Discovering Hierarchy in Reinforcement Learning with HEXQ
Abstract
An open problem in reinforcement learning is discovering hierarchical structure. HEXQ, an algorithm which automatically attempts to decompose and solve a model-free factored MDP hierarchically is described. By searching for aliased Markov sub-space regions based on the state variables the algorithm uses temporal and state abstraction to construct a hierarchy of interlinked smaller MDPs. 1.
Cite
Text
Hengst. "Discovering Hierarchy in Reinforcement Learning with HEXQ." International Conference on Machine Learning, 2002.Markdown
[Hengst. "Discovering Hierarchy in Reinforcement Learning with HEXQ." International Conference on Machine Learning, 2002.](https://mlanthology.org/icml/2002/hengst2002icml-discovering/)BibTeX
@inproceedings{hengst2002icml-discovering,
title = {{Discovering Hierarchy in Reinforcement Learning with HEXQ}},
author = {Hengst, Bernhard},
booktitle = {International Conference on Machine Learning},
year = {2002},
pages = {243-250},
url = {https://mlanthology.org/icml/2002/hengst2002icml-discovering/}
}