Automated Aircraft Recovery via Reinforcement Learning: Initial Experiments
Abstract
Initial experiments described here were directed toward using reinforce(cid:173) ment learning (RL) to develop an automated recovery system (ARS) for high-agility aircraft. An ARS is an outer-loop flight-control system de(cid:173) signed to bring an aircraft from a range of out-of-control states to straight(cid:173) and-level flight in minimum time while satisfying physical and phys(cid:173) iological 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-Ievel flight in unconstrained cases while avoiding ground-strike. The RL system was also able to adhere to a pilot-station acceleration constraint while execut(cid:173) ing simulated recoveries.
Cite
Text
Monaco et al. "Automated Aircraft Recovery via Reinforcement Learning: Initial Experiments." Neural Information Processing Systems, 1997.Markdown
[Monaco et al. "Automated Aircraft Recovery via Reinforcement Learning: Initial Experiments." Neural Information Processing Systems, 1997.](https://mlanthology.org/neurips/1997/monaco1997neurips-automated/)BibTeX
@inproceedings{monaco1997neurips-automated,
title = {{Automated Aircraft Recovery via Reinforcement Learning: Initial Experiments}},
author = {Monaco, Jeffrey F. and Ward, David G. and Barto, Andrew G.},
booktitle = {Neural Information Processing Systems},
year = {1997},
pages = {1022-1028},
url = {https://mlanthology.org/neurips/1997/monaco1997neurips-automated/}
}