Researchers use graph-based reinforcement learning to automatically design more efficient thermodynamic cycles, overcoming limitations of traditional expert-driven methods.

Novel approach encodes thermodynamic cycles as graphs with components and connections as nodes and edges, following grammatical constraints

Deep learning-based thermophysical surrogate model enables stable graph decoding and simultaneous optimization of global parameters

Hierarchical reinforcement learning framework uses a high-level manager to explore structural variations and a low-level worker to optimize parameters

Method addresses scalability issues in traditional design approaches that rely on expert knowledge or exhaustive enumeration

Framework enables automated discovery of high-performance energy conversion cycles previously difficult to identify with conventional methods