Explainable Reinforcement Learning on Financial Stock Trading using SHAP
This addresses the need for transparency in AI for finance, but it is incremental as it adapts an existing XAI method to a new application.
The authors tackled the lack of explainability in reinforcement learning for financial stock trading by applying SHAP to a deep Q-network, demonstrating its effectiveness on SENSEX and DJIA datasets.
Explainable Artificial Intelligence (XAI) research gained prominence in recent years in response to the demand for greater transparency and trust in AI from the user communities. This is especially critical because AI is adopted in sensitive fields such as finance, medicine etc., where implications for society, ethics, and safety are immense. Following thorough systematic evaluations, work in XAI has primarily focused on Machine Learning (ML) for categorization, decision, or action. To the best of our knowledge, no work is reported that offers an Explainable Reinforcement Learning (XRL) method for trading financial stocks. In this paper, we proposed to employ SHapley Additive exPlanation (SHAP) on a popular deep reinforcement learning architecture viz., deep Q network (DQN) to explain an action of an agent at a given instance in financial stock trading. To demonstrate the effectiveness of our method, we tested it on two popular datasets namely, SENSEX and DJIA, and reported the results.