In this paper, we introduce a groundbreaking approach for automating surgical procedures through the implementation of a comprehensive pipeline known as Reinforcement Imitational Diffusion Learning (RIDL). Our focus extends to the application of this innovative framework to enhance the training of surgical robots, including the renowned DaVinci robot and the da Vinci Research Kit (dVRK). The RIDL pipeline incorporates Classic Reinforcement Learning and Imitation Learning, running simultaneously in a dynamic positive feedback loop, aiming to achieve a continuous reciprocal enhancement of learning processes. This integration is further enriched by the assimilation of VR-Enhanced Data, emphasizing the immersive and realistic training environment. The final refinement step involves stable diffusion optimization to fine-tune the learned trajectories. With a specific emphasis on the dVRK, our study underscores the potential of RIDL as a comprehensive and synergistic methodology to automate surgical procedures, offering a refined, adaptable, and contextually sophisticated model for optimizing the precision and efficiency of robotic-assisted surgeries.