critic algorithm, specifically tailored for dynamic
clustering and power allocation.
