用深度学习改进解释雷达资源管理决策，让黑箱模型更透明。
Explainable AI for Radar Resource Management: Modified LIME in Deep Reinforcement Learning
- 用深度学习优化LIME的采样方式，捕捉特征间相关性。
- 在雷达资源管理任务中，解释精度和任务性能均优于传统LIME。
- 帮助理解雷达决策关键因素，适合需要可解释性的系统设计者。
深度强化学习在决策过程中的应用已广泛研究，并在雷达资源管理（RRM）等多个领域展现出优于传统方法的性能。然而，神经网络的“黑箱”特性是一个显著局限，近年来研究愈发关注可解释人工智能（XAI）技术以揭示神经网络决策背后的逻辑。一种有前景的XAI方法是局部可解释模型无关解释（LIME）。但传统的LIME在采样过程中忽略了特征间的相关性。本文提出一种改进的LIME方法，将深度学习（DL）融入采样过程，称为DL-LIME。我们将DL-LIME应用于深度强化学习进行雷达资源管理。数值结果表明，与传统LIME相比，DL-LIME在解释保真度和任务性能上均表现更优，双重指标均实现提升。同时，DL-LIME还揭示了雷达资源管理决策中更具影响力的因素。
原文摘要 · Abstract (English)
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.
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