# Batch-Constrained Reinforcement Learning for Dynamic Distribution Network Reconfiguration

arXiv: https://arxiv.org/abs/2006.12749 · HTML: https://mlsanity.com/paper/2006.12749/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2020.

## Built on by (most cited first, 8 of 8)

- [Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges](https://mlsanity.com/paper/2102.01168/) (2021)
- [Bi-level Off-policy Reinforcement Learning for Volt/VAR Control Involving Continuous and Discrete Devices](https://mlsanity.com/paper/2104.05902/) (2021)
- [Multi-agent assignment via state augmented reinforcement learning](https://mlsanity.com/paper/2406.01782/) (2024)
- [Safe Reinforcement Learning for Power System Control: A Review](https://mlsanity.com/paper/2407.00681/) (2024)
- [Knowledge Transfer in Deep Reinforcement Learning for Slice-Aware Mobility Robustness Optimization](https://mlsanity.com/paper/2203.03227/) (2022)
- [Hybrid Imitation Learning for Real-Time Service Restoration in Resilient Distribution Systems](https://mlsanity.com/paper/2011.14458/) (2020)
- [Exploring a Physics-Informed Decision Transformer for Distribution System Restoration: Methodology and Performance Analysis](https://mlsanity.com/paper/2407.00808/) (2024)
- [Dual-Head Physics-Informed Graph Decision Transformer for Distribution System Restoration](https://mlsanity.com/paper/2508.06634/) (2025)

## Abstract (arXiv)

Dynamic distribution network reconfiguration (DNR) algorithms perform hourly status changes of remotely controllable switches to improve distribution system performance. The problem is typically solved by physical model-based control algorithms, which not only rely on accurate network parameters but also lack scalability. To address these limitations, this paper develops a data-driven batch-constrained reinforcement learning (RL) algorithm for the dynamic DNR problem. The proposed RL algorithm learns the network reconfiguration control policy from a finite historical operational dataset without interacting with the distribution network. The numerical study results on three distribution networks show that the proposed algorithm not only outperforms state-of-the-art RL algorithms but also improves the behavior control policy, which generated the historical operational data. The proposed algorithm is also very scalable and can find a desirable network reconfiguration solution in real-time.