## 1 Introduction
Moreover, studies like [18] by [PERSON] et al. have focused on TD3-based trajectory optimization for energy consumption minimization in UAV-assisted MEC systems, and [19] by [PERSON] et al. optimized resource and trajectory for UAV-assisted MEC communication, underscoring the direct link between UAV movement and system performance. Beyond these, recent works have also explored UAV trajectory optimization for specific objectives in diverse scenarios. For example, Ref. [20] focused on ordered submodularity-based value maximization for UAV data collection in earthquake areas, while [21] investigated reward maximization for disaster zone monitoring with heterogeneous UAVs. These studies highlight the critical role of UAV trajectory planning in achieving various system goals. Therefore, to fully exploit the potential of UAV-assisted vehicular MEC, a joint optimization of caching strategies, task offloading, and UAV trajectory planning is essential. Acknowledging this complexity, a holistic approach is warranted, necessitating a joint optimization of caching strategies, task offloading, and UAV trajectory planning. For example, Ref. [4] by [PERSON] et al. proposed an intelligent joint optimization of deployment (implying trajectory) and task scheduling for multi-UAV-assisted MEC systems, and [22] by [PERSON] et al. explored efficient and sustainable task offloading in UAV-assisted MEC systems using meta deep reinforcement learning, implicitly integrating aspects of trajectory and resource management. This joint optimization problem exhibits high dynamism and complexity, involving discrete caching and offloading decisions alongside continuous trajectory variables. Traditional optimization methods, such as convex optimization or integer programming, face substantial challenges due to the non-convexity and high dimensionality of the problem, making real-time decision-making in dynamic environments difficult. For instance, while methods like those in [23] by [PERSON] apply optimization techniques to UAV-assisted MEC systems for security and resource allocation, the inherent non-convexity and dynamic nature of jointly optimizing caching, offloading, and trajectory often render classical approaches computationally intractable for real-time scenarios. DRL, leveraging its adaptability to dynamic environments and capability to handle high-dimensional decision spaces, provides a novel approach to solving such complex optimization problems. Multi-Agent DRL (MADRL) extends this capability further, making it suitable for collaborative optimization scenarios involving multiple UAVs and vehicles [6]. Recent advancements have seen MADRL effectively applied to various aspects of UAV-aided MEC and resource management. For instance, [PERSON] et al. [24; 25] extensively investigated the energy-efficient computation offloading problem in multi-UAV-assisted Internet of Things (IoT) networks over the Terahertz (THz) band. They jointly optimized UAV trajectories, user equipment (UE), local central processing unit (CPU) clock speeds, UAV-UE associations, time slot slicing, and UE offloading powers through a tailored MADRL solution, DSPAC-MN. Their work notably focused on maximizing energy efficiency in THz communications and ensuring safe UAV flights, even incorporating a more practical ITU-suggested building distribution model [24] into their channel modeling. However, existing MADRL methods still exhibit notable limitations when addressing joint optimization in vehicular networks: most studies focus on optimizing only one or two aspects among offloading [26], caching [27], and trajectory planning [28], failing to achieve comprehensive joint optimization [29], and widely used algorithms (such as Multi-Agent Deep Deterministic Policy Gradient (MADDPG) [6], Deep Q-Learning Network (DQN) [30], Actor Critic (AC) [31], and Deep Deterministic Policy Gradient (DDPG) [32] suffer from training instability and hyperparameter sensitivity, limiting their effectiveness in complex vehicular environments.