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Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents

, , , , and . Energy and AI, (2023)
DOI: 10.1016/j.egyai.2023.100276

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Learning to run a Power Network Challenge: a Retrospective Analysis., , , , , , , , , and . CoRR, (2021)Exploring grid topology reconfiguration using a simple deep reinforcement learning approach., , , , and . CoRR, (2020)Critical Transition Analysis of the Deterministic Wind-Driven Ocean Circulation - A Flux-Based Network Approach., and . Int. J. Bifurc. Chaos, (2014)Learning to run a Power Network Challenge: a Retrospective Analysis., , , , , , , , , and . NeurIPS (Competition and Demos), volume 133 of Proceedings of Machine Learning Research, page 112-132. PMLR, (2020)Continuation of probability density functions using a generalized Lyapunov approach., , , , , and . J. Comput. Phys., (2017)Continuation of Probability Density Functions using a Generalized Lyapunov Approach., , , , , and . CoRR, (2020)Hierarchical Reinforcement Learning for Power Network Topology Control., , and . CoRR, (2023)Sensitivity and resilience of the climate system: A conditional nonlinear optimization approach., and . Commun. Nonlinear Sci. Numer. Simul., 22 (Issues): 13-22 (2015)Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents., , , , and . CoRR, (2023)OMUSE: Oceanographic multipurpose software environment., , , , , , and . eScience, page 399. IEEE Computer Society, (2016)