Value-Based Reinforcement Learning for Digital Twins in Cloud Computing
arxiv(2023)
摘要
The setup considered in the paper consists of sensors in a Networked Control
System that are used to build a digital twin (DT) model of the system dynamics.
The focus is on control, scheduling, and resource allocation for sensory
observation to ensure timely delivery to the DT model deployed in the cloud.
Low latency and communication timeliness are instrumental in ensuring that the
DT model can accurately estimate and predict system states. However, acquiring
data for efficient state estimation and control computing poses a non-trivial
problem given the limited network resources, partial state vector information,
and measurement errors encountered at distributed sensors. We propose the
REinforcement learning and Variational Extended Kalman filter with Robust
Belief (REVERB), which leverages a reinforcement learning solution combined
with a Value of Information-based algorithm for performing optimal control and
selecting the most informative sensors to satisfy the prediction accuracy of
DT. Numerical results demonstrate that the DT platform can offer satisfactory
performance while reducing the communication overhead up to five times.
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