# Model-Free DRL Control for Power Inverters: From Policy Learning to Real-Time Implementation via Knowledge Distillation

arXiv: https://arxiv.org/abs/2603.07964 · HTML: https://mlsanity.com/paper/2603.07964/

## 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 5 of its 27 references that are in the map.
- First posted on arXiv 9 Mar 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, 5 of 5)

- [FitNets: Hints for Thin Deep Nets](https://mlsanity.com/paper/1412.6550/) (2014)
- [Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing](https://mlsanity.com/paper/1910.05316/) (2019)
- [Focal and Global Knowledge Distillation for Detectors](https://mlsanity.com/paper/2111.11837/) (2021)
- [1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities](https://mlsanity.com/paper/2503.14858/) (2025)
- [Fusion of Model-free Reinforcement Learning with Microgrid Control: Review and Vision](https://mlsanity.com/paper/2206.11398/) (2022)

## Abstract (arXiv)

In response to the trade-off between control performance and computational burden hindering the deployment of Deep Reinforcement Learning (DRL) in power inverters, this paper presents a novel model-free control framework leveraging policy distillation. To handle the convergence instability and steady-state errors inherent in model-free agents, an error energy-guided hybrid reward mechanism is established to theoretically constrain the exploration space. More specifically, an adaptive importance weighting mechanism is integrated into the distillation architecture to amplify the significance of fluctuation regions, ensuring high-quality transfer of transient control logic by mitigating the observational bias dominated by steady-state data. This approach efficiently compresses the heavy DRL policy into a lightweight neural network, retaining the desired control performance while overcoming the computational bottleneck during deployment. The proposed method is validated through a hardware-based kilowatt-level experimental platform. Experimental comparison results with traditional methods demonstrate that the proposed technique reduces inference time to the microsecond level and achieves superior transient response speed and parameter robustness.