# An Efficient Distributed Multi-Agent Reinforcement Learning for EV Charging Network Control

arXiv: https://arxiv.org/abs/2308.12921 · HTML: https://mlsanity.com/paper/2308.12921/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 26 references that are in the map.
- First posted on arXiv 24 Aug 2023.

## Builds on (most cited first, 6 of 6)

- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Consensus Multi-Agent Reinforcement Learning for Volt-VAR Control in Power Distribution Networks](https://mlsanity.com/paper/2007.02991/) (2020)
- [Optimal Demand Response Using Device Based Reinforcement Learning](https://mlsanity.com/paper/1401.1549/) (2014)
- [Dynamic Scheduling for Charging Electric Vehicles: A Priority Rule](https://mlsanity.com/paper/1602.00372/) (2016)
- [A Survey of Algorithms for Distributed Charging Control of Electric Vehicles in Smart Grid](https://mlsanity.com/paper/1911.06500/) (2019)
- [Federated Reinforcement Learning for Real-Time Electric Vehicle Charging and Discharging Control](https://mlsanity.com/paper/2210.01452/) (2022)

## Built on by (most cited first, 1 of 1)

- [Centralized vs. Decentralized Multi-Agent Reinforcement Learning for Enhanced Control of Electric Vehicle Charging Networks](https://mlsanity.com/paper/2404.12520/) (2024)

## Abstract (arXiv)

The increasing trend in adopting electric vehicles (EVs) will significantly impact the residential electricity demand, which results in an increased risk of transformer overload in the distribution grid. To mitigate such risks, there are urgent needs to develop effective EV charging controllers. Currently, the majority of the EV charge controllers are based on a centralized approach for managing individual EVs or a group of EVs. In this paper, we introduce a decentralized Multi-agent Reinforcement Learning (MARL) charging framework that prioritizes the preservation of privacy for EV owners. We employ the Centralized Training Decentralized Execution-Deep Deterministic Policy Gradient (CTDE-DDPG) scheme, which provides valuable information to users during training while maintaining privacy during execution. Our results demonstrate that the CTDE framework improves the performance of the charging network by reducing the network costs. Moreover, we show that the Peak-to-Average Ratio (PAR) of the total demand is reduced, which, in turn, reduces the risk of transformer overload during the peak hours.