Information Gain Regulation In Reinforcement Learning With The Digital Twins' Level Of Realism

Digital Twin (DT) is widely used in various industrial sectors to optimize the operations and maintenance of physical assets, system and manufacturing processes. In this paper our goal is to introduce an architecture in which the radio access control happens automatically to minimize the utilized radio resources while still maximizing the production KPIs of the robot cell. To achieve this, we apply Reinforcement Learning (RL) in a simulated environment to explore the environment fast, while the DT ensures that the learned policy can be applied on the real world environment as well. We show that the application of Ultra Reliable Low Latency Communication (URLLC) connection can be reduced to approx. 30% of the total radio time while achieving real-world accurate robot control. The system in action can be seen on [1].