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

Onno Eberhard, Thibaut Cuvelier, Michal Valko, Bruno Adrien De Backer

2023 · in (NeurIPS 2023 - GCRL)

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.

PDF · NeurIPS 2023 Workshop: Goal-conditioned RL · OpenReview preprint · bibtex