Middle-mile logistics through the lens of goal-conditioned reinforcement learning

Onno Eberhard; Thibaut Cuvelier; Michal Valko; Bruno De Backer

doi:10.48550/arxiv.2605.02461

Middle-mile logistics through the lens of goal-conditioned reinforcement learning

arXiv (Cornell University) 2026 Preprint Open access

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Authors: Onno Eberhard, Thibaut Cuvelier, Michal Valko, Bruno De Backer

DOI: 10.48550/arxiv.2605.02461

Cited by: 0

Abstract

Middle-mile logistics describes the problem of routing parcels through a network of hubs linked by trucks with finite capacity. We rephrase this as a multi-object goal-conditioned MDP. Our method combines graph neural networks with model-free RL, extracting small feature graphs from the environment state.

Fields of Study & Subjects

Digital Transformation in Industry Collaboration in agile enterprises Global trade sustainability and social impact

Publication details

Journal / source: arXiv (Cornell University)
Publication date: 4 May 2026
Language: EN
Document type: Preprint