标题： 可解释的人工智能用于雷达资源管理：深度强化学习中的修改版LIME 显示英文标题

标题： Explainable AI for Radar Resource Management: Modified LIME in Deep Reinforcement Learning

摘要： 深度强化学习在决策过程中得到了广泛研究，并在包括雷达资源管理（RRM）在内的各个领域表现出优于传统方法的性能。 然而，神经网络的一个显著局限性是其“黑箱”特性，最近的研究工作越来越多地关注可解释人工智能（XAI）技术，以描述神经网络决策背后的合理性。 一种有前景的XAI方法是局部可解释模型无关解释（LIME）。 然而，LIME中的采样过程忽略了特征之间的相关性。 在本文中，我们提出了一种改进的LIME方法，将深度学习（DL）整合到采样过程中，我们将其称为DL-LIME。 我们在深度强化学习中应用DL-LIME进行雷达资源管理。 数值结果表明，DL-LIME在保真度和任务性能方面都优于传统LIME，两种指标均表现出优越的性能。 DL-LIME还提供了关于在雷达资源管理决策中哪些因素更为重要的见解。 显示英文摘要

摘要： Deep reinforcement learning has been extensively studied in decision-making processes and has demonstrated superior performance over conventional approaches in various fields, including radar resource management (RRM). However, a notable limitation of neural networks is their ``black box" nature and recent research work has increasingly focused on explainable AI (XAI) techniques to describe the rationale behind neural network decisions. One promising XAI method is local interpretable model-agnostic explanations (LIME). However, the sampling process in LIME ignores the correlations between features. In this paper, we propose a modified LIME approach that integrates deep learning (DL) into the sampling process, which we refer to as DL-LIME. We employ DL-LIME within deep reinforcement learning for radar resource management. Numerical results show that DL-LIME outperforms conventional LIME in terms of both fidelity and task performance, demonstrating superior performance with both metrics. DL-LIME also provides insights on which factors are more important in decision making for radar resource management.