Tabular RL on a Non-Markovian Rewards Decision Process formulation matches deep RL performance on real metro expansion in Xi'an and Amsterdam while cutting episodes by 18x and carbon emissions by 12x on average.
Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling.arXiv:2110.03524 [cs], October 2021
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Smart Transportation Without Neurons -- Fair Metro Network Expansion with Tabular Reinforcement Learning
Tabular RL on a Non-Markovian Rewards Decision Process formulation matches deep RL performance on real metro expansion in Xi'an and Amsterdam while cutting episodes by 18x and carbon emissions by 12x on average.