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Autonomous coding is evolving from better prompting to better control systems, with engineers wrapping agents in goals, evaluators, and loops.
This paper proposes PE-MHL, a Physics-Encoded Modular Hybrid Layer framework that incrementally refines a physics-based model with data-driven sub-models, providing theoretical convergence guarantees and outperforming monolithic networks on control benchmarks.
A unified Python framework using PPO-based deep reinforcement learning for optimizing HVAC control with economizer logic and CO2-constrained ventilation is presented, showing improved energy efficiency and temperature stability over traditional PID controllers.