{"id":640,"date":"2025-07-29T12:45:52","date_gmt":"2025-07-29T10:45:52","guid":{"rendered":"https:\/\/rl4ces.de\/?p=640"},"modified":"2025-08-15T12:56:27","modified_gmt":"2025-08-15T10:56:27","slug":"publikation-akzeptiert-bei-ecml-2025","status":"publish","type":"post","link":"https:\/\/rl4ces.de\/en\/publikation-akzeptiert-bei-ecml-2025\/","title":{"rendered":"Publication accepted at ECML 2025"},"content":{"rendered":"

Our paper Learning Topology Actions for Power Grid Control: A Graph-Based Soft-Label Imitation Learning Approach<\/a> by Mohamed Hassouna, Clara Holzh\u00fcter, Malte Lehna, Matthijs de Jong, Jan Viebahn, Bernhard Sick and Christoph Scholz has been accepted at European Conference on Machine Learning<\/em> 2025.<\/p>\n\n\n\n

Abstract: The rising proportion of renewable energy in the electricity mix introduces significant operational
challenges for power grid operators. Effective power grid management demands adaptive decisionmaking strategies capable of handling dynamic conditions. With the increase in complexity, more
and more Deep Learning (DL) approaches have been proposed to find suitable grid topologies
for congestion management. In this work, we contribute to this research by introducing a novel
Imitation Learning (IL) approach that leverages soft labels derived from simulated topological action
outcomes, thereby capturing multiple viable actions per state. Unlike traditional IL methods that
rely on hard labels to enforce a single optimal action, our method constructs soft labels that capture
the effectiveness of actions that prove suitable in resolving grid congestion. To further enhance
decision-making, we integrate Graph Neural Networks (GNNs) to encode the structural properties of
power grids, ensuring that the topology-aware representations contribute to better agent performance.
Our approach significantly outperforms its hard-label counterparts as well as state-of-the-art Deep
Reinforcement Learning (DRL) baseline agents. Most notably, it achieves a 17% better performance
compared to the greedy expert agent from which the imitation targets were derived.<\/p>\n