Explainable Reinforcement Learning for assisting Air Traffic Controllers

Explainable RL with saliency maps applied to Air Traffic Control routing

A new arXiv preprint applies explainability techniques to reinforcement learning agents operating in a simplified Air Traffic Control environment, where the agent learns to route flights around no-fly zones. Saliency maps are used as a preliminary explainability mechanism to identify which input features most influence the agent's decisions. The work targets the broader challenge of building trust in AI systems deployed in safety-critical domains.