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Explainable Reinforcement Learning for assisting Air Traffic Controllers

Primary research

#794

T1new
Topic
unassigned (set during synthesis)
First seen
2026-07-27 13:13:52
Last seen
2026-07-27 13:13:52

Source raw items (1)

  • arXiv2026-07-27 13:13:07
    Explainable Reinforcement Learning for assisting Air Traffic Controllers

    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.