This paper explores the theoretical application of reinforcement learning (RL) to dynamic resource management in Continuous Integration and Continuous Delivery (CI/CD) environments like build and test environments. Focusing on the scaling and capacity optimization of virtual machine (VM) pools, the study proposes the use of a Deep Deterministic Policy Gradient (DDPG) model, tailored for environments characterized by continuous action spaces and complex, dynamic demands. The paper delineates a theoretical framework where an RL agent dynamically adapts VM allocations based on real-time requirements, potentially enhancing operational efficiency and reducing costs. Tthe research outlines a conceptual model that leverages the capabilities of RL to address resource allocation challenges inherent to modern software development. The discussion anticipates that the integration of RL could revolutionize traditional resource management strategies by providing more agile, efficient, and cost-effective solutions. Future research directions are suggested, focusing on exploration of alternative RL algorithms for practical implementations in CI/CD environments. This work contributes to the literature by proposing a novel approach to optimizing resource management in CI/CD systems, setting a foundation for future studies and technological advancements in the field.