Using deep Reinforcement Learning to Optimize the Motivational Incentive Mechanism of Online English Learners

With the rapid development of online education platforms, how to effectively improve learners' motivation has become an important research topic. This study aims to explore the use of deep reinforcement learning (DRL) to optimize the motivational incentive mechanism of online English learners. By constructing a personalized learning path recommendation model based on DRL, this study attempts to enhance learners' learning motivation and improve learning efficiency. Experimental results show that compared with traditional motivation methods, DRL's optimized incentive mechanism significantly improves learners' learning progress, satisfaction, and learning outcomes. In addition, the study also found that personalized learning content and instant feedback have a significant impact on improving learning motivation. However, this study also has problems such as sample selection bias and short-term experimental limitations. Based on these findings, future research can conduct in-depth research in improving sample diversity, extending the research cycle, and exploring applications in more educational fields.