<ul><li>Reinforcement Learning (RL) is a promising approach to power network control (PNC) for optimizing power grid topologies.</li><li>The Learning To Run a Power Network (L2RPN) competitions have accelerated research in RL-based methods for power grid optimization.</li><li>This survey provides a comprehensive overview of RL applications for power grid topology optimization, categorizing existing techniques and highlighting key design choices.</li><li>The survey also presents a comparative study evaluating the practical effectiveness of commonly applied RL-based methods and identifies open research challenges.</li></ul>

Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and Challenges

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