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This paper investigates a master unmanned aerial vehicle (MUAV)-poweredInternet of Things (IoT) network, in which we propose using a rechargeableauxiliary UAV (AUAV) equipped with an intelligent reflecting surface (IRS) toenhance the communication signals from the MUAV and also leverage the MUAV as arecharging power source. Under the proposed model, we investigate the optimalcollaboration strategy of these energy-limited UAVs to maximize the accumulatedthroughput of the IoT network. Depending on whether there is charging betweenthe two UAVs, two optimization problems are formulated. To solve them, twomulti-agent deep reinforcement learning (DRL) approaches are proposed, whichare centralized training multi-agent deep deterministic policy gradient(CT-MADDPG) and multi-agent deep deterministic policy option critic (MADDPOC).It is shown that the CT-MADDPG can greatly reduce the requirement on thecomputing capability of the UAV hardware, and the proposed MADDPOC is able tosupport low-level multi-agent cooperative learning in the continuous actiondomains, which has great advantages over the existing option-based hierarchicalDRL that only support single-agent learning and discrete actions.

Optimization for Master-UAV-powered Auxiliary-Aerial-IRS-assisted IoT  Networks: An Option-based Multi-agent Hierarchical Deep Reinforcement  Learning Approach