Reinforcement Learning For Intensity Control: An Application To Choice-based Network Revenue Management

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 ev

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• arxiv keymeng2024reinforcement

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