Dynamic Pricing in High-Speed Railways Using Multi-Agent Reinforcement Learning
Not on the map. Why?
Abstract (arXiv)
This paper addresses a critical challenge in the high-speed passenger railway industry: designing effective dynamic pricing strategies in the context of competing and cooperating operators. To address this, a multi-agent reinforcement learning (MARL) framework based on a non-zero-sum Markov game is proposed, incorporating random utility models to capture passenger decision making. Unlike prior studies in areas such as energy, airlines, and mobile networks, dynamic pricing for railway systems using deep reinforcement learning has received limited attention. A key contribution of this paper is a parametrisable and versatile reinforcement learning simulator designed to model a variety of railway network configurations and demand patterns while enabling realistic, microscopic modelling of user behaviour, called RailPricing-RL. This environment supports the proposed MARL framework, which models heterogeneous agents competing to maximise individual profits while fostering cooperative behaviour to synchronise connecting services. Experimental results validate the framework, demonstrating how user preferences affect MARL performance and how pricing policies influence passenger choices, utility, and overall system dynamics. This study provides a foundation for advancing dynamic pricing strategies in railway systems, aligning profitability with system-wide efficiency, and supporting future research on optimising pricing policies.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 44 references that are in the map. First posted on arXiv 14 Jan 2025.
Builds on
17 of 44 references.
References · 17
- 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
- 2015Continuous control with deep reinforcement learningcited by 3,279
- 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
- 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
- 2015Prioritized Experience Replaycited by 1,070
- 2017Value-Decomposition Networks For Cooperative Multi-Agent Learningcited by 490
- 2018Actor-Attention-Critic for Multi-Agent Reinforcement Learningcited by 259
- 2017Emergence of Grounded Compositional Language in Multi-Agent Populationscited by 257
- 2019Google Research Football: A Novel Reinforcement Learning Environmentcited by 257
- 2017Multi-agent Reinforcement Learning in Sequential Social Dilemmascited by 206
- 2018Inequity aversion improves cooperation in intertemporal social dilemmascited by 106
- 2021Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Potcited by 97
5 more
- 2017A multi-agent reinforcement learning model of common-pool resource appropriationcited by 68
- 2020Flatland-RL : Multi-Agent Reinforcement Learning on Trainscited by 32
- 2023POMDP inference and robust solution via deep reinforcement learning: An application to railway optimal maintenancecited by 13
- 2021Learning to Share in Multi-Agent Reinforcement Learningcited by 4
- 2024Reinforcement Learning for Scalable Train Timetable Rescheduling with Graph Representationcited by 2
Built on by
Citing papers per year: 2026: 1.
1 paper.