Deep-Q Classifier for Predicting Balanced and Imbalanced Features in Cartpole and Lunarlander Dataset

The purpose of this paper is to investigate the impact of Significant Q-Assignment (SQN) on the convenience of multiclass portrayal estimations. To create a framework that blends DQN with existing supervised multiclass classification algorithms, we will utilize a Kaggle benchmark dataset. The findings of this review will provide insight on how to utilize profound support learning methods to enhance multiclass order precision. They have been used in different fields, including picture affirmation, normal language processing, and bioinformatics. The specialist demonstrates one example at each time point, and the climate evaluates the order activity and gives the specialist a prize. The specialist is more attentive to the minority class due to the higher prize for minority class tests. The agent ultimately discovers the optimal classification policy in the data with imbalances, guided by a specific reward function and a beneficial learning environment.