Summary of Reinforcement Learning and Regret Bounds For Admission Control, by Lucas Weber et al.
Reinforcement Learning and Regret Bounds for Admission Control
by Lucas Weber, Ana Bušić, Jiamin Zhu
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC); Machine Learning (stat.ML)
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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 paper presents a novel approach to reinforcement learning, specifically addressing the problem of admission control in an M/M/c/S queue with class-dependent rewards and holding costs. The authors demonstrate that traditional methods have a prohibitively large lower bound due to the exponential nature of the buffer size S, making them impractical for real-world applications. They propose an algorithm inspired by UCRL2 and show that it achieves a regret of O(SlogT + sqrt(mTlogT)) in the finite server case and eliminates the dependence on S in the infinite server case. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how machines learn from experience, making decisions based on rewards or penalties. The researchers study a specific problem where machines decide which jobs to accept or reject in a queue. They show that traditional methods are not suitable for this task because they require too much information. Instead, the authors propose a new approach that works well and is practical. |
Keywords
* Artificial intelligence * Reinforcement learning
