Automated co-design of high-performance thermodynamic cycles via graph-based hierarchical reinforcement learning

This study introduces a graph-based hierarchical reinforcement learning approach for the automated co-design of high-performance thermodynamic cycles. It encodes cycles as graphs, uses a deep learning surrogate for decoding, and employs a hierarchical RL framework for structural evolution and parameter optimization.