## 2 Related Work  
Researchers have actively adopted reinforcement learning methods to guide UAVs in autonomous decision making, which includes the application of basic control systems, path planning, obstacle avoidance, and advanced control techniques.  
**Application of Basic Control Systems:** The fundamental aspects of UAV technology encompass attitude adjustment and basic flight control, with a key focus on ensuring the stability and controllability of UAVs under various flight conditions. [PERSON] et al. [17] utilized Deep Reinforcement Learning (DRL) to optimize UAV attitude control, especially under extreme conditions, such as strong winds and turbulence. Their research demonstrates the potential application of DRL in UAV control systems.  
**Path Planning and Obstacle Avoidance:** Path planning is a critical component of UAV navigation, involving the efficient selection of routes and avoidance of obstacles. [PERSON] et al. [18] employed the Q-learning algorithm to improve UAV path planning and obstacle avoidance capabilities, focusing on enhancing autonomous navigation technology, and confirmed the effectiveness of the algorithm in dealing with static and dynamic obstacles. [PERSON] et al. [19] implemented autonomous navigation of UAVs using RL and the TEXPLORE algorithm, advancing sophisticated UAV control. Their research demonstrated the efficacy of this algorithm in navigating and avoiding obstacles in unknown environments. [PERSON] et al. [20] improved the autonomous navigation capabilities of UAVs in unknown environments, particularly in path planning and obstacle avoidance, using DRL based on the Twin Delayed DDPG (TD3) algorithm. However, these methods also have drawbacks, requiring continuous trial-and-error training with low sample efficiency, and they struggle to generalize to new environments without prior examples.  
**Advanced Control:** Advanced control in UAV technology not only includes basic flight operations but also encompasses the ability to execute complex tasks, such as formation control and advanced navigation techniques. [PERSON] et al. [21] significantly enhanced the formation control capabilities of UAVs using GAIL. Their method, by imitating peer actions and combining historical data, effectively improved the recognition of real-world environmental states. Additionally, [PERSON] et al. [22] significantly enhanced the navigation and control efficiency of racing drones by combining imitation learning with modular strategies and utilizing Convolutional Neural Networks (CNNs). [PERSON] et al. [23] employed meta-reinforcement learning to improve trajectory design for energy-constrained UAVs in dynamic network environments, focusing on enhancing the adaptability and response speed of UAVs as mobile base stations in unfamiliar environments.  
While the applications of meta-reinforcement learning and imitation learning have achieved significant results in improving the efficiency and adaptability of UAV training, they each have their limitations. Meta-reinforcement learning (Meta-RL) often requires the manual design of reward functions to describe new tasks, which can be both cumbersome and impractical for non-experts. A more natural way to describe tasks is through demonstration, as performed in imitation learning (IL). However, IL is limited in its ability to continue improving policies and, compared to reinforcement learning methods, is constrained by the similarity between new tasks and the training dataset, leading to deficiencies in generalization capabilities [24]. Therefore, this paper is dedicated to combining meta-reinforcement learning and imitation learning to address the aforementioned issues.Table 1 compares our work with previous studies.