Optimization and research on resource allocation strategy of vehicle edge computing based on deep learning

In vehicular edge computing environments, where resources are limited and task latency sensitivity is high, we propose a resource allocation method based on deep reinforcement learning. First, a vehicular edge computing system model is constructed, then an optimal resource allocation problem is derived using a gradient iteration method and KKT conditions. The resource-optimized MADDPG algorithm is employed to replace the original DDPG algorithm to reduce time delays caused by uneven resource allocation. The effectiveness of the algorithm is verified through simulation experiments, and its performance in actual traffic scenarios is analyzed. Simulation results show that our method has superior performance in reducing time delays compared to traditional methods.