Middle-mile logistics through the lens of goal-conditioned reinforcement learning
Onno Eberhard, Thibaut Cuvelier, Michal Valko, Bruno De Backer
stat.ML cs.LG
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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Classified with taxonomy v2 on Wed, 2 Sept 2026.