End-to-End Hierarchical Reinforcement Learning for Adaptive Flight Control: A method for model-independent control through Proximal Policy Optimization with learned Options

Aircraft with disruptive designs have no high-fidelity and accurate flight models. At the same time, developing models for stochastic phenomena for traditional aircraft configurations are costly, and classical control methods cannot operate beyond the predefined operation points or adapt to unexpected changes to the aircraft. The Proximal Policy...