Financial Reinforcement Learning

Financial reinforcement learning (FinRL) aims to develop AI agents capable of making optimal financial decisions, such as portfolio optimization and algorithmic trading, by learning from market data and maximizing reward functions. Current research emphasizes the use of deep reinforcement learning (DRL) algorithms, often incorporating convolutional neural networks (CNNs), recurrent neural networks (RNNs like LSTMs and GRUs), and large language models (LLMs) to process diverse data types including textual earnings reports and numerical market data. This field is significant because it offers the potential to improve investment strategies, risk management, and the overall efficiency of financial markets, while also driving advancements in explainable AI and the development of robust, trustworthy AI systems for financial applications.