Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets

Source

Source name: arXiv
Source kind: paper
Authors / organization: Enrique Adrian Villarrubia-Martin, David Muñoz-Valero, Luis Rodriguez-Benitez, Giovanni Montana, Luis Jimenez-Linares
Venue: arXiv: cs.LG
Published: 2026-07-06
Collected: 2026-07-07
URL: http://arxiv.org/abs/2607.05179v1
PDF: https://arxiv.org/pdf/2607.05179v1
DOI: UNKNOWN

Plain-Language Brief

Researchers propose a new approach for dynamic pricing in liberalized railway systems using Relational Multi-Agent Reinforcement Learning (RL). The method models the environment as a graph, process features of entities through a multi-layer relational graph convolutional network, and uses a learnt attention mechanism for aggregation. Experimental results show higher revenue and stability compared to baselines.

What the Document Says

The research is about dynamic pricing in liberalized railway systems using Relational Multi-Agent Reinforcement Learning.
The environment is modeled as a graph of operational units, encoding competition, coordination, and connectivity relations between entities.
A multi-agent twin delayed deep deterministic policy gradient algorithm with graph-based representation learning is used.
The features of the entities are processed through a multi-layer relational graph convolutional network.
A learnt attention mechanism is used for aggregation.
Experimental results show higher revenue and stability compared to baselines in a rail pricing RL environment.

Key Concepts Mentioned

Relational Multi-Agent Reinforcement Learning
Dynamic Pricing
High-Speed Railway Markets
Graph-based Representation Learning
Multi-layer Relational Graph Convolutional Network
Learnt Attention Mechanism

Claims Made by the Document

This novel framework achieves higher revenue and stability compared to baselines.

Evidence Used

Experimental results in a rail pricing RL environment.

Methods / Approach

Relational Multi-Agent Reinforcement Learning
Graph-based Representation Learning
Multi-layer Relational Graph Convolutional Network
Learnt Attention Mechanism

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

In liberalised railway systems, operators must set prices dynamically in an environment with partial observability, as they retain private information about their objectives and performance, where regulatory constraints prohibit communication or direct information exchange between competitors to prevent explicit collusion. Consequently, agents must learn to infer strategic interactions only from observable market data which presents a significant challenge for multi-agent reinforcement learning, where standard approaches typically treat observations as unstructured vectors, ignoring the underlying market topology that governs strategic interactions. To address this, an entity graph modelling approach is proposed, which represents the environment as a graph of operational units, rather than decision-making agents or static infrastructure, encoding competition, coordination, and connectivity relations between entities. Then, an extension of the multi-agent twin delayed deep deterministic policy gradient algorithm with graph-based representation learning processes the features of the entities through a multi-layer relational graph convolutional network and aggregates them via a learnt attention mechanism. Experimental results in a rail pricing reinforcement learning environment show that this novel framework achieves higher revenue and stability in two different settings of increasing market complexity compared to a representative selection of relational and non-relational baselines. The code is publicly available at: https://github.com/Kinrre/RelationalRailPricing-RL