Explainable Reinforcement Learning on Financial Stock Trading using SHAP

TL;DR

This paper introduces a novel explainability method for reinforcement learning in stock trading by applying SHAP to DQN models, enhancing transparency in financial decision-making.

Contribution

It pioneers the use of SHAP for explaining actions of deep reinforcement learning agents in stock trading, addressing a gap in XAI for RL in finance.

Findings

SHAP effectively explains DQN agent actions in stock trading.

The method was validated on SENSEX and DJIA datasets.

Results demonstrate improved interpretability of RL decisions.

Abstract

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.