# Optimizing Electric Vehicle Charging Station Placement Using Reinforcement Learning and Agent-Based Simulations

arXiv: https://arxiv.org/abs/2511.01218 · HTML: https://mlsanity.com/paper/2511.01218/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 7 of its 46 references that are in the map.
- First posted on arXiv 3 Nov 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 7 of 7)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Demand Prediction and Placement Optimization for Electric Vehicle Charging Stations](https://mlsanity.com/paper/1604.05472/) (2016)
- [Coherent Hierarchical Probabilistic Forecasting of Electric Vehicle Charging Demand](https://mlsanity.com/paper/2411.00337/) (2024)
- [Demand Forecasting for Electric Vehicle Charging Stations using Multivariate Time-Series Analysis](https://mlsanity.com/paper/2502.16365/) (2025)
- [Optimal Placement of Public Electric Vehicle Charging Stations Using Deep Reinforcement Learning](https://mlsanity.com/paper/2108.07772/) (2021)
- [Dynamic Load Balancing for EV Charging Stations Using Reinforcement Learning and Demand Prediction](https://mlsanity.com/paper/2503.06370/) (2025)

## Abstract (arXiv)

The rapid growth of electric vehicles (EVs) necessitates the strategic placement of charging stations to optimize resource utilization and minimize user inconvenience. Reinforcement learning (RL) offers an innovative approach to identifying optimal charging station locations; however, existing methods face challenges due to their deterministic reward systems, which limit efficiency. Because real-world conditions are dynamic and uncertain, a deterministic reward structure cannot fully capture the complexities of charging station placement. As a result, evaluation becomes costly and time-consuming, and less reflective of real-world scenarios. To address this challenge, we propose a novel framework that integrates deep RL with agent-based simulations to model EV movement and estimate charging demand in real time. Our approach employs a hybrid RL agent with dual Q-networks to select optimal locations and configure charging ports, guided by a hybrid reward function that combines deterministic factors with simulation-derived feedback. Case studies in Hanoi, Vietnam, show that our method reduces average waiting times by 53.28% compared to the initial state, outperforming static baseline methods. This scalable and adaptive solution enhances EV infrastructure planning, effectively addressing real-world complexities and improving user experience.