基于目标条件强化学习视角的中段物流优化 / Middle-mile logistics through the lens of goal-conditioned reinforcement learning
1️⃣ 一句话总结
本文提出一种结合图神经网络与无模型强化学习的新方法，将中段物流（即卡车在枢纽间运输包裹的问题）转化为多目标条件马尔可夫决策过程，通过从环境状态中提取小特征图来高效优化包裹路由策略。
arXiv 提交日期: 2026-05-04
Abstract - Middle-mile logistics through the lens of goal-conditioned reinforcement 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.
源自 arXiv: 2605.02461