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arXiv Machine Learning By Korbinian K\"ugler, Carolin Schmidt, Florian Dandl, Filipe Rodrigues

Regulating Autonomous Mobility-on-Demand Operators with Fixed and Adaptive Penalties: A Multi-Agent Reinforcement Learning Approach

Read the original on arXiv Machine Learning →

The paper investigates how penalty-based regulation can curb surge pricing and enhance service coverage for profit-maximizing Autonomous Mobility-on-Demand operators. It models regulation as an outer control loop, using fixed penalties formulated as a Markov Decision Process and adaptive penalties as a Partially Observable Stochastic Game, with both operator and regulator policies learned via graph-based multi-agent reinforcement learning. Experiments on Manhattan data show that fixed penalties cut excessive pricing from 22% to 1%, while dynamic penalties improve regulator rewards by up to 15.6% when spare fleet capacity is available.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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