mlsanityData to 14 Oct 2026

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

arXiv 2506.20916 ↗First posted 26 Jun 2025cs.LGcited by 0

On the map: Deep reinforcement learning methods → Interpretable and logic-guided RL

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Interpretable and logic-guided RL, one of 15 directions in Deep reinforcement learning methods. Builds on 9 of its 18 references that are in the map. First posted on arXiv 26 Jun 2025.

Builds on

9 of 18 references.

References · 9

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  3. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  4. 2015Continuous control with deep reinforcement learningcited by 3,407
  5. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
  6. 2019Explainable Machine Learning in Deploymentcited by 211
  7. 2020Deep Reinforcement Learning Control for Radar Detection and Tracking in Congested Spectral Environmentscited by 15
  8. 2020A Modified Perturbed Sampling Method for Local Interpretable Model-agnostic Explanationcited by 13
  9. 2020Quality of service based radar resource management using deep reinforcement learningcited by 3

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  8. 2025Multi-Objective Reinforcement Learning for Cognitive Radar Resource Management