Prioritized Task Offloading in Vehicular Edge Computing Using Deep Reinforcement Learning

This paper primarily focuses on vehicular computation offloading and scheduling based on prioritized incentives. The objective of this work is to maximize task completion within designated deadlines while concurrently minimizing energy consumption and latency. To achieve this, a prioritized Deep Q Network (DQNP) has been proposed to optimize long-term rewards by dynamically selecting a priority class, server, and CPU frequency at each computational step. Simulation results demonstrate that, compared with baseline algorithms, the proposed DQNP can effectively choose tasks, ensuring minimal energy consumption, reduced latency, and improved completion rates, especially for tasks in the highest priority class. Additionally, the findings shed light on how the model alleviates the resource starvation challenge faced by low-priority tasks.