# Optimal Placement of Public Electric Vehicle Charging Stations Using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2108.07772 · HTML: https://mlsanity.com/paper/2108.07772/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 17 Aug 2021.

## Builds on (most cited first, 4 of 4)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Deep Reinforcement Learning: An Overview](https://mlsanity.com/paper/1701.07274/) (2017)
- [Demand Prediction and Placement Optimization for Electric Vehicle Charging Stations](https://mlsanity.com/paper/1604.05472/) (2016)

## Built on by (most cited first, 2 of 2)

- [A Survey on Applications of Reinforcement Learning in Spatial Resource Allocation](https://mlsanity.com/paper/2403.03643/) (2024)
- [Optimizing Electric Vehicle Charging Station Placement Using Reinforcement Learning and Agent-Based Simulations](https://mlsanity.com/paper/2511.01218/) (2025)

## Abstract (arXiv)

The placement of charging stations in areas with developing charging infrastructure is a critical component of the future success of electric vehicles (EVs). In Albany County in New York, the expected rise in the EV population requires additional charging stations to maintain a sufficient level of efficiency across the charging infrastructure. A novel application of Reinforcement Learning (RL) is able to find optimal locations for new charging stations given the predicted charging demand and current charging locations. The most important factors that influence charging demand prediction include the conterminous traffic density, EV registrations, and proximity to certain types of public buildings. The proposed RL framework can be refined and applied to cities across the world to optimize charging station placement.