## 1 Introduction
Particularly, in scenarios where task execution depends on cached services, existing algorithms struggle to manage multi-agent coordination and non-stationarity effectively. To address the aforementioned challenges, particularly the fragmented optimization of existing approaches and the inherent instability of current MADRL algorithms in dynamic vehicular environments where task execution depends on cached services, this paper proposes a MADRL problem tailored for joint optimization in UAV-assisted vehicular networks. Unlike existing studies that often overlook the strong interdependencies or optimize only one or two aspects among offloading, caching, and trajectory planning, our problem achieves comprehensive joint optimization of content and service caching, computation offloading decisions, and UAV trajectories, explicitly accounting for task dependencies on cached services. We precisely model this highly dynamic and complex problem as a Dec-POMDP, which effectively captures system dynamics and multi-agent interactions. Furthermore, to overcome the training instability and hyperparameter sensitivity observed in widely used MADRL algorithms like MADDPG, we employ a MAPPO algorithm. This algorithm operates under a CTDE paradigm, balancing efficient learning with robust real-world deployment. Table 1 summarizes and compares our contributions in the field of UAV-assisted MEC with the current state-of-the-art in this domain. The key contributions of this work include the following:  
* We propose a comprehensive joint optimization problem for UAV-assisted vehicular networks, precisely modeled as a Dec-POMDP to capture dynamic environments and complex agent interactions. The model thoroughly considers UAVs' roles in service and content caching and vehicular computation offloading, while simultaneously accounting for cache hit rate, computation delay, and system energy consumption. To balance these conflicting metrics and ensure fairness, we innovatively introduce \"system efficiency\" as a holistic and unified performance indicator. This metric comprehensively considers cache hit rate, task computation latency, system energy consumption, and resource allocation fairness, and is balanced through appropriate weighting. The ultimate objective is to maximize overall system efficiency by jointly optimizing UAV caching strategies, vehicular computation offloading decisions, and UAV flight trajectories within the Dec-POMDP framework. * To address the complex Dec-POMDP model, we propose a MAPPO reinforcement learning algorithm. It efficiently solves the problem through innovative state space design and refined reward shaping mechanisms. The state space comprehensively includes UAV locations, caching states, vehicular service requests, network resource status, and real-time channel quality, providing agents with complete environmental awareness. Beyond positive rewards for service efficiency, the reward mechanism meticulously designs various penalty terms for exceeding UAV flight areas, excessive energy consumption, latency, and unfair resource allocation. These designs effectively constrain agent behavior, ensuring learning stability, accelerating convergence, and enabling optimal decisions in dynamic environments, significantly improving system performance. * To comprehensively validate the effectiveness, robustness, and practical feasibility of the proposed joint optimization problem and reinforcement learning algorithm, extensive system simulations were conducted. The simulation results clearly demonstrate our method's significant advantages in improving the service efficiency of UAV-assisted vehicular networks, specifically in reducing user task computation delay, decreasing overall system energy consumption, increasing content and service hit rates, and ensuring fair resource allocation.