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This paper proposes a novel design on the wireless powered communicationnetwork (WPCN) in dynamic environments under the assistance of multipleunmanned aerial vehicles (UAVs). Unlike the existing studies, where thelow-power wireless nodes (WNs) often conform to the coherentharvest-then-transmit protocol, under our newly proposed double-threshold basedWN type updating rule, each WN can dynamically and repeatedly update its WNtype as an E-node for non-linear energy harvesting over time slots or an I-nodefor transmitting data over sub-slots. To maximize the total transmission datasize of all the WNs over T slots, each of the UAVs individually determines itstrajectory and binary wireless energy transmission (WET) decisions over timesslots and its binary wireless data collection (WDC) decisions over sub-slots,under the constraints of each UAV's limited on-board energy and each WN's nodetype updating rule. However, due to the UAVs' tightly-coupled trajectories withtheir WET and WDC decisions, as well as each WN's time-varying battery energy,this problem is difficult to solve optimally. We then propose a new multi-agentbased hierarchical deep reinforcement learning (MAHDRL) framework with twotiers to solve the problem efficiently, where the soft actor critic (SAC)policy is designed in tier-1 to determine each UAV's continuous trajectory andbinary WET decision over time slots, and the deep-Q learning (DQN) policy isdesigned in tier-2 to determine each UAV's binary WDC decisions over sub-slotsunder the given UAV trajectory from tier-1. Both of the SAC policy and the DQNpolicy are executed distributively at each UAV. Finally, extensive simulationresults are provided to validate the outweighed performance of the proposedMAHDRL approach over various state-of-the-art benchmarks.

On Designing Multi-UAV aided Wireless Powered Dynamic Communication via Hierarchical Deep Reinforcement Learning