Self-Learning AI Agents for Adaptive Decision-Making in Dynamic Environment

Abstract

Intelligent systems that can adjust to constantly shifting conditions are necessary in dynamic contexts like financial markets, smart grids, autonomous cars, smart cities, and cyber security systems. Conventional rule-based systems are unable to adapt to real-time fluctuations and uncertainty. In order to facilitate adaptive decision-making, this study suggests a Self-Learning AI Agent framework that makes use of Reinforcement Learning (RL), Deep Neural Networks (DNN), and memory-augmented architectures. Without human assistance, the suggested model optimises decision results, dynamically updates its policy, and continually learns from environmental feedback. When compared to static AI models, experimental evaluation shows increased performance, quicker convergence, and higher flexibility. The following keywords are used: AI agents, adaptive systems, reinforcement learning, dynamic environments, and autonomous decision-making