Reactive Programming

Reactive programming focuses on building systems that continuously interact with their environment by processing streams of data, enabling efficient responses to dynamic events. Current research emphasizes applying reactive principles to diverse areas, including reinforcement learning (with algorithms like Soft Actor-Critic being adapted for variable control rates), neural network control in reactive systems (with a focus on explainable AI for improved transparency), and network programming (using models like extended finite-state machines to design adaptable network services). This approach offers significant advantages in resource efficiency, adaptability, and the development of robust, explainable AI systems for various applications, from robotics and machine learning to 6G network orchestration.