Abstract
In the Internet of Vehicles (IoV), vehicles have the capability to offload their computational tasks to the Mobile Edge Computing (MEC) servers in order to reduce service delay. However, the majority of existent task offloading and computational resource allocation schemes are static and lack consideration of the heterogeneous nature of tasks. Furthermore, in scenarios involving both collaborative and competitive resource utilization, there remains considerable room for performance enhancement for delay-sensitive tasks. To address these challenges, this paper proposes a novel Global Heterogeneous Multi-Agent Reinforcement Learning (GHMARL) that is an enhancement to the general MARL. In GHMARL, each vehicle and MEC server is represented by an agent, and intelligent collaboration and dynamic resource allocation are employed to balance resource usage and delay performance. In particular, GHMARL introduces a global Critic network and a local Critic network, working in a collaborative manner. The former is responsible for guiding the overall system performance to ensure the service delay performance, and the latter is responsible for MEC servers' performance to ensure the resource usage efficiency. Simulation results demonstrate that, in comparison with alternative schemes, GHMARL significantly enhances the overall system performance, particularly with regard to resource usage efficiency. Furthermore, GHMARL offers distinct advantages in balancing task delay and resource consumption under various system dynamics, making it a robust solution to address issues of resource wastage and delay violations for IoV systems.