Abstract:

This article provides a overview of the application of reinforcement learning in algorithmic trading. Reinforcement learning is a type of machine learning that involves an agent making a series of decisions and receiving re - wards or punishments based on the outcomes. In algorithmic trading, reinforcement learning has been used to optimize trading strategies, improve portfolio management, and enhance market prediction. The article discusses various reinforcement learning algorithms and approaches from state of the art research and their applications in financial markets. The article also highlights the challenges and limitations of using reinforcement learning in algorithmic trading and suggests potential future directions for research in this field.