A shift in the way we generate and distribute electricity from few, large-scale units; to many, small-scale, decentralized units has created power distribution complications that cannot be managed by one centralized authority. We hypothesize that through the utilization of photonic-based computing, physics-based hierarchical machine learning frameworks will allow for optimized grid operation. Behavioral analysis of trained agents demonstrates that local, modular control of microgrids could provide a reliable alternative to the current method of power grid control. It is our vision that these agents could discover new methods for optimal grid control.