Optimizing Explainable Deep Q-Learning via SHAP, LIME, & Policy Visualization
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Date
2025-06
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Addis Ababa University
Abstract
Reinforcement learning (RL) has demonstrated remarkable promise in sequential
decision-making tasks; however, its interpretability issues continue to be a hindrance in
high-stakes domains that demand regulatory compliance, transparency, and trust. Posthoc
explainability has been investigated in recent research using techniques like SHAP
and LIME; however, these methods are frequently isolated from the training process and
lack cross-domain evaluation. In order to fill this gap, we propose an explainable Deep
Q-Learning (DQL) framework that incorporates explanation-aligned reward shaping and
model-agnostic explanation techniques into the agent’s learning pipeline. The framework
exhibits broad applicability as it is tested in both financial settings and traditional
control environments. According to experimental findings, the explainable agent continuously
performs better than the baseline in terms of explanation fidelity, average reward,
and convergence speed. In CartPole, the agent obtained a LIME fidelity score of 87.2%
versus 63.5% and an average reward of 190 versus 130 for the baseline. It produced an
89.10% win ratio, a Sharpe Ratio of 0.4782, and a return of 154.32% in the financial
domain. The development of transparent and reliable reinforcement learning systems
is aided by these results, which demonstrate that incorporating explainability into RL
enhances interpretability as well as stability and performance across domains.
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Keywords
Deep Q-Learning, Explainable AI, SHAP, LIME, Reinforcement Learning, Policy Visualization