Reinforcement Learning Based Edge-End Collaboration for Multi-Task Scheduling in 6G Enabled Intelligent Autonomous Transport Systems

As communication and computing technologies advance, vehicular edge computing emerges as a promising paradigm for delivering a wide array of intelligent services in 6G enabled Intelligent Autonomous Transport Systems. These service requests, are safety-oriented and typically require the fusion of processing results from multiple independent computation tasks generated by various onboard sensors, in which the computation tasks are delay-sensitive and computation-intensive. Consequently, the allocation of multiple tasks within a single service request while efficiently reducing request completion time and energy consumption presents a substantial challenge. In order to address the problem of multi-task simultaneous scheduling, this paper proposed to employ deep reinforcement learning and edge computing architecture to make task scheduling decisions for vehicles. Firstly, the Vehicle-Infrastructure Network (VINET) is designed, in which the vehicles can assign multiple tasks to the edge servers and other idle vehicles, thus extending the task processing capabilities for vehicles. Secondly, Fully-decentralized Multi-agent Proximal Policy Optimization (FMPPO) algorithm is proposed to make task scheduling decisions for autonomous driving, the large model trained via FMPPO is adaptable to different scenarios with various numbers of vehicles. Thirdly, by taking into account task characteristic, environmental status, and vehicle mobility, the proposed method can make task scheduling decisions in real-time and then dynamically distributes tasks based on the decisions. Finally, experimental results demonstrate that the designed method outperforms benchmark methods in terms of both completion time and energy consumption of computation tasks.