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