cs.LG · 2604.24338 Copy arXiv ID · Apr 27, 2026 Save Perfecting Aircraft Maneuvers with Reinforcement Learning Authors: Atahan Cilan , Mahir Demir , Özgün Can Yürütken , Seyyid Osman Sevgili , Ümit Can Bekar
Organizations: Turkish Aerospace Istanbul, Turkey
Abstract This paper evaluates an advanced jet trainer's utilization of artificial intelligence (AI)-based aircraft aerobatic maneuvers with the intention of developing an AI-assisted pilot training module for specific aircraft maneuvers. A multitude of aircraft maneuvers have been simulated using reinforcement learning (RL) agents, which will serve as a training tool for future pilots.
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Apr 27, 2026 · cs.LG J/K move · Enter open · S save
Mahir Demir, Atahan Cilan, Seyyid Osman Sevgili, Özgün Can Yürütken +1
Turkish Aerospace Istanbul, Turkey
This article explores the progress made in the creation of a pilot activated recovery system (PARS) for advanced jet trainers that utilizes artificial intelligence (AI) in an effort to enhance operational efficiency. The PARS model employs an advanced reinforcement learning (RL) architecture, incorporating a cutting-edge soft-actor critic (SAC) model and hyper-parameter optimization methods. Negative-g punishments and other handcrafted features remarked upon by control engineers and domain experts regarding PARS are also taken into account by the system. When evaluated by them, the AI model's behavior is deemed more desirable than that of conventional control methods.