Explainable Reinforcement Learning for Air Traffic Control: Saliency Maps for Safer AI Decisions

Forum topic · 小凯 · 2026-07-28

Summary

This arXiv paper (2607.22525) by Anduel Mehmeti, Gabriella Gigante, and Salvatore Venticinque explores applying explainability techniques to Reinforcement Learning (RL) in the safety-critical domain of Air Traffic Control (ATC). The authors argue that integrating AI into high-stakes environments such as healthcare, autonomous driving, and aviation requires trust, which is closely tied to the explainability of AI systems—a challenge heightened when deep learning is involved. As an initial testbed, the work uses a simplified ATC environment in which an RL agent is trained to make rerouting decisions that avoid restricted (no-fly) zones. As a preliminary explainability approach, the authors employ saliency maps to reveal which input features most significantly influence the agent's decision-making process. The work represents an early step toward transparent, trustworthy human-AI collaboration in air traffic management. This post on zhichai.net summarizes the paper's motivation, methodology, and initial findings for readers interested in machine learning, explainable AI, and aviation safety.

Paper Overview

Field: Machine Learning
Authors: Anduel Mehmeti, Gabriella Gigante, Salvatore Venticinque
Published: 2026-07-24
arXiv: 2607.22525

Abstract

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.

Key Contributions

Explores the application of explainability techniques to Reinforcement Learning (RL) algorithms in the safety-critical domain of Air Traffic Control (ATC).
Uses a simplified ATC environment as an initial testbed.
Trains an RL agent to make alternative-route decisions that avoid no-fly zones.
Applies saliency maps as a preliminary explainability method, providing insight into which input features most significantly influence the agent's decision-making process.

Why It Matters

Explainability is a prerequisite for deploying RL in safety-critical settings like air traffic management, where human controllers must be able to trust and verify AI-driven recommendations. This work offers an early framework for combining RL-based route planning with interpretable visual explanations, laying groundwork for future human-AI collaboration in aviation.

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