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Smart Transportation Without Neurons -- Fair Metro Network Expansion with Tabular Reinforcement Learning

arXiv:2606.04167v1 Announce Type: new Abstract: We tackle the Metro Network Expansion Problem (MNEP), a subset of the Transport Network Design Problem (TNDP), which focuses on expanding metro systems to satisfy travel demand. Traditional methods rely on exact and heuristic approaches that require expert-defined constraints to reduce the search space. Recently, deep reinforcement learning (Deep RL) has emerged due to its effectiveness in complex sequential decision-making processes-it...

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Orbital Networks in the Three-Body Problem

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A Kinetic Theory of Encounter-Based Information Propagation in Multi-Robot Systems

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EU auditors flag weak oversight of €527 million Balkans transport fund

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How's Linear so fast? A technical breakdown

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BMCR: Adaptive Backbone Module Composition via Reinforcement Learning for Remote Sensing Object Detection

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The Untold Story of the Google Buses That Took Over San Francisco

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Giuliani: WC security challenge 'unprecedented'

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Zoned tourist vessel routes could curb invasive marine pests across Galápagos Islands

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