2022/06/21 by Trang H. Tran, Lam M. Nguyen, Tran, Trang H. +3
Business, Management and Accounting · Computer Science · Engineering · #Advanced Queuing Theory Analysis #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Network Traffic and Congestion Control #Optimization and Control (math.OC) #Smart Grid Security and Resilience
paper · pdf · doi:10.48550/arxiv.2206.10073
openalex publication_date 2022/06/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Queueing systems appear in many important real-life applications including communication networks, transportation and manufacturing systems. Reinforcement learning (RL) framework is a suitable model for the queueing control problem where the underlying dynamics are usually unknown and the agent receives little information from the environment to navigate. In this work, we investigate the optimization aspects of the queueing model as a RL environment and provide insight to learn the optimal policy efficiently. We propose a new parameterization of the policy by using the intrinsic properties of queueing network systems. Experiments show good performance of our methods with various load conditions from light to heavy traffic.