Offline Critic-guided Diffusion Policy For Multi-user Delay-constrained Scheduling

Abstract

Effective multi-user delay-constrained scheduling is crucial in various real-world applications, such as instant messaging, live streaming, and data center management. 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 interactions with actual systems during the training stage, which can be difficult or impractical, as it is capable of significantly degrading system performance and incurring 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 Guidance and \underline\{D\}iffusion Generation (SOCD), to learn efficient scheduling policies purely from pre-collected *offline data*. SOCD innovatively employs a diffusion-based po

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