Research on Electric Power Dialogue System Based on Deep Reinforcement Learning

This paper proposes an innovative deep reinforcement learning framework for building a power dialogue system, which aims to optimize the interactive experience and efficiency of power services. Therefore, this paper designs a hybrid reinforcement learning architecture combining Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) to solve the complex and variable dialogue scenarios in the power field. DQN is responsible for the preliminary value evaluation and action selection due to its efficient exploration ability in discrete action space. However, PPO, with its robust policy optimization mechanism, focuses on the fine adjustment of policies to promote the continuous improvement and optimization of dialogue strategies. The method designed in this paper not only improves the response quality and user satisfaction of the dialogue system, but also enhances the robustness and adaptability of the system to the dynamic environment of the power system. It shows a significant performance improvement in the power dialogue task, and can well handle unstructured user requests and perform precise power service operations. And we can maintain high performance and high reliability in complex and changing environments.