Digital Twin for Industry 5.0: An Reinforcement Learning Model for Backoff Optimization

Industry 5.0 envisions the seamless integration of automation, human-machine collaboration, and intelligent systems to enable highly flexible and adaptive manufacturing. However, this puts stringent requirements on the communication systems extending beyond the capabilities of current 5G technologies. This gap motivates the adoption of the digital twin (DT) paradigm, where a digital replica of physical assets and networks enables continuous monitoring, simulation, and optimization of industrial processes. This paper investigates the optimization of network access protocols, which are critical to maintaining uninterrupted industrial production. Specifically, we develop a reinforcement learning (RL) algorithm embedded within the DT framework to ensure reliable communication and production flow continuity. In a congested factory environment, an autonomous guided vehicle (AGV) must transmit data to a base station (BS) using an ALOHA-like protocol at terahertz (THz) frequencies. The DT-enabled RL model dynamically learns traffic patterns and adaptively selects backoff (BO) times for network access, maximizing reliability without requiring prior knowledge of system topology. Extensive evaluations across diverse operating scenarios–ranging from static to mobile settings, varying traffic loads, and different data and action space sizes–confirm the robustness, scalability, and adaptability of the proposed solution in highly dynamic industrial environments against conventional and advanced baselines.