This research introduces a novel approach that leverages Large Language Models to generate control policies as readable computer programs for complex dynamic systems.

• Combines LLMs with evolutionary algorithms to create optimized control policies
• Represents controllers as standard Python programs, making them interpretable and transparent
• Demonstrates effectiveness on classical control problems like pendulum swing-up
• Bridges the gap between AI capabilities and traditional engineering control systems

This innovation matters for engineering because it transforms how we develop controllers for physical systems - creating human-readable solutions that can be verified, modified, and deployed with greater confidence than black-box alternatives.

Synthesizing Interpretable Control Policies through Large Language Model Guided Search