AINeutralarXiv – CS AI · 8h ago6/10
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Smart Transportation Without Neurons -- Fair Metro Network Expansion with Tabular Reinforcement Learning
Researchers demonstrate that tabular reinforcement learning outperforms computationally expensive deep RL methods for metro network expansion problems, achieving 18x fewer training episodes and 12x lower carbon emissions while incorporating fairness criteria. The approach offers an interpretable, resource-efficient alternative to traditional optimization methods for urban transportation planning.
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