Middle-mile logistics through the lens of goal-conditioned reinforcement learning
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866913085945544704 |
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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 |