###### Abstract
In [22], BS is used to summarize and compress vehicle observation data, and then the compressed information is fed back and the reinforcement learning process is carried out at the base station to improve the spectrum sharing decision performance in the network. In [23], the graph neural network (GNN) is used to build the V2X network; GNN extracts the features of each V2V pair. Based on the extracted features and local observations, the V2V pair can use the Q-network to make distributed decision-making. The authors in [24] propose a V2V communication wireless resource allocation system based on proximal policy optimization. In this radio resource allocation framework, continuous actions and multi-dimensional actions can be output to reduce the implementation complexity of large-scale communication scenarios. In [25], the DQN network is improved. Aiming at the non-stationarity problem caused by multi-agent parallel learning, lag Q-learning and parallel experience replay trajectory are introduced to stabilize the training process, and approximate regret reward (ARR) is added to stabilize the reward estimation. In order to improve the adaptability of the traditional deep reinforcement learning (DRL) algorithm in a dynamic environment, [26] further combines meta-learning with DRL and proposes a meta-based DRL algorithm. Compared with the DQN-based algorithm, our DRL-based algorithm can provide better performance on both V2I and V2V links. In addition, the DRL algorithm training strategy proposed in this paper has good generalization ability and can quickly adapt to the new environment with limited experience. The authors in [27] propose a centralized dynamic channel allocation method based on deep reinforcement learning for satellite Internet of Things. This method makes use of the strong representation ability of the deep neural network to make intelligent allocation decisions through continuous learning of allocation strategies so as to minimize the average transmission delay of all sensors. However, in the above methods, differentiated service design is carried out according to vehicle type or link service characteristics. However, in real life, special vehicles can be seen everywhere on the road, such as police cars, ambulances, fire trucks and so on; they need a better information transmission environment in the Internet of vehicles. Faced with these special vehicles with urgent business needs, compared with all V2V links in existing literature that compete fairly for spectrum resources, this paper proposes a V2V link and V2I link sharing strategy for the scenario that urgent services need to be handled first in the Internet of vehicles. Mode 4 in cellular V2X architecture is used for resource allocation, and vehicles share resource pools for communication between V2V and V2I. In this strategy, the link priority mechanism is introduced, and the higher priority link can get a better information interaction environment by reinforcing the reward design of learning. The innovation points of this paper are summarized as follows:1. Formulate the dynamic spectrum allocation problem in a CU and D2D co-existed vehicle network. 2. Develop a centralized low complexity algorithm based on the deep reinforcement learning method to achieve priority-based spectrum allocation. 3. Build a weighted sum reward function to realize the dynamically adaptive rates--interference between V2I and V2V links. The simulation results show that the proposed control method can effectively improve the service quality of high-priority links while ensuring the overall performance of the system, and has good robustness to communication noise. The rest of this paper is organized as follows. We depict the system model and formulate the optimization problem in Section 2. The proposed RL-based algorithm is presented in Section 3. Section 4 demonstrates simulation results and Section 5 concludes this paper.