Summary of Differentiable Discrete Event Simulation For Queuing Network Control, by Ethan Che et al.
Differentiable Discrete Event Simulation for Queuing Network Control
by Ethan Che, Jing Dong, Hongseok Namkoong
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY); Optimization and Control (math.OC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework for policy optimization in queueing network control uses differentiable discrete event simulation to tackle the challenges of high stochasticity, large state and action spaces, and lack of stability. By implementing a well-designed smoothing technique, the framework can compute pathwise policy gradients for large-scale queueing networks using auto-differentiation software and GPU parallelization. This approach leads to more accurate policy gradient estimators than traditional REINFORCE-based methods, with a 50-1000x improvement in sample efficiency over state-of-the-art RL methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to control queues in job-processing systems like service systems, communication networks, and manufacturing processes. They use a special type of computer simulation that can be adjusted and improved using artificial intelligence techniques. This helps make better decisions about how to manage the flow of work in these systems. The results show that this approach is much faster and more accurate than previous methods, which could help solve real-world problems. |
Keywords
» Artificial intelligence » Optimization
