New Offline Learning Algorithm Improves Multi-User Scheduling Without Real-Time System Testing
Researchers have developed SOCD, an offline reinforcement learning algorithm that learns efficient scheduling policies from pre-collected data rather than requiring real-time interaction with live systems. The method combines diffusion models with critic guidance and Lagrangian optimization to handle delay-constrained scheduling across diverse applications like data centers and live streaming. This approach reduces service costs and system performance degradation that typically occur during traditional online training.
A new algorithm called SOCD (Scheduling By Offline Learning with Critic Guidance and Diffusion Model) addresses a key challenge in multi-user scheduling: training effective policies without disrupting live systems. Traditional learning-based scheduling methods require online interactions with actual systems during training, which can degrade performance and incur substantial costs. SOCD instead learns from offline datasets using a diffusion policy combined with a sampling-free critic network and Lagrangian multiplier optimization. The approach is designed to handle real-world constraints including time-varying system dynamics, partial observability, and large-scale environments. Experimental results indicate the algorithm performs better than existing methods while maintaining resilience across various system conditions.
What's missing
The paper does not provide specific quantitative comparisons (e.g., performance metrics, percentage improvements) against baseline methods, nor does it detail the characteristics of the offline datasets used for training or specify which real-world applications were tested in experiments.
What different sources said
- arXiv cs.AICenter
Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling
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