Automated multi-objective HPO over 15 environment design choices produces RL-OPF environments that dominate a manual baseline, with objective differencing, action autoscaling, and mixed real/random training data as the most robustly beneficial choices.
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A General Approach of Automated Environment Design for Learning the Optimal Power Flow
Automated multi-objective HPO over 15 environment design choices produces RL-OPF environments that dominate a manual baseline, with objective differencing, action autoscaling, and mixed real/random training data as the most robustly beneficial choices.