As a core enabling technology for trustworthy artificial intelligence， research on the interpretability of machine learning models plays a vital role in automated decision-making systems. Among them， saliency-based explanations， which intuitively reveal the basis of model decisions in the form of heatmaps， have become a key research direction in explainable artificial intelligence. However， existing approaches suffer from fundamental limitations： Perturbation-based methods are constrained by manually designed distributional assumptions， making it difficult to balance computational efficiency and generalization ability； gradient-based methods require prior knowledge of the model’s internal structure and thus fail to explain black-box models； and linear-approximation-based methods introduce model bias due to excessive local simplification. To address these limitations， we propose Polixplain， a policy-gradient-based saliency explanation method for black-box models. By using the Deletion Score as the core reward signal and incorporating a differentiable relaxation technique into the policy gradient optimization process， Polixplain enables end-to-end optimization toward the faithfulness objective. Image classification tasks are adopted as the experimental testbed， where the high-dimensional feature space and intuitive visual representation provide an ideal environment for multidimensional evaluation of explanation methods. Polixplain outperforms a range of existing explanation methods in terms of both explanation faithfulness and the human interpretability of the generated heatmaps.