# A Human-AI Teaming Framework for Deep Reinforcement Learning-Based Voltage Regulation in Distribution Networks

arXiv: https://arxiv.org/abs/2607.19667 · HTML: https://mlsanity.com/paper/2607.19667/

## 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 9 of its 27 references that are in the map.
- First posted on arXiv 22 Jul 2026.

## 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, 9 of 9)

- [Two-Timescale Voltage Control in Distribution Grids Using Deep Reinforcement Learning](https://mlsanity.com/paper/1904.09374/) (2019)
- [Consensus Multi-Agent Reinforcement Learning for Volt-VAR Control in Power Distribution Networks](https://mlsanity.com/paper/2007.02991/) (2020)
- [Deep Reinforcement Learning Based Volt-VAR Optimization in Smart Distribution Systems](https://mlsanity.com/paper/2003.03681/) (2020)
- [Online Multi-agent Reinforcement Learning for Decentralized Inverter-based Volt-VAR Control](https://mlsanity.com/paper/2006.12841/) (2020)
- [Data-Driven Power Flow Linearization: A Regression Approach](https://mlsanity.com/paper/1710.10621/) (2017)
- [Fully Decentralized Reinforcement Learning-based Control of Photovoltaics in Distribution Grids for Joint Provision of Real and Reactive Power](https://mlsanity.com/paper/2008.01231/) (2020)
- [A Fully Distributed Reactive Power Optimization and Control Method for Active Distribution Networks](https://mlsanity.com/paper/1407.5032/) (2014)
- [Bi-Level Volt-VAR Optimization to Coordinate Smart Inverters with Voltage Control Devices](https://mlsanity.com/paper/1912.03596/) (2019)
- [Robust Deep Reinforcement Learning for Volt-VAR Optimization in Active Distribution System under Uncertainty](https://mlsanity.com/paper/2409.18937/) (2024)

## Abstract (arXiv)

The growing penetration of distributed energy resources (DERs) has increased the operational variability of distribution networks, making voltage regulation increasingly challenging. Conventional deep reinforcement learning (DRL) methods exhibit unsafe exploration behavior, slow convergence, and limited reliability, which restrict their applicability in safety-critical power system settings. This paper presents a human-interactive reinforcement learning framework that enhances the safety and robustness of autonomous voltage regulation. The proposed approach integrates a Soft Actor-Critic (SAC) agent with an adaptive Lagrange constraint mechanism to enforce voltage limits, while a human-guidance module provides sensitivity-based corrections through coordinated capacitor-bank and Battery Energy Storage System (BESS) dispatch. These corrections are incorporated into a human-regularized actor loss, enabling the policy to internalize safe and interpretable control behavior. The framework is implemented in the PowerGym-OpenDSS environment using the IEEE 13-node feeder. Simulation results show that the proposed Human-Interactive Lagrangian SAC (HI-LSAC) achieves significantly lower voltage-violation severity and reduced power losses compared with the other baseline methods.