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Employing unmanned aerial vehicles (UAVs) has attracted growing interests andemerged as the state-of-the-art technology for data collection inInternet-of-Things (IoT) networks. In this paper, with the objective ofminimizing the total energy consumption of the UAV-IoT system, we formulate theproblem of jointly designing the UAV's trajectory and selecting cluster headsin the IoT network as a constrained combinatorial optimization problem which isclassified as NP-hard and challenging to solve. We propose a novel deepreinforcement learning (DRL) with a sequential model strategy that caneffectively learn the policy represented by a sequence-to-sequence neuralnetwork for the UAV's trajectory design in an unsupervised manner. Throughextensive simulations, the obtained results show that the proposed DRL methodcan find the UAV's trajectory that requires much less energy consumption whencompared to other baseline algorithms and achieves close-to-optimalperformance. In addition, simulation results show that the trained model by ourproposed DRL algorithm has an excellent generalization ability to largerproblem sizes without the need to retrain the model.

Joint Cluster Head Selection and Trajectory Planning in UAV-Aided IoT Networks by Reinforcement Learning with Sequential Model