Robotic systems rely on a series of connected control cycles, each with its own time limit and deadline that is crucial to the overall operation. The work was tested successfully in simulations and on a UniTree Go2, a “robotic dog” available on the commercial market. Known as Dynamic Time Reinforcement Learning (DTRL), is the first reinforcement-learning framework designed specifically for cyber-physical systems operating under dynamic timing requirements. Mengyu LiuIn reinforcement learning, an AI agent “learns” to improve its decision-making capacity through trial-and-error. By combining adaptive neural network inference with real-time systems principles, the framework enables AI controllers to better utilize available computing resources while maintaining predictable timing behavior required for safety-critical applications.