DEEP REINFORCEMENT LEARNING ON STOCK DATA

Abstract

This study proposes using Deep Reinforcement Learning (DRL) for stock trading decisions and prediction. DRL is a machine learning technique that enables agents to learn optimal strategies by interacting with their environment. The proposed model surpasses traditional models and can make informed trading decisions in real-time. The study highlights the feasibility of applying DRL in financial markets and its advantages in strategic decision- making. The model's ability to learn from market dynamics makes it a promising approach for stock market forecasting. Overall, this paper provides valuable insights into the use of DRL for stock trading decisions and prediction, establishing a strong case for its adoption in financial markets. Keywords: reinforcement learning, stock market, deep reinforcement learning.