Reinforcement learning has shown strong potential for sequential control problems, providing an effective framework for adaptive policy derivation in complex and dynamic environments. However, standalone RL controllers may behave unstably or too aggressively, particularly in safety-critical applications where constrain...
P. Michailidis, Iakovos T. Michailidis, F. Minelli et al.· Engineer· 0 citations
A comprehensive review of RL-based control applications across RES-integrated energy domains, including power grids, microgrids, and building energy systems, identifies emerging trends and highlights dominant design patterns across power grid, microgrid, and building-level applications.
P. Michailidis, F. Minelli, H. Coban et al.· Infrastructures· 0 citations
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