Middle-mile logistics through the lens of goal-conditioned reinforcement learning
On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. First posted on arXiv 4 May 2026.
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