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

Fuente: arXiv
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Autori principali: Eberhard, Onno, Cuvelier, Thibaut, Valko, Michal, De Backer, Bruno
Natura: Preprint
Pubblicazione: 2026
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author Eberhard, Onno
Cuvelier, Thibaut
Valko, Michal
De Backer, Bruno
author_facet Eberhard, Onno
Cuvelier, Thibaut
Valko, Michal
De Backer, Bruno
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2605_02461
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Middle-mile logistics through the lens of goal-conditioned reinforcement learning
Eberhard, Onno
Cuvelier, Thibaut
Valko, Michal
De Backer, Bruno
Machine Learning
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.
title Middle-mile logistics through the lens of goal-conditioned reinforcement learning
topic Machine Learning
url https://arxiv.org/abs/2605.02461