Research on the Natural Processing Technology of Human Interactive Behavior in VR Environment

This paper proposes an innovative algorithm based on the combination of deep learning and reinforcement learning, aiming to optimize the naturalness and real-time performance of human interactive behavior in VR environment. The algorithm first uses a deep learning model to analyze the user's real-time posture data and predict its future actions. The action decision is dynamically adjusted through the reinforcement learning mechanism to ensure the smooth transition and natural performance of the interactive behavior. In this way, the freeze and unnatural transition in the virtual environment can be effectively reduced, and the coherence of the action and the immediacy of the feedback can be improved. To verify the effectiveness of this method, this paper designs a series of experiments covering multiple VR application scenarios, including virtual games and immersive training. The experiment results indicate that the proposed algorithm is superior to the conventional one in nature, real time and precision. Specifically, the naturalness of the interactive behavior is improved by about 20 %, the real-time response speed is improved by 15 %, and the error of the action accuracy is reduced by 10 %. These experimental data show that the proposed algorithm not only effectively solves the shortcomings of the traditional method, but also significantly enhances the user's immersion and interactive experience.