Automated Aircraft Recovery via Reinforcement Learning: Initial Experiments
Initial experiments described here were directed toward using reinforcement learning (RL) to develop an automated recovery system (ARS) for high-agility aircraft. An ARS is an outer-loop flight-control system designed to bring an aircraft from a range of out-of-control states to straight-and-level flight in minimum time while satisfying physical and physiological constraints. Here we report on results for a simple version of the problem involving only single-axis (pitch) simulated recoveries. Through simulated control experience using a medium-fidelity aircraft simulation, the RL system approximates an optimal policy for pitch-stick inputs to produce minimum-time transitions to straight-and-level flight in unconstrained cases while avoiding ground-strike. The RL system was also able to adhere to a pilot-station acceleration constraint while executing simulated recoveries.