mlsanityData to 7 Oct 2026

Explainable Reinforcement Learning on Financial Stock Trading using SHAP

arXiv 2208.08790 ↗First posted 18 Aug 2022cs.AIcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 14 of its 33 references that are in the map. First posted on arXiv 18 Aug 2022.

Builds on

14 of 33 references.

References · 14

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
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  4. 2019Explainable Reinforcement Learning Through a Causal Lenscited by 177
  5. 2020Explainable Reinforcement Learning: A Surveycited by 139
  6. 2019Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Surveycited by 93
  7. 2017Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciencescited by 84
  8. 2018Relational Deep Reinforcement Learningcited by 72
  9. 2019Shapley Q-value: A Local Reward Approach to Solve Global Reward Gamescited by 71
  10. 2018Composable Deep Reinforcement Learning for Robotic Manipulationcited by 70
  11. 2021A Survey on Interpretable Reinforcement Learningcited by 70
  12. 2019Interestingness Elements for Explainable Reinforcement Learning: Understanding Agents' Capabilities and Limitationscited by 50
2 more
  1. 2019Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based roboticscited by 21
  2. 2021Application of deep reinforcement learning for Indian stock trading automationcited by 2

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Cited by year

Citing papers per year: 2023: 2.

2 papers.

  1. 2023A Comprehensive Review on Financial Explainable AIcited by 10
  2. 2023Know, Grow, and Protect Net Worth: Using ML for Asset Protection by Preventing Overdraft Feescited by 0