Smart Balancing of E-scooter Sharing Systems via Deep Reinforcement Learning

Abstract

Nowadays, micro-mobility sharing systems have become extremely popular. Such systems consist in fleets of electric vehicles which are deployed in cities, and used by citizens to move in a more ecological and flexible way. Unfortunately, one of the issues related to such technologies is its intrinsic load imbalance; since the users can pick up and drop off the electric vehicles where they prefer. We present ESB-DQN, a multi-agent system based on Deep Reinforcement Learning that offers suggestions to pick or return e-scooters in order to make the fleet usage and sharing as balanced as possible.

Publication
WOA 2021: 22nd Workshop “From Objects to Agents”
Gianvito Losapio
Gianvito Losapio
Research Fellow

Research Fellow @ MaLGa, Italy

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