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Thursday, October 1 • 12:20pm - 12:50pm
Information Gain Regulation in Reinforcement Learning with the Digital Twins' Level of Realism In RlLib - Géza Szabó, Ericsson Research & József Pető, Budapest University of Technology and Economics

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Digital Twin (DT) is widely used in various industrial sectors to optimize the operations and maintenance of physical assets, system and manufacturing processes. 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. We discuss the implementation of the concept within RLlib.

avatar for Géza Szabó

Géza Szabó

Senior Researcher, Ericsson Research
Géza Szabó joined Ericsson Research as an undergraduate student in 2005. He wrote his MSc thesis on Ericsson topic in the Budapest University of Technology and Economics, about comparing various application traffic classification methods. He pursued further this topic in his PhD... Read More →
avatar for József Pető

József Pető

PhD Student, Budapest University of Technology and Economics
József Pető received his MSc. degree in Computer Engineering from the Budapest University of Technology and Economics in 2018.He is currently working toward his Ph.D. degree. His current areas of interest and research include cloud robotics, digital twin, robot simulation, machine... Read More →

Thursday October 1, 2020 12:20pm - 12:50pm PDT
Virtual 2