Tajik Hesarkuchak, Saeed2024-06-112024-06-112024-06-10vt_gsexam:40553https://hdl.handle.net/10919/119384We develop a data-driven approach to Pareto optimal control of large-scale systems, where decision makers know only their local dynamics. Using reinforcement learning, we design a control strategy that optimally balances multiple objectives. The proposed method achieves near-optimal performance and scales well with the total dimension of the system. Experimental results demonstrate the effectiveness of our approach in managing multi-area power systems.ETDenIn CopyrightControlLarge scale systemsData Driven ControlThesis