Incorporating Deep Q -- Network with Multiclass Classification
  Algorithms
Noopur Zambare Ravindranath Sawane
In this study, we explore how Deep Q-Network (DQN) might improve the
functionality of multiclass classification algorithms. We will use a benchmark
dataset from Kaggle to create a framework incorporating DQN with existing
supervised multiclass classification algorithms. The findings of this study
will bring insight into how deep reinforcement learning strategies may be used
to increase multiclass classification accuracy. They have been used in a number
of fields, including image recognition, natural language processing, and
bioinformatics. This study is focused on the prediction of financial distress
in companies in addition to the wider application of Deep Q-Network in
multiclass classification. Identifying businesses that are likely to experience
financial distress is a crucial task in the fields of finance and risk
management. Whenever a business experiences serious challenges keeping its
operations going and meeting its financial responsibilities, it is said to be
in financial distress. It commonly happens when a company has a sharp and
sustained recession in profitability, cash flow issues, or an unsustainable
level of debt.