mlsanityData to 14 Oct 2026

Explainable Reinforcement Learning for assisting Air Traffic Controllers

arXiv 2607.22525 ↗First posted 24 Jul 2026cs.AIcited by 0

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

Abstract (arXiv)

To effectively integrate AI into high-stakes, critical environments such as healthcare, autonomous driving, and aviation--and to advance toward higher levels of automation and seamless human-AI collaboration--building trust in AI-driven solutions is essential. Trust, in turn, is closely linked to the explainability of AI systems. The rapid advancements in AI across various domains have underscored the challenges of establishing trust, raising increasing interest in AI explainability even more when applied to deep learning. In this context, the present work aims to explore the application of explainability techniques to Reinforcement Learning (RL) algorithms, specifically within the safety-critical domain of Air Traffic Control (ATC). Using a simplified ATC environment as an initial testbed, an intelligent agent is trained with a reinforcement learning algorithm to make decisions on alternative flight routes that avoid no-fly zones. As a preliminary explainability approach, a saliency map is employed, providing insights into the input features that most significantly influence the agent's decision-making process.

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 5 of its 12 references that are in the map. First posted on arXiv 24 Jul 2026.

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5 of 12 references.

References · 5

  1. 2017Visualizing and Understanding Atari Agentscited by 141
  2. 2021Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunitiescited by 125
  3. 2020Explainability in Deep Reinforcement Learningcited by 97
  4. 2020CDT: Cascading Decision Trees for Explainable Reinforcement Learningcited by 15
  5. 2021Safety Enhancement for Deep Reinforcement Learning in Autonomous Separation Assurancecited by 8

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