The complexity and dynamism of container orchestration in Kubernetes environments present significant challenges for traditional scheduling mechanisms, often leading to suboptimal resource utilization and performance. This paper proposes a theoretical framework for enhancing the Kubernetes scheduler using Reinforcement Learning (RL), specifically the Deep Q-Network (DQN) algorithm. The adaptive scheduling framework aims to optimize workload allocation by learning and predicting efficient scheduling policies over time. We integrate RL principles with Kubernetes scheduling components to improve decision-making processes. The proposed architecture dynamically adapts to changing cluster conditions and workload characteristics, offering potential benefits in resource allocation efficiency, reduced operational costs, and enhanced performance. We also address the challenges of data collection, model training, and balancing decision accuracy with computational overhead. This work lays the groundwork for future research in intelligent container orchestration.