Abstract: The rapid advancement of unmanned aerial vehicle (UAV) technology has underscored the significant potential of multi-UAV systems in managing complex tasks. Efficient task-allocation strategies are crucial for enhancing the overall performance of these systems. Although conventional methods perform adequately in simple environments, they often struggle in more complex scenarios where environmental disturbances and resource constraints hinder their effectiveness, resulting in suboptimal task allocation outcomes. By contrast, reinforcement learning (RL), as a powerful optimization technique, is particularly suitable for addressing the challenges inherent in multi-UAV task allocation. Unlike conventional approaches, RL does not rely on predefined models or external knowledge, enabling the system to learn optimal strategies via continuous interactions with the environment. This flexibility enables the system to adapt to dynamic conditions and improve its decision making over time. This study proposes an innovative approach based on deep reinforcement learning to address the challenges encountered in multi-UAV task allocation, with specific consideration given to the uncertainties typically prevalent in real-world battlefield scenarios. These uncertainties include variable wind conditions, precipitation, and other environmental factors that can potentially affect UAV performance. The primary objective of this study is to ensure that multi-UAV systems can respond rapidly to multiple simultaneous tasks while optimizing resource utilization. Traditional task allocation methods, which are often heuristic or rule-based, lack the flexibility required to handle environmental complexity or dynamic changes. They are typically rigid and struggle to adapt to unanticipated situations, which results in inefficiencies and delays in task allocation. To address these challenges, this study modeled the task allocation problem as a Markov Decision Process. In this framework, the system can select the most appropriate task allocation strategy based on the current state of the environment, ensuring flexibility and timeliness in decision making. To enhance the stability and robustness of the model, an evaluation network and a target network were designed in tandem to ensure reliable learning. By separating the state and advantage values, the model effectively reduces the noise introduced by action selection, resulting in more accurate predictions and enhanced decision making. In addition, this study introduces a prioritized experience replay module that ranks the importance of each experience sample based on its temporal difference error, thereby prioritizing the most useful experiences for learning. This approach enables the model to focus on more informative samples, thereby accelerating the learning process and improving algorithm efficiency. By addressing the inefficiencies of traditional experience replay methods, which often reuse low-value samples, this technique ensures a more efficient use of the available training time. Moreover, this study employed neural network approximation techniques to reduce the computational demands of online learning, which is particularly important in real-time applications with limited processing power. Experimental results demonstrate that the proposed method substantially reduces resource waste in UAV task scheduling. On average, each UAV assignment is completed in just 0.24 s, indicating substantial improvement in task allocation efficiency. The proposed algorithm outperforms traditional methods in efficiency as well as in convergence speed and stability, owing to the prioritized experience replay module. Furthermore, the scalability of the algorithm was validated via simulations involving larger UAV fleets, where performance remained robust without degradation. Additional simulation tests confirmed that the proposed method can optimize resource allocation, reduce system interference, and accelerate convergence. In conclusion, the proposed method offers significant improvements in multi-UAV system task allocation, particularly in terms of task allocation efficiency and system adaptability.