Reinforcement Learning for Intensity Control: An Application to Choice-Based Network Revenue Management
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866911585371422720 |
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| author | Meng, Huiling Chen, Ningyuan Gao, Xuefeng |
| author_facet | Meng, Huiling Chen, Ningyuan Gao, Xuefeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_05358 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reinforcement Learning for Intensity Control: An Application to Choice-Based Network Revenue Management Meng, Huiling Chen, Ningyuan Gao, Xuefeng Machine Learning Optimization and Control 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. |
| title | Reinforcement Learning for Intensity Control: An Application to Choice-Based Network Revenue Management |
| topic | Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2406.05358 |