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arxiv:2605.02461
Middle-mile logistics through the lens of goal-conditioned reinforcement learning
Published on May 4
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Abstract
Multi-object goal-conditioned MDP reformulation of middle-mile logistics integrates graph neural networks with model-free reinforcement learning using feature graphs extracted from environmental states.
AI-generated summary
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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