Multi-Agent RL Aided Task Offloading and Resource Management in Wi-Fi 6 and 5G Coexisting Industrial Wireless Environment

With the emergence of Industrial Internet of Things (IIoT), intensive computation workload will be imposed to Industrial End Units (IEUs). By leveraging Mobile Edge Computing (MEC), the local computational tasks can be offloaded to servers deployed in mobile edge networks with low latency. This paper proposes the intelligent cost-and-energy-effective task offloading in the 5G and Wi-Fi 6 coexisting heterogeneous IIoT networks. The novel Joint Task Scheduling and Resource Allocation (JTSRA) approach comprises two parts: a Lyapunov optimization-based component to decide local task scheduling and computing power, and an online multi-agent reinforcement learning component together with a game theory-based algorithm to select offloading link and decide transmit power, respectively. Simulation results demonstrate the proposed approach holds obvious advantage over the compared ‘intuition’ and ‘cost optimal’ approaches in the efficiency of making comprehensive decision that improves energy efficiency and cost while controlling task delay in the multi-IEU and multi-access-node MEC systems.