###### Abstract  
Reinforcement learning (RL) is pivotal in empowering Unmanned Aerial Vehicles (UAVs) to navigate and make decisions efficiently and intelligently within complex and dynamic surroundings. Despite its significance, RL is hampered by inherent limitations such as low sample efficiency, restricted generalization capabilities, and a heavy reliance on the intricacies of reward function design. These challenges often render single-method RL approaches inadequate, particularly in the context of UAV operations where high costs and safety risks in real-world applications cannot be overlooked. To address these issues, this paper introduces a novel RL framework that synergistically integrates meta-learning and imitation learning. By leveraging the Reptile algorithm from meta-learning and Generative Adversarial Imitation Learning (GAIL), coupled with state normalization techniques for processing state data, this framework significantly enhances the model's adaptability. It achieves this by identifying and leveraging commonalities across various tasks, allowing for swift adaptation to new challenges without the need for complex reward function designs. To ascertain the efficacy of this integrated approach, we conducted simulation experiments within both two-dimensional environments. The empirical results clearly indicate that our GAIL-enhanced Reptile method surpasses conventional single-method RL algorithms in terms of training efficiency. This evidence underscores the potential of combining meta-learning and imitation learning to surmount the traditional barriers faced by reinforcement learning in UAV trajectory planning and decision-making processes. unmanned aerial vehicles (UAVs); meta-reinforcement learning; generative adversarial imitation learning +
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as depicted in Figure 1. Thus, developing methods to enhance sample efficiency is of paramount importance. Moreover, the generalization capabilities of RL models in the context of UAV trajectory planning and control are often found wanting. Due to the variability and complexity of real-world environments, models that perform well in training scenarios may experience significant performance declines when introduced to new, unfamiliar settings. This drop in performance is often linked to the models' strategies being too tailored to specific training environments, lacking the flexibility needed to adapt to new situations. Additionally, traditional RL approaches are heavily reliant on the design of reward functions. Minor alterations in these functions can lead to vastly different flight strategies, making the task of crafting reward functions that accurately reflect the complex objectives of UAV missions challenging [5; 6]. To tackle these challenges, the research community is turning towards innovative approaches such as meta-learning and imitation learning [7; 8; 9]. Meta-learning, often described as 'learning to learn,' empowers models with the ability to swiftly acclimate to new tasks by discerning and assimilating the underlying similarities across diverse tasks. This approach fundamentally aims to equip models with the capability to identify and abstract the core attributes of tasks, thereby leveraging accumulated knowledge to adeptly modify strategies when confronted with novel scenarios. Such a methodology enhances models' adaptability and generalization skills, making them more versatile and effective in dynamic environments [10; 11]. Imitation learning serves as a valuable complement to meta-learning, focusing on teaching agents to acquire strategies through the observation and replication of expert behaviors, instead of relying solely on reward signals. This learning paradigm is inspired by the human method of skill acquisition, which typically involves emulating the actions of individuals who have already achieved proficiency in a particular domain [12; 13].