Development and Evaluation Model of Korean Conversation Robot Based on Deep Reinforcement Learning

With the rapid development of deep learning and Reinforcement Learning (RL) technology, this research is devoted to the development and evaluation of Korean conversation robot based on Deep RL (DRL). This research designs and implements an advanced dialogue model by comprehensively considering the grammar, social culture and other characteristics of Korean context, so as to improve the performance of robots in complex Korean dialogue tasks. In the development stage of the model, this study introduces the deep Double Q Network (DDQN) to solve the over-estimation problem in traditional Q-learning. The application of this network structure enables our Korean conversation robot to learn and optimize dialogue strategies more stably, so as to realize more natural and smooth reply generation. Through a large number of experiments, we have confirmed the effectiveness of DDQN in improving the model performance. In the evaluation stage of the model, we extensively tested the performance of the model in different dialogue scenarios. The experimental results show that our model has achieved remarkable success rate in Korean dialogue tasks, and surpassed the traditional methods in key indicators such as user satisfaction. This proves the adaptability and superiority of our model to Korean context. This study provides useful experience and insights for the application of DRL in the field of Korean conversation robot, and provides strong support for the further research and development of multilingual dialogue system.