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Although Reinforcement Learning (RL) exhibits significant potential for refining intricate control and decision-making systems, Deep Reinforcement Learning (DRL) suffers from a distinct lack of explainability. This limitation restricts its deployment within highly regulated domains, including manufacturing, finance, and healthcare. The opacity of DRL decision-making introduces structural challenges that impede operational efficiency and resource management—an issue that intensifies alongside ongoing algorithmic advancements. While current trends favor transitioning from traditional Experience Replay configurations to A3C architectures, the latter imposes substantially greater resource demands. Furthermore, despite continuous efforts to refine selection mechanisms within Experience Replay, repository capacities generally remain elevated. In this study, we evaluate a Deep Convolutional Q-learning agent across 20 Atari environments while deliberately downsizing the Experience Replay capacity from 1×106 to 5×102. Our findings indicate that lowering the capacity from 1×104 to 5×103 does not meaningfully degrade performance rewards, thereby establishing a viable strategy for resource-efficient DRL frameworks. To clarify agent choices and correlate them with inherent game mechanics, we introduce a novel methodology: visualizing the Experience Replay mechanism utilizing the Deep SHAP Explainer. This framework enhances domain comprehension and delivers transparent, interpretable accounts, though capacity constraints must be managed carefully to mitigate overfitting. Ultimately, this research validates the practicality of minimizing Experience Replay storage and supports the integration of transparent explanations via the Deep SHAP Explainer to advance resource conservation.

Machine learning (ML) techniques have been applied in diverse manners to combat the COVID-19 pandemic. Predictive ML models have been constructed, for example, for both diagnostic and prognostic objectives, utilizing varying data modalities such as textual, visual, and structured formats. Because of the distinct characteristics of this disease and its temporal transformation, a comprehensive understanding of the central elements driving the clinical trajectory of COVID-19 in individual patients remains incomplete. Throughout all phases of this study, a medical specialist was actively engaged via a human-in-the-loop framework. This collaborative element represents a vital yet frequently overlooked component of the ML and knowledge extraction (KE) workflow. Our findings indicate that explainable artificial intelligence (XAI) can offer substantial reinforcement to this dimension of ML and KE. This investigation centered on employing ML for knowledge extraction across two distinct operational scenarios. In the initial scenario, we sought to determine if integrating data regarding the dominant COVID-19 variant affects the predictive performance of the ML models. In the subsequent scenario, we concentrated on prognostic classification models designed to predict a patient's requirement for intensive care unit admission, integrating various XAI methodologies. We implemented nine distinct ML techniques, specifically XGBoost, CatBoost, LightGBM, logistic regression, Naive Bayes, random forest, SGD, SVM-linear, and SVM-RBF. The predictive quality of these models was evaluated using precision, accuracy, and AUC metrics. Following this evaluation, knowledge extraction was performed on the highest-performing models through two discrete pathways: (a) automated feature extraction using forward stepwise selection (FSS); and (b) the identification of attributes and their interactions using model explainability tools. Both sets of features were evaluated against a baseline configuration of attributes selected a priori by medical professionals using domain expertise. Our empirical tests demonstrated that incorporating data on the prevailing COVID-19 variant did not alter the performance of the generated ML models. Furthermore, the results indicated that human medical experts displayed significantly higher precision in identifying meaningful attributes compared to the automated FSS approach. Explainability tools identified nearly identical sets of attributes as the medical expert, while additionally uncovering noteworthy attribute interactions that the specialist subsequently evaluated from a clinical perspective. The outcomes of this investigation and their broader implications are examined.