# Optimal Task Offloading Strategy for Vehicular Networks in Mixed Coverage Scenarios
## 2 Related Work  
Recently, there has been widespread attention for how to alleviate the overload of EC servers on RSUs during traffic congestion while meeting the needs of users within a service area. To address this issue, genetic algorithms, particle swarm optimization (PSO), deep Q-networks (DQNs), deep deterministic policy gradients (DDPGs), PPO, and other improvedalgorithms have been introduced to determine the optimal task offloading strategy [14; 15; 16; 17; 18]. In [19], [PERSON] et al. proposed a framework and PSO for a cooperative computation algorithm for minimizing the delay and cost of task offloading in VEC. [PERSON] et al. [20] proposed a dynamic computation offloading algorithm based on the estimated time of arrival to optimize the QoS and fairness in the IoV. [PERSON] et al. [21] presented a task offloading method based on the DDPG algorithm and highlighted the benefits of using unmanned aerial vehicles (UAVs) to optimize task offloading ratios in a 5G IoT environment, aiming to improve the efficiency of task offloading decisions. [PERSON] and [PERSON] [22] proposed a DQN model based on deep reinforcement learning (DRL) for future intelligent transportation scenarios. This model aims to balance the trade-off between computational capability and traffic conditions through vehicle-to-vehicle (V2V) self-organizing networks sharing computational resources on an EC platform. They introduced a new metric to resolve the conflicting objectives between traffic performance and computational capacity. [PERSON] et al. [23] proposed a dynamic resource allocation scheme based on policy gradient DRL to optimize the computational performance and energy consumption of mobile edge computing (MEC) systems. In [24], [PERSON] et al. introduced an actor-critic DRL-based method for task offloading in a multi-vehicle assisted MEC system. This method aims to improve system performance, accelerate convergence efficiency, and reduce overall operating costs by enhancing model training efficiency. [PERSON] et al. [18] proposed the \"Com-DDPG\" algorithm, in which a multi-agent DRL method was used and via DDPG enhances the offloading performance. [PERSON] et al. [25] proposed a novel multi-objective reinforcement learning for a multi-objective offloading problem that considers both the service provider's revenue and the quality of experience of the vehicle users. The existing work shows that DRL is feasible and has better performance than heuristic algorithms. However, some DRL algorithms, such as DDPG and multi-agent DRL algorithms, require a large amount of computing power and have poor robustness. Urban roads are often overlapped by both 5G BSs and RSUs. There has been some research on the utilization of idle computing resources of roadside BSs. In [6], a multi-task offloading strategy based on energy consumption is proposed to address the EC issue in vehicular networks. It deploys EC servers at RSUs and macro-BSs and offloads computing tasks to EC servers through wireless cellular networks, enabling resource sharing and mutual benefit between V2V, vehicle-to-roadside (V2R), and vehicle-to-BS communications. [PERSON] et al. [10] proposed a heterogeneous EC architecture for the IoV, utilizing aerial relay stations to connect vehicles with nearby heterogeneous edge infrastructure for task offloading. By introducing a centralized training distributed execution (CTDE) multi-agent DRL algorithm, they aimed to optimize system computational delay and ensure load balancing among edge infrastructures. [PERSON] et al. [26] proposed an approach combining LSTM and convolutional neural networks to predict the location of car users to realize the task offloading scheme of vehicles-to-BSs. [PERSON] and [PERSON] [27] proposed a distributed task offloading strategy to low load BSs in MEC environment; this work considers the computing and communication resources of the BSs. [PERSON] et al.