Explainable reinforcement learning for trading decisions

This article explores the application of Explainable Reinforcement Learning (XRL) in financial trading decisions, addressing the critical need for transparency and interpretability in AI-driven trading strategies. The study aims at understanding how to improve traditional reinforcement learning models which can be viewed as black-box systems such that they allow explainable insights without affecting performance. Through case studies, real-life applications, and comparative studies, the article investigates some of the XRL techniques, including the model-agnostic techniques and the hybrid techniques, to provide a better insight into the trading algorithms. The paper presents the following significant results, namely, explainable models are effective to enhance trust, mitigate risks, and allow human control over algorithmic trading. Moreover, the findings stress that explainable RL advances the transparency but creates complications concerning the model complexity and computational expenses. The article ends with the recommendations to continue investigating the hybrid XRL frameworks and outlines future research to make reinforcement learning models more ethical, accountable, and efficient in regards to the process of financial decision-making.