In the domains of robotics and industrial process control, few emerging technologies have garnered as much interest and industry excitement as deep reinforcement learning (Deep RL). However, despite highly publicised results in games and simulated physical challenges, deploying these controllers in physical systems introduces instability, heavy data requirements, and safety risks.
This technical whitepaper compares deep RL to Luffy’s next-generation adaptive AI technology. We demonstrate that by challenging the field’s core assumptions and casting off the deep learning paradigm, adaptive neural control offers a more practical and robust technology for real-world industrial systems. The paper highlights key performance differences, use cases, and engineering implications.
