Sarcouncil Journal of Engineering and Computer Sciences

Abstract: The integration of reinforcement learning (RL) into distributed systems represents a transformative advancement in AI-integrated software engineering. This study explores the potential of RL algorithms, including Q-learning, DQN, and PPO, to optimize key aspects of distributed systems such as resource allocation, load balancing, fault tolerance, and system efficiency. Through extensive experimentation and statistical analysis, we demonstrate that RL-driven approaches significantly outperform traditional methods, achieving improvements of up to 25% in resource utilization, 30% in load distribution fairness, and 40% in fault recovery time. The robustness of RL models is further validated under extreme conditions, with only a 10% degradation in system efficiency compared to 30% for baseline methods. Additionally, the integration of RL with AI-driven software engineering practices, such as modular code, CI/CD pipelines, and automated testing, reduces development time by 20% and error rates by 15%. These findings highlight the adaptability, scalability, and resilience of RL-integrated distributed systems, making them well-suited for dynamic and complex environments. Despite challenges such as computational costs and model interpretability, this research underscores the transformative potential of RL in advancing distributed systems and shaping the future of intelligent, self-optimizing software architectures