Reinforcement Learning for Intensity Control: An Application to Choice-Based Network Revenue Management

[2406.05358] Reinforcement Learning for Intensity Control: An Application to Choice-Based Network Revenue Management Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2406.05358 (cs) [Submitted on 8 Jun 2024 ( v1 ), last revised 12 Apr 2026 (this version, v3)] Title: Reinforcement Learning for Intensity Control: An Application to Choice-Based Network Revenue Management Authors: Huiling Meng , Ningyuan Chen , Xuefeng Gao View a PDF of the paper titled Reinforcement Learning for Intensity Control: An Application to Choice-Based Network Revenue Management, by Huiling Meng and 2 other authors View PDF HTML (experimental) Abstract: Intensity control is a class of continuous-time dynamic optimization problems with many important applications in Operations Research including queueing and revenue management. In this study, we propose a practical continuous-time reinforcement learning framework for intensity control using choice-based network revenue management as a case study, which is a classical problem in revenue management that features a large state space, a large action space, and a continuous time horizon. We show that by leveraging the event-driven structure of the problem and the inherent discretization of sample paths created by the state-jump times, a defining feature of intensity control, one does not need to discretize the time horizon in advance. We adapt discrete-time Monte Carlo and temporal difference learning algorithms for policy evaluation to continuous time and develop policy-gradient-based actor-critic algorithms for event-driven intensity control. Through a comprehensive numerical study, we evaluate the proposed approach against various state-of-the-art benchmarks, demonstrating its overall superior performance and effective scalability to large-scale problems. Notably, compared to discretization-based reinforcement learning methods, our continuous-time approach delivers significantly superior performance while maintaining comparable computational efficiency. This advantage is particularly pronounced in highly non-stationary environments. Subjects: Machine Learning (cs.LG) ; Optimization and Control (math.OC) Cite as: arXiv:2406.05358 [cs.LG] (or arXiv:2406.05358v3 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2406.05358 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Huiling Meng [ view email ] [v1] Sat, 8 Jun 2024 05:27:01 UTC (79 KB) [v2] Sat, 2 Aug 2025 02:11:19 UTC (209 KB) [v3] Sun, 12 Apr 2026 00:40:35 UTC (181 KB) Full-text links: Access Paper: View a PDF of the paper titled Reinforcement Learning for Intensity Control: An Application to Choice-Based Network Revenue Management, by Huiling Meng and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2024-06 Change to browse by: cs math math.OC References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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