A continual reinforcement learning scheduler with multi-timescale replay and a resource-constrained actor-critic reduces digital twin state estimation error by up to 55.2% in simulation.
Digital twin networks: A survey,
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Continual Reinforcement Learning for Digital Twin Synchronization Optimization
A continual reinforcement learning scheduler with multi-timescale replay and a resource-constrained actor-critic reduces digital twin state estimation error by up to 55.2% in simulation.