Learning to Fly: An Application of Hierarchical Reinforcement Learning
Abstract
Hierarchical reinforcement learning promises to be the key to scaling reinforcement learning methods to large, complex, real-world problems. Many theoretical models have been proposed but so far there has been little in the way of empirical work published to demonstrate these claims. In this paper we begin to fill this void by by demonstrating the application of the RL-TOPs hierarchical reinforcement learning system to the problem of learning to control an aircraft in a ight simulator. We explain the steps needed to encode the background knowledge for this domain and present experimental data to show the success of this technique.
Cite
Text
Ryan and Reid. "Learning to Fly: An Application of Hierarchical Reinforcement Learning." International Conference on Machine Learning, 2000.Markdown
[Ryan and Reid. "Learning to Fly: An Application of Hierarchical Reinforcement Learning." International Conference on Machine Learning, 2000.](https://mlanthology.org/icml/2000/ryan2000icml-learning/)BibTeX
@inproceedings{ryan2000icml-learning,
title = {{Learning to Fly: An Application of Hierarchical Reinforcement Learning}},
author = {Ryan, Malcolm and Reid, Mark D.},
booktitle = {International Conference on Machine Learning},
year = {2000},
pages = {807-814},
url = {https://mlanthology.org/icml/2000/ryan2000icml-learning/}
}