# A Reinforcement Learning Approach for Rebalancing Electric Vehicle Sharing Systems

arXiv: https://arxiv.org/abs/2010.02369 · HTML: https://mlsanity.com/paper/2010.02369/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2020.

## Built on by (most cited first, 7 of 7)

- [Learning to Solve Vehicle Routing Problems: A Survey](https://mlsanity.com/paper/2205.02453/) (2022)
- [A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with Drone](https://mlsanity.com/paper/2112.12545/) (2021)
- [A new Hyper-heuristic based on Adaptive Simulated Annealing and Reinforcement Learning for the Capacitated Electric Vehicle Routing Problem](https://mlsanity.com/paper/2206.03185/) (2022)
- [Deep PackGen: A Deep Reinforcement Learning Framework for Adversarial Network Packet Generation](https://mlsanity.com/paper/2305.11039/) (2023)
- [Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey](https://mlsanity.com/paper/2108.04462/) (2021)
- [Real-time Control of Electric Autonomous Mobility-on-Demand Systems via Graph Reinforcement Learning](https://mlsanity.com/paper/2311.05780/) (2023)
- [Deep VULMAN: A Deep Reinforcement Learning-Enabled Cyber Vulnerability Management Framework](https://mlsanity.com/paper/2208.02369/) (2022)

## Abstract (arXiv)

This paper proposes a reinforcement learning approach for nightly offline rebalancing operations in free-floating electric vehicle sharing systems (FFEVSS). Due to sparse demand in a network, FFEVSS require relocation of electrical vehicles (EVs) to charging stations and demander nodes, which is typically done by a group of drivers. A shuttle is used to pick up and drop off drivers throughout the network. The objective of this study is to solve the shuttle routing problem to finish the rebalancing work in the minimal time. We consider a reinforcement learning framework for the problem, in which a central controller determines the routing policies of a fleet of multiple shuttles. We deploy a policy gradient method for training recurrent neural networks and compare the obtained policy results with heuristic solutions. Our numerical studies show that unlike the existing solutions in the literature, the proposed methods allow to solve the general version of the problem with no restrictions on the urban EV network structure and charging requirements of EVs. Moreover, the learned policies offer a wide range of flexibility resulting in a significant reduction in the time needed to rebalance the network.