Autonomous AI Agents: Reinforcement Learning for Self-Improving Systems

Abstract

Autonomous AI agents are transforming industries by enabling self-learning systems that adapt to dynamic environments. This paper explores the use of reinforcement learning (RL) in developing AI agents capable of self-improvement and decision-making without human intervention. We present a hierarchical RL framework that enhances agent performance in robotics, autonomous vehicles, and financial trading. Experimental evaluations on simulated and real-world datasets demonstrate the efficiency of the proposed system in achieving optimal policies with minimal human supervision. Challenges related to reward shaping, scalability, and explainability are also discussed.