A Multi-Agent Deep Reinforcement Learning Algorithm for Task Offloading in Future 6G V2X Network

Abstract

In future 6G Vehicle-to-Everything (V2X) Network, task offloading of mobile edge computing (MEC) systems will face complex challenges in high mobility, dynamic environment. We herein propose a Multi-Agent Deep Reinforcement Learning algorithm (MADRL) with cloud-edge-vehicle collaborations to address these challenges. Firstly, we build the model of the task offloading problem in the cloud-edge-vehicle system, which meets low-latency, low-energy computing requirements by coordinating the computational resources of connected vehicles and MEC servers. Then, we reformulate this problem as a Markov Decision Process and propose a digital twin-assisted MADRL algorithm to tackle it. This algorithm tackles the problem by treating each connected vehicle as a agent, where the observations of agents are defined as the current local environmental state and global digital twin information. The action space of agents comprises discrete task offloading targets and continuous resource allocation. The objective of this algorithm is to improve overall system performance, taking into account collaborative learning among the agents. Experimental results show that the MADRL algorithm performed well in computational efficiency and energy consumption compared with other strategies.