Graph Neural Networks (GNNs) and reinforcement learning techniques are combined in GRAPPLE (GraphSAGE Reinforced with Actor-Proximal Policy Optimization), a revolutionary framework for improving personalized recommendation systems. GRAPPLE allows for dynamic adaptation to changing user preferences and item dynamics by fusing Proximal Policy Optimization (PPO) with GraphSAGE, a powerful representation learning technique. GRAPPLE can now efficiently extract extensive relational information from interaction graphs and capture complex user-item relationships. Adaptive learning techniques allow model to continuously update their recommendation criteria in response to user feedback, increase the precision of recommendations while maintaining the user satisfaction quota that it has. Experiments performed on real-world dataset demonstrate that our algorithm outperforms conventional recommendation methods, demonstrating its superiority in a range of recommendation scenarios as well as its durability and scalability. GRAPPLE represents a significant advancement in recommendation systems by combining GNNs with reinforcement learning methods. It provides a versatile and efficient way to manage interaction patterns and fluctuating user preferences in recommendation jobs.