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In these scenarios, schedulers must make real-time decisions to satisfy both delay and resource constraints without prior knowledge of system dynamics, which are often time-varying and challenging to estimate. {Current learning-based methods typically require online interactions with actual systems during the training stage. Therefore, these approaches are often difficult or impractical, as they can significantly degrade system performance and incur substantial service costs.} To address these challenges, we propose a novel offline reinforcement learning-based algorithm, named \\underline{S}cheduling By \\underline{O}ffline Learning with \\underline{C}ritic Guidanc","title":"Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling","url":"https://arxiv.org/abs/2501.12942","vendor":"arxiv_cs_ai"},"summary":"arXiv:2501.12942v2 Announce Type: replace \nAbstract: Effective multi-user delay-constrained scheduling is crucial in various real-world applications, including embodied AI, instant messaging, live streaming, and data center management, where efficient resource allocation is required among users with diverse delay sensitivities. 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