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.
Toward communication-efficient digital twin via ai-powered transmission and reconstruction,
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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.