Abstract: Mobile Edge Computing （MEC）， as a new technology， provides a new solution for the application of the Internet of Vehicles. However， the limited resources in the connected vehicle environment cannot meet the needs of the connected vehicle equipment， which leads to an increase in the service response time and execution energy consumption of tasks， which greatly affects the Quality of Experience （QoE） of users. In order to reduce the delay and energy consumption of task execution and improve the flexibility of algorithm deployment， this paper constructs the networked vehicle system model and proposes an asynchronous advantage actor-critic based task offloading and resource allocation strategy. The algorithm framework uses asynchronous updating to train the model， and adds time attenuation coefficient to reduce the adverse effect of backward model on global model updating. Experimental results show that the proposed algorithm can effectively improve model training efficiency and reduce task execution delay and energy consumption.

Key words:  , Internet of Vehicles, edge computing, task offloading, resource allocation, deep reinforcement learning