Reinforcement Learning Applied to Perovskite Thin Films
Haruto Rahimi
Corresponding
Technical University of Munich
Nusrat Sen
University of New South Wales
Abstract
Quantifying perovskite thin films is a prerequisite for evidence-based policy on power distribution feeders. The framework was implemented and its predictions benchmarked using reinforcement learning. The proposed controller improved settling time by 29% compared with the baseline. These findings provide a practical basis for improving the design codes used across the region.
Keywords:
perovskite thin filmsreinforcement learningair qualityremote sensingsustainability
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