# Consensus Multi-Agent Reinforcement Learning for Volt-VAR Control in Power Distribution Networks

arXiv: https://arxiv.org/abs/2007.02991 · HTML: https://mlsanity.com/paper/2007.02991/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2020.

## Built on by (most cited first, 18 of 18)

- [Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges](https://mlsanity.com/paper/2102.01168/) (2021)
- [PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems](https://mlsanity.com/paper/2111.05969/) (2021)
- [Hierarchical Consensus-Based Multi-Agent Reinforcement Learning for Multi-Robot Cooperation Tasks](https://mlsanity.com/paper/2407.08164/) (2024)
- [A Graph Policy Network Approach for Volt-Var Control in Power Distribution Systems](https://mlsanity.com/paper/2109.12073/) (2021)
- [Networked Multiagent Safe Reinforcement Learning for Low-carbon Demand Management in Distribution Network](https://mlsanity.com/paper/2311.15594/) (2023)
- [An Optimization Method-Assisted Ensemble Deep Reinforcement Learning Algorithm to Solve Unit Commitment Problems](https://mlsanity.com/paper/2206.04249/) (2022)
- [Safe Reinforcement Learning for Power System Control: A Review](https://mlsanity.com/paper/2407.00681/) (2024)
- [Multi-Agent Reinforcement Learning for Energy Networks: Computational Challenges, Progress and Open Problems](https://mlsanity.com/paper/2404.15583/) (2024)
- [Safety Constrained Multi-Agent Reinforcement Learning for Active Voltage Control](https://mlsanity.com/paper/2405.08443/) (2024)
- [Reducing Learning Difficulties: One-Step Two-Critic Deep Reinforcement Learning for Inverter-based Volt-Var Control](https://mlsanity.com/paper/2203.16289/) (2022)
- [An Efficient Distributed Multi-Agent Reinforcement Learning for EV Charging Network Control](https://mlsanity.com/paper/2308.12921/) (2023)
- [A Reinforcement Learning-based Volt-VAR Control Dataset and Testing Environment](https://mlsanity.com/paper/2204.09500/) (2022)
- [RACA: Relation-Aware Credit Assignment for Ad-Hoc Cooperation in Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2206.01207/) (2022)
- [Reducing Action Space: Reference-Model-Assisted Deep Reinforcement Learning for Inverter-based Volt-Var Control](https://mlsanity.com/paper/2210.07360/) (2022)
- [Distributionally Robust Cooperative Multi-Agent Reinforcement Learning via Robust Value Factorization](https://mlsanity.com/paper/2602.11437/) (2026)
- [Decentralized Stochastic Subgradient-type Methods with Communication Compression for Nonsmooth Nonconvex Optimization](https://mlsanity.com/paper/2607.01755/) (2026)
- [A Human-AI Teaming Framework for Deep Reinforcement Learning-Based Voltage Regulation in Distribution Networks](https://mlsanity.com/paper/2607.19667/) (2026)
- [Safety Screening for Voltage Control in Active Distribution Grids via Distributionally Robust Conformal Screening](https://mlsanity.com/paper/2608.30889/) (2026)

## Abstract (arXiv)

Volt-VAR control (VVC) is a critical application in active distribution network management system to reduce network losses and improve voltage profile. To remove dependency on inaccurate and incomplete network models and enhance resiliency against communication or controller failure, we propose consensus multi-agent deep reinforcement learning algorithm to solve the VVC problem. The VVC problem is formulated as a networked multi-agent Markov decision process, which is solved using the maximum entropy reinforcement learning framework and a novel communication-efficient consensus strategy. The proposed algorithm allows individual agents to learn a group control policy using local rewards. Numerical studies on IEEE distribution test feeders show that our proposed algorithm matches the performance of single-agent reinforcement learning benchmark. In addition, the proposed algorithm is shown to be communication efficient and resilient.