Deep Reinforcement Learning and Decision Analytics for Integrated Energy Systems

This project will develop a novel deep reinforcement learning approach that can manage distributed or tightly coupled multi-agent systems utilizing deep neural networks for automatic system representation, modeling, and end-to-end learning. This new control method will enable complex, nonlinear system optimization over timescales from milliseconds to months.