Ai-Based Reinforcement Learning FOR Real-Time Autonomous Systems

Abstract

The rapid evolution of autonomous systems has intensified the need for intelligent decision-making frameworks capable of operating reliably under dynamic, uncertain, and time-critical environments. Traditional rule-based and supervised learning approaches often struggle to adapt in real time due to their limited generalization and delayed response to environmental changes. To address these challenges, this work presents an AI-based reinforcement learning (RL) framework designed for real-time autonomous systems, enabling continuous perception–action–reward optimization through interaction with the environment. The proposed approach integrates deep reinforcement learning with adaptive state representation and policy optimization to support fast decision making, low-latency control, and robust behavior under non-stationary conditions. Key mechanisms such as reward shaping, exploration–exploitation balancing, and online policy refinement are employed to enhance learning stability and convergence speed. The framework is evaluated across representative autonomous scenarios, including navigation, obstacle avoidance, and resource-aware control, demonstrating improved responsiveness, adaptability, and operational efficiency compared to conventional control and learning methods. Experimental results indicate that the proposed RL-driven architecture significantly reduces decision latency while maintaining high task success rates, making it suitable for safety-critical and real-time autonomous applications.