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Unmanned aerial vehicle (UAV)-assisted data collection has been emerging as aprominent application due to its flexibility, mobility, and low operationalcost. However, under the dynamic and uncertainty of IoT data collection andenergy replenishment processes, optimizing the performance for UAV collectorsis a very challenging task. Thus, this paper introduces a novel framework thatjointly optimizes the flying speed and energy replenishment for each UAV tosignificantly improve the data collection performance. Specifically, we firstdevelop a Markov decision process to help the UAV automatically and dynamicallymake optimal decisions under the dynamics and uncertainties of the environment.We then propose a highly-effective reinforcement learning algorithm leveragingdeep Q-learning, double deep Q-learning, and a deep dueling neural networkarchitecture to quickly obtain the UAV's optimal policy. The core ideas of thisalgorithm are to estimate the state values and action advantages separately andsimultaneously and to employ double estimators for estimating the actionvalues. Thus, these proposed techniques can stabilize the learning process andeffectively address the overestimation problem of conventional Q-learningalgorithms. To further reduce the learning time as well as significantlyimprove learning quality, we develop advanced transfer learning techniques toallow UAVs to ``share'' and ``transfer'' learning knowledge. Extensivesimulations demonstrate that our proposed solution can improve the average datacollection performance of the system up to 200% compared with those of currentmethods.

Joint Speed Control and Energy Replenishment Optimization for UAV-assisted IoT Data Collection with Deep Reinforcement Transfer Learning