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Researchers from the University of Amsterdam propose a tabular reinforcement learning approach to the Metro Network Expansion Problem, showing it achieves comparable performance to Deep RL while reducing training episodes by 18x and carbon emissions by 12x on average. The method also incorporates social equity criteria and is evaluated on real-world metro networks in Xi'an and Amsterdam.