# Robust Deep Reinforcement Learning for Inverter-based Volt-Var Control in Partially Observable Distribution Networks

arXiv: https://arxiv.org/abs/2408.06776 · HTML: https://mlsanity.com/paper/2408.06776/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 50 references that are in the map.
- First posted on arXiv 13 Aug 2024.

## Builds on (most cited first, 13 of 13)

- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Distributional Reinforcement Learning with Quantile Regression](https://mlsanity.com/paper/1710.10044/) (2017)
- [Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users](https://mlsanity.com/paper/2007.06823/) (2020)
- [Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability](https://mlsanity.com/paper/2107.06277/) (2021)
- [pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems](https://mlsanity.com/paper/1709.06743/) (2017)
- [Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges](https://mlsanity.com/paper/2102.01168/) (2021)
- [Challenges in Markov chain Monte Carlo for Bayesian neural networks](https://mlsanity.com/paper/1910.06539/) (2019)
- [Data-driven Optimal Power Flow: A Physics-Informed Machine Learning Approach](https://mlsanity.com/paper/2006.00544/) (2020)
- [Online Multi-agent Reinforcement Learning for Decentralized Inverter-based Volt-VAR Control](https://mlsanity.com/paper/2006.12841/) (2020)
- [Bayesian State Estimation for Unobservable Distribution Systems via Deep Learning](https://mlsanity.com/paper/1811.02756/) (2018)
- [Two-stage Deep Reinforcement Learning for Inverter-based Volt-VAR Control in Active Distribution Networks](https://mlsanity.com/paper/2005.11142/) (2020)
- [State and Topology Estimation for Unobservable Distribution Systems using Deep Neural Networks](https://mlsanity.com/paper/2104.07208/) (2021)
- [Data-driven Inverter-based Volt/VAr Control for Partially Observable Distribution Networks](https://mlsanity.com/paper/2007.16039/) (2020)

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

- [Partially Observable Residual Reinforcement Learning for PV-Inverter-Based Voltage Control in Distribution Grids](https://mlsanity.com/paper/2506.19353/) (2025)

## Abstract (arXiv)

Inverter-based volt-var control is studied in this paper. One key issue in DRL-based approaches is the limited measurement deployment in active distribution networks, which leads to problems of a partially observable state and unknown reward. To address those problems, this paper proposes a robust DRL approach with a conservative critic and a surrogate reward. The conservative critic utilizes the quantile regression technology to estimate conservative state-action value function based on the partially observable state, which helps to train a robust policy; the surrogate rewards of power loss and voltage violation are designed that can be calculated from the limited measurements. The proposed approach optimizes the power loss of the whole network and the voltage profile of buses with measurable voltages while indirectly improving the voltage profile of other buses. Extensive simulations verify the effectiveness of the robust DRL approach in different limited measurement conditions, even when only the active power injection of the root bus and less than 10% of bus voltages are measurable.