Published Paper Details:

Predicting stock prices is essential in financial markets, but it can be difficult because of how dynamically the market behaves. Conventional techniques frequently fail to capture this complexity. One possible approach is deep reinforcement learning, or deep Q-Learning (DQL). This essay examines DQL's use in stock price prediction and considers its advantages, disadvantages, and methods. It starts with the basics of DQL and how it relates to financial forecasting before exploring several implementation strategies like experience replay and neural network architectures. Financial market-specific issues are covered, including model evaluation and data pretreatment. Synthesized empirical data contrasting DQL with conventional techniques is presented, demonstrating its effectiveness and outlining potential areas for further investigation. In the end, this review seeks to provide practitioners and scholars with an understanding of DQL's effectiveness in stock price prediction, enabling future developments in this rapidly developing subject.