This paper introduces an AI-based Automatic Ground Collision Avoidance System (AGCAS) for advanced jet trainers. It utilizes a customized Soft Actor-Critic (SAC) algorithm, integrated with a custom Convolutional Neural Network (CNN) and hyperparameter optimization, to enhance safety and operational capabilities by effectively avoiding ground collisions within limited observation spaces.

This paper evaluates the use of AI-based reinforcement learning agents for developing an AI-assisted pilot training module for specific aircraft aerobatic maneuvers. It demonstrates that AI can learn and execute various complex maneuvers with a quality comparable to professional pilots, utilizing both real pilot data and artificially created trajectories.