Self-Learning AI Agents; Architectures and Challenges

Self-learning AI agents represent a shift from static machine learning systems to adaptive, continuously evolving entities that improve through interaction with their environment. These agents operate on feedback-driven loops, where they observe inputs, take actions, receive feedback, and update their internal models accordingly. Unlike traditional systems that require retraining on new data, self-learning agents refine their behavior in real time, enabling them to handle dynamic environments, l