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

Onno Eberhard, Thibaut Cuvelier, Michal Valko, Bruno De Backer
5/4/2026
stat.MLcs.LG

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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.

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Cite this paper

@article{eberhard2026middlemile,
  title  = {Middle-mile logistics through the lens of goal-conditioned reinforcement learning},
  author = {Onno Eberhard and Thibaut Cuvelier and Michal Valko and Bruno De Backer},
  year   = {2026},
  eprint = {2605.02461},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2605.02461v1}
}

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