An Overview for Markov Decision Processes in Queues and Networks
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Abstract (arXiv)
Markov decision processes (MDPs) in queues and networks have been an interesting topic in many practical areas since the 1960s. This paper provides a detailed overview on this topic and tracks the evolution of many basic results. Also, this paper summarizes several interesting directions in the future research. We hope that this overview can shed light to MDPs in queues and networks, and also to their extensive applications in various practical areas.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Jul 2019.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 0; 2025: 0; 2026: 1.
4 papers.
- 2022Learning Mean-Field Control for Delayed Information Load Balancing in Large Queuing Systemscited by 2
- 2023Reinforcement Learning in a Birth and Death Process: Breaking the Dependence on the State Spacecited by 2
- 2022Performance Evaluation, Optimization and Dynamic Decision in Blockchain Systems: A Recent Overviewcited by 0
- 2026RA-CAD: Learning Post-Execution Critique for State-Aware Text-to-CAD Generationcited by 0
- 2026RA-CAD: Learning Post-Execution Critique for State-Aware Text-to-CAD Generation6 Aug 2026
- 2023Reinforcement Learning in a Birth and Death Process: Breaking the Dependence on the State Space21 Feb 2023
- 2022Performance Evaluation, Optimization and Dynamic Decision in Blockchain Systems: A Recent Overview29 Nov 2022
- 2022Learning Mean-Field Control for Delayed Information Load Balancing in Large Queuing Systems9 Aug 2022