# Adversarial Training for a Continuous Robustness Control Problem in Power Systems

arXiv: https://arxiv.org/abs/2012.11390 · HTML: https://mlsanity.com/paper/2012.11390/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2020.

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

- [Learning to run a Power Network Challenge: a Retrospective Analysis](https://mlsanity.com/paper/2103.03104/) (2021)
- [Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges](https://mlsanity.com/paper/2102.01168/) (2021)
- [Learning to run a power network with trust](https://mlsanity.com/paper/2110.12908/) (2021)
- [Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents](https://mlsanity.com/paper/2304.00765/) (2023)
- [A Search-Based Testing Approach for Deep Reinforcement Learning Agents](https://mlsanity.com/paper/2206.07813/) (2022)
- [HUGO -- Highlighting Unseen Grid Options: Combining Deep Reinforcement Learning with a Heuristic Target Topology Approach](https://mlsanity.com/paper/2405.00629/) (2024)
- [Improving Robustness of Reinforcement Learning for Power System Control with Adversarial Training](https://mlsanity.com/paper/2110.08956/) (2021)
- [Bayesian Reinforcement Learning for Automatic Voltage Control under Cyber-Induced Uncertainty](https://mlsanity.com/paper/2305.16469/) (2023)

## Abstract (arXiv)

We propose a new adversarial training approach for injecting robustness when designing controllers for upcoming cyber-physical power systems. Previous approaches relying deeply on simulations are not able to cope with the rising complexity and are too costly when used online in terms of computation budget. In comparison, our method proves to be computationally efficient online while displaying useful robustness properties. To do so we model an adversarial framework, propose the implementation of a fixed opponent policy and test it on a L2RPN (Learning to Run a Power Network) environment. This environment is a synthetic but realistic modeling of a cyber-physical system accounting for one third of the IEEE 118 grid. Using adversarial testing, we analyze the results of submitted trained agents from the robustness track of the L2RPN competition. We then further assess the performance of these agents in regards to the continuous N-1 problem through tailored evaluation metrics. We discover that some agents trained in an adversarial way demonstrate interesting preventive behaviors in that regard, which we discuss.