Optimization of Computer Network Routing using Reinforcement Learning Algorithm

Conventional network routing is based on strict rules that are inflexible, preventing dynamic adaptation to traffic movement. Our groundbreaking research introduces Q-learning, a machine learning method that transcends network behaviour by allowing real-time learning and traffic optimization. This revolutionary approach enables networks to mimic the strategic choice-making of a seasoned taxi driver navigating in complex shortcuts, leading to improved efficiency and congestion evasion. Defying challenges of computational efficiency and scalability, our results show significant advantages: an impressive 33% delay reduction, near-zero data loss (only 0.1%), and consistent high-speed throughput up to 9.5 Gbps. Importantly, our solution outperforms other AI routing techniques by performing better in stability and interpretability, establishing a new benchmark for adaptive networking technology. This revolutionary work lays the ground for future networks that deploy a mix of AI methods to learn autonomously while being transparent about their decision-making processes, guaranteeing robust infrastructures that can cater to the changing needs of the future seamlessly.