A computation offloading method with distributed double deep Q-network for connected vehicle platooning with vehicle-to-infrastructure communications

Current connected vehicle applications, such as platooning require heavy-load computing capability. Although mobile edge computing (MEC) servers connected to the roadside intelligence facility can assist such separable applications from vehicles, it is a challenge to coordinate the allocation of subtasks among vehicles and MEC servers on the premise of ensuring communication quality. Therefore, an offloading algorithm is proposed based on a double deep Q-network to solve the placement of subtasks for vehicle to infrastructure and vehicle to vehicle cases. This algorithm considers the randomness of task generation and is model-free. The MEC server can assist the vehicle in training the neural network and storing relevant state transitions. To improve the performance of the algorithm, the decaying $\epsilon -$greedy policy is incorporated for faster convergence. The simulation results showed that this algorithm performed well in reducing the dropped subtask rate, average time delay, and total energy consumption.