mlsanityData to 14 Oct 2026

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

arXiv 2511.01218 ↗First posted 3 Nov 2025cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

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.

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.

Builds on

7 of 46 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025Dynamic Load Balancing for EV Charging Stations Using Reinforcement Learning and Demand Prediction9 Mar

Other references · 6

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015Continuous control with deep reinforcement learningcited by 3,407
  3. 2016Demand Prediction and Placement Optimization for Electric Vehicle Charging Stationscited by 4
  4. 2024Coherent Hierarchical Probabilistic Forecasting of Electric Vehicle Charging Demandcited by 3
  5. 2025Demand Forecasting for Electric Vehicle Charging Stations using Multivariate Time-Series Analysiscited by 3
  6. 2021Optimal Placement of Public Electric Vehicle Charging Stations Using Deep Reinforcement Learningcited by 2

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Similar, not cited

Close in wording, no citation either way

  1. 2025Reinforcement Learning for Hybrid Charging Stations Planning and Operation Considering Fixed and Mobile Chargers
  2. 2025Reinforcement Learning-based Approach for Vehicle-to-Building Charging with Heterogeneous Agents and Long Term Rewards
  3. 2026Smart charging of large fleets of Electric Vehicles: Independent Multi-Agent Reinforcement Learning approaches
  4. 2026Profit Maximization for Electric Vehicle Charging Stations Using Multiagent Reinforcement Learning ↗
  5. 2024Centralized vs. Decentralized Multi-Agent Reinforcement Learning for Enhanced Control of Electric Vehicle Charging Networks
  6. 2025A Generative Model Enhanced Multi-Agent Reinforcement Learning Method for Electric Vehicle Charging Navigation
  7. 2024Advancements in Electric Vehicle Charging Optimization: A Survey of Reinforcement Learning Approaches
  8. 2022Optimized cost function for demand response coordination of multiple EV charging stations using reinforcement learning