Reinforcement Learning for Self-Optimizing Infrastructure as Code (IaC)

Abstract

Reinforcement Learning for Self-Optimizing Infrastructure as Code introduces a paradigm shift that fundamentally transforms cloud operations, moving beyond mere infrastructure improvement to reimagine the entire operational model. This article examines how reinforcement learning techniques create autonomous infrastructure systems that continuously evolve through operational feedback loops, eliminating traditional boundaries between deployment, monitoring, and optimization phases. By replacing manual intervention with intelligent, self-directing systems, RL-based approaches revolutionize how organizations interact with cloud environments—transitioning from hands-on management to strategic governance of self-optimizing infrastructure ecosystems. The architecture, implementation challenges, and practical applications showcase how this approach represents not just an advancement in infrastructure tooling but a complete reconceptualization of cloud operations that promises to reshape enterprise IT management fundamentally.