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- Technical University of MunichDE11 papers
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cs.LG2024★ 7 cited
Graph Reinforcement Learning for Power Grids: A Comprehensive Survey
Mohamed Hassouna, Clara Holzhüter, Pawel Lytaev +3
The increasing share of renewable energy and distributed electricity generation requires the development of deep learning approaches to address the lack of flexibility inherent in…
cs.LG2023★ 27 cited
Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents
Malte Lehna, Jan Viebahn, Christoph Scholz +2
The operation of electricity grids has become increasingly complex due to the current upheaval and the increase in renewable energy production. As a consequence, active grid manage…
cs.LG2019★ 12 cited
SGAS: Sequential Greedy Architecture Search
Guohao Li, Guocheng Qian, Itzel C. Delgadillo +3
Architecture design has become a crucial component of successful deep learning. Recent progress in automatic neural architecture search (NAS) shows a lot of promise. However, disco…