Deep Reinforcement Learning and BigBird-BiLSTM Models in Automated Essay Scoring: An Exploratory Study

This paper evaluates the potential of Deep Reinforcement Learning (DRL) and BigBird-BiLSTM models in enhancing Automated Essay Grading (AEG) systems. Leveraging the Hewlett dataset, the study examines how these models handle semantic features and scalability challenges compared to existing frameworks. Evaluation metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2) highlight the strengths and limitations of each model.