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Vision-based reinforcement learning (RL) methods enable efficient policy learning and adaptive decision-making for quadrotor uncrewed aerial vehicles (UAVs) navigation in complex, high-dimensional flight environments. Although end-to-end vision-based RL approaches are effective, they often function as closed-box models, lacking interpretability. We develop an explainable vision-based hierarchical RL algorithm for QUAV navigation, integrating perception, obstacle avoidance, and motion control into a unified framework. Due to the high-dimensional state space and complex dynamics of QUAV tasks, traditional RL methods often suffer from sparse and difficult-to-obtain rewards. To address this, we introduce the echoic hindsight experience replay mechanism, which accelerates convergence by transforming failed episodes into successful ones. Building on this, we propose an RL-based proportional-integral-derivative-retarded control method that leverages multirate measurements to enhance low-level control performance, improving maneuverability and precision in QUAV operations. Both simulated and real-world experiments demonstrate the effectiveness of our proposed method for UAV navigation in complex environments.