A Distributed Vehicle-assisted Computation Offloading Scheme based on DRL in Vehicular Networks

With the development of 5G and the Internet, the explosion of new mobile applications has led to an increasing number of computation-intensive and latency-sensitive tasks, which poses a severe challenge to resource-limited vehicles. Mobile Edge Computing (MEC) is recognized as an encouraging paradigm for providing offloading services from vehicles to the edge of wireless access networks. Computation offloading is the key technology in MEC that determines which tasks should be offloaded, for achieving the minimal time and energy consumption. However, in highway scenarios, statically deployed lightweight edge servers cannot meet the high resource requirements of vehicles with dependencies among subtasks. To alleviate this issue, this paper explores the utility of vehicles with idle resources as vehicular MEC servers. Considering the problem of dependency-aware task offloading, we utilize the directed acyclic graph (DAG) to analyze the task topology. Furthermore, due to privacy preservation, a distributed deep reinforcement learning-based algorithm with an optimized structure for offloading strategy is proposed. Convolutional neural networks and transformers are employed in the structure to extract rich state information efficiently. Experimental results reveal that, the proposed scheme achieves superior performance compared to the benchmark algorithms.