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Enhancing reinforcement learning controllers with GAN-generated data and transfer learning
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AI
DOI：10.1080/18824889.2025.2527471.png)
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Abstract
En 中文
This study addresses the challenge of data scarcity in training reinforcement learning (RL) controllers for power system economic dispatch problems (EDP) by integrating Generative Adversarial Network (GAN)-generated synthetic data and transfer learning (TL). Traditional data collection for power systems may face limitations like privacy concerns hindering the performance of deep neural network-based controllers. To overcome this, a GAN-based framework is proposed to generate synthetic load demand data, preserving characteristics of real datasets. A TL technique is then employed to fine-tune a Twin Delayed Deep Deterministic Policy Gradient (TD3) agent, pretrained in a synthetic environment, into a target environment with real-world data. Experiments evaluate three GAN-generated datasets, including scenarios with mode collapse, and compare results against regression-based data generation methods. Key findings demonstrate that even low-quality synthetic data, when combined with TL, significantly enhances RL performance. For instance, a mode-collapsed GAN model reduced test operation cost by 54.7% and power unbalance by 89.9% compared to a baseline TD3 agent. This work highlights the potential of synthetic data augmentation and TL in data-scarce power system applications, offering a viable pathway to improve controller performance without additional real-world data collection.
Keywords:
Economic dispatch
GAN
transfer learning
reinforcement learning
twin delayed DDPG
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