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Unmanned-aerial-vehicle (UAV)-assisted data collection has been emerging as a prominent application due to its flexibility, mobility, and low operational cost. However, under the dynamic and uncertainty of Internet of Things data collection and energy replenishment processes, optimizing the performance for UAV collectors is a very challenging task. Thus, this article introduces a novel framework that jointly optimizes the flying speed and energy replenishment for each UAV to significantly improve the overall system performance (e.g., data collection and energy usage efficiency). Specifically, we first develop a Markov decision process to help the UAV automatically and dynamically make optimal decisions under the dynamics and uncertainties of the environment. Although traditional reinforcement learning algorithms, such as -learning and deep -learning, can help the UAV to obtain the optimal policy, they …