Learning to Operate a Fleet of Cars
| Autor: | Jan Hakenberg, Julian Zilly, Claudio Ruch, Emilio Frazzoli, Christian Fluri |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
050210 logistics & transportation
0209 industrial biotechnology Operations research Computer science 05 social sciences Control (management) 02 engineering and technology Idle 020901 industrial engineering & automation Work (electrical) Service level Factor (programming language) 0502 economics and business Reinforcement learning computer computer.programming_language |
| Zdroj: | ITSC |
| DOI: | 10.1109/itsc.2019.8917533 |
| Popis: | In a mobility-on-demand system, travel requests are handled by a fleet of shared vehicles in an on-demand fashion. An important factor that determines the operational efficiency and service level of such a mobility-on-demand system is its operational policy that assigns available vehicles to open passenger requests and relocates idle vehicles. Previously described operational policies are based on control theoretical approaches, most notably on receding horizon control. In this work, we employ reinforcement learning techniques to design an operational policy for a mobility-on-demand system. In particular, we propose a cascaded learning framework to reduce the number of state-action pairs which allows for more efficient learning. We train our model using the AMoDeus simulation environment and real taxi trip travel data from the city of San Francisco. Finally, we demonstrate that our reinforcement learning based operational policy for mobility-on-demand systems outperforms state-of the art fleet operational policies that are based on conventional control theoretical approaches. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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