# Demonstration of effective UCB-based routing in skill-based queues on real-world data

arXiv: https://arxiv.org/abs/2506.20543 · HTML: https://mlsanity.com/paper/2506.20543/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 25 Jun 2025.

## Builds on (most cited first, 7 of 7)

- [Queueing Network Controls via Deep Reinforcement Learning](https://mlsanity.com/paper/2008.01644/) (2020)
- [On Learning the cμ Rule in Single and Parallel Server Networks](https://mlsanity.com/paper/1802.06723/) (2018)
- [Reinforcement Learning for Datacenter Congestion Control](https://mlsanity.com/paper/2102.09337/) (2021)
- [Adaptive Matching for Expert Systems with Uncertain Task Types](https://mlsanity.com/paper/1703.00674/) (2017)
- [Score-Aware Policy-Gradient and Performance Guarantees using Local Lyapunov Stability](https://mlsanity.com/paper/2312.02804/) (2023)
- [Data Centers Job Scheduling with Deep Reinforcement Learning](https://mlsanity.com/paper/1909.07820/) (2019)
- [Learning payoffs while routing in skill-based queues](https://mlsanity.com/paper/2412.10168/) (2024)

## Abstract (arXiv)

This paper is about optimally controlling skill-based queueing systems such as data centers, cloud computing networks, and service systems. By means of a case study using a real-world data set, we investigate the practical implementation of a recently developed reinforcement learning algorithm for optimal customer routing. Our experiments show that the algorithm efficiently learns and adapts to changing environments and outperforms static benchmark policies, indicating its potential for live implementation. We also augment the real-world applicability of this algorithm by introducing a new heuristic routing rule to reduce delays. Moreover, we show that the algorithm can optimize for multiple objectives: next to payoff maximization, secondary objectives such as server load fairness and customer waiting time reduction can be incorporated. Tuning parameters are used for balancing inherent performance trade--offs. Lastly, we investigate the sensitivity to estimation errors and parameter tuning, providing valuable insights for implementing adaptive routing algorithms in complex real-world queueing systems.